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Kernels",479,81,"C++","2025-10-31","2026-10-15",0,[],"HazyResearch"],["ermongroup/CSDI","Codes for \"CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation\"",466,113,"Jupyter Notebook","2021-07-07","2024-03-14",0,[],"ermongroup"],["StanfordSNR/guardian-agent","[beta] Guardian Agent: secure ssh-agent forwarding for Mosh and SSH",461,31,"Go","2017-09-28","2023-07-20",0,[],"StanfordSNR"],["HazyResearch/pdftotree",":evergreen_tree: A tool for converting PDF into hOCR with text, tables, and figures being recognized and preserved.",460,94,"Python","2017-12-20","2023-08-03",0,[],"HazyResearch"],["snap-stanford/med-flamingo","",458,38,"Python","2023-02-25","2023-08-23",0,[],"snap-stanford"],["stanford-cs336/spring2024-lectures","",442,104,"Python","2024-04-01","2024-12-26",0,[],"stanford-cs336"],["HazyResearch/manifest","Prompt programming with FMs.",439,45,"Python","2022-05-21","2024-07-22",0,[],"HazyResearch"],["stanford-futuredata/noscope","Accelerating 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data",411,75,"Python","2018-02-02","2021-06-23",0,["knowledge-base-construction","machine-learning","multimodality"],"HazyResearch"],["stanfordmlgroup/chexpert-labeler","CheXpert NLP tool to extract observations from radiology reports.",411,85,"Python","2018-12-10","2023-02-03",0,[],"stanfordmlgroup"],["sisl/tufte_algorithms_book","A template for textbooks in the same style as Algorithms for Optimization",409,74,"TeX","2019-06-21","2026-03-08",0,[],"sisl"],["TreeHacks/botnet-hackpack","Build a basic Command & Control botnet in C",408,94,"C","2017-01-27","2020-10-27",0,[],"TreeHacks"],["GradientSpaces/LoopSplat","[3DV 2025, Oral] LoopSplat: Loop Closure by Registering 3D Gaussian Splats",402,31,"Python","2024-08-12","2025-02-03",0,["3dgs","3dgs-slam","3dvision","dense-mapping"],"GradientSpaces"],["StanfordASL/frax","Fast Robot Kinematics and Dynamics in JAX",401,29,"Python","2026-04-05","2026-10-26",0,[],"StanfordASL"],["stanford-star/relbench","RelBench: Relational Deep Learning Benchmark",397,89,"Python","2023-10-29","2026-10-19",0,["benchmark","relational-databases","relational-foundation-models","relational-learning"],"stanford-star"],["HazyResearch/hyperbolics","Hyperbolic Embeddings",396,63,"Python","2018-01-24","2023-07-06",0,[],"HazyResearch"],["Gen-Verse/ScienceBuddy","ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents",394,53,"Python","2026-09-14","2026-09-17",0,["agent-harness","aiforscience","autoresearch","llm-agents"],"Gen-Verse"],["habitlab/habitlab","Build better habits online! Tell HabitLab your goals, and it will determine the appropriate interventions via experimentation.",378,56,"JavaScript","2016-06-13","2023-04-10",0,["chrome-extension","javascript","livescript"],"habitlab"],["cvxgrp/cvx_short_course","Materials for a short course on convex optimization.",373,120,"Jupyter Notebook","2015-07-07","2026-07-14",0,[],"cvxgrp"],["Gen-Verse/JEPA-Anything","JEPA-Anything: Learning Predictive Models across Different Worlds",373,26,"Python","2026-09-04","2026-09-18",0,["ai4science","biomedical","jepa","world-models"],"Gen-Verse"],["snap-stanford/neural-subgraph-learning-GNN","",366,73,"Jupyter Notebook","2020-07-01","2024-06-25",0,[],"snap-stanford"],["snap-stanford/MLAgentBench","",358,75,"Python","2023-08-30","2024-06-19",0,[],"snap-stanford"],["stanford-cs336/assignment2-systems","Student version of Assignment 2 for Stanford CS336 - Language Modeling From Scratch",357,807,"Python","2025-04-17","2026-05-01",0,[],"stanford-cs336"],["HazyResearch/flash-fft-conv","FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores",355,37,"C++","2023-11-02","2024-12-28",0,[],"HazyResearch"],["stanfordnqp/spins-b","Photonic optimization library",355,123,"Python","2019-03-25","2024-04-13",0,[],"stanfordnqp"],["SALT-NLP/MixText","MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification",354,60,"Jupyter Notebook","2020-04-22","2020-06-05",0,["computation","dataaugmentation","interpolation","machine-learning"],"SALT-NLP"],["snap-stanford/UCE","UCE is a zero-shot foundation model for single-cell gene expression data",351,50,"Python","2023-09-27","2026-07-08",0,[],"snap-stanford"],["HazyResearch/cartridges","Storing long contexts in tiny caches with self-study",344,45,"Python","2025-06-01","2026-03-23",0,[],"HazyResearch"],["stanfordmlgroup/MedAgentBench","MedAgentBench: A Realistic Virtual EHR Environment to Benchmark Medical LLM Agents",340,72,"Python","2025-01-22","2025-11-21",0,["agent","benchmark","medical"],"stanfordmlgroup"],["snap-stanford/stark","(NeurIPS D&B 2024) STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases",337,54,"Python","2024-04-22","2026-02-06",0,["graph","information-retrieval","knowledge-base","llm"],"snap-stanford"],["startup-class/bitstarter-leaderboard","A more sophisticated Bitcoin-powered crowdfunder.",337,350,"JavaScript","2013-09-07","2013-09-07",0,[],"startup-class"],["ermongroup/ncsnv2","The official PyTorch implementation for NCSNv2 (NeurIPS 2020)",330,59,"Python","2020-06-17","2021-06-12",0,["diffusion-models","generative-models","neurips-2020","score-based-generative-modeling"],"ermongroup"],["StanfordVL/3DSceneGraph","The data skeleton from \"3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera\" http://3dscenegraph.stanford.edu",329,36,"Python","2019-09-20","2024-06-28",0,[],"StanfordVL"],["drorlab/gvp-pytorch","Geometric Vector Perceptrons --- a rotation-equivariant GNN for learning from biomolecular structure",328,57,"Python","2021-04-29","2025-11-15",0,[],"drorlab"],["StanfordPL/stoke-release","",328,19,"","2014-06-04","2016-02-08",0,[],"StanfordPL"],["snap-stanford/cs224w-notes","CS224W Course Notes",326,76,"CSS","2019-09-26","2021-02-22",0,[],"snap-stanford"],["drorlab/atom3d","ATOM3D: tasks on molecules in three dimensions",319,35,"Python","2020-04-03","2023-03-02",0,[],"drorlab"],["datademofun/heroku-basic-flask","Example of deploying a Python Flask app onto Heroku",316,246,"Python","2016-05-09","2019-06-27",0,[],"datademofun"],["HazyResearch/flyingsquid","More interactive weak supervision with FlyingSquid",315,23,"Python","2020-02-10","2020-09-01",0,[],"HazyResearch"],["snap-stanford/KGReasoning","Multi-Hop Logical Reasoning in Knowledge Graphs",312,63,"Python","2020-10-22","2022-03-27",0,["embedding","knowledge-base","knowledge-graph","reasoning"],"snap-stanford"],["snap-stanford/snap-python","SNAP Python code, SWIG related files",312,106,"C++","2013-01-17","2022-06-21",0,[],"snap-stanford"],["ermongroup/Wifi_Activity_Recognition","Code for IEEE Communication Magazine (A Survey on Behaviour Recognition Using WiFi Channle State Information)",308,82,"Jupyter Notebook","2017-07-20","2023-06-08",0,[],"ermongroup"],["stanford-oval/suql","SUQL: Conversational Search over Structured and Unstructured Data with LLMs",308,40,"Python","2023-11-16","2026-08-11",0,[],"stanford-oval"],["snap-stanford/POPPER","Automated Hypothesis Testing with Agentic Sequential Falsifications",301,32,"Python","2025-01-28","2025-05-14",0,["agent","ai-for-science","hypothesis-testing","llm"],"snap-stanford"],["stanford-oval/genie-cloud","Genie As A Service and Thingpedia",301,47,"JavaScript","2016-02-11","2023-03-05",0,["cloud-native","hacktoberfest","voice-assistant"],"stanford-oval"],["computational-imaging/ACORN","ACORN: Adaptive Coordinate Networks for Neural Scene Representation | SIGGRAPH 2021",300,32,"Python","2021-04-16","2022-04-11",0,[],"computational-imaging"],["stanford-cs221/lectures","Executable lectures for Autumn 2025",300,74,"Python","2025-09-21","2026-10-23",0,[],"stanford-cs221"],["startup-class/setup","AWS EC2 setup files for Startup Engineering MOOC.",294,1224,"Shell","2013-07-01","2017-04-25",0,[],"startup-class"],["stanford-ppl/spatial","Spatial: \"Specify Parameterized Accelerators Through Inordinately Abstract Language\"",292,32,"Scala","2018-03-23","2024-06-02",0,["fpgas"],"stanford-ppl"],["stanford-futuredata/FrugalGPT","FrugalGPT: better quality and lower cost for LLM applications",291,38,"Jupyter Notebook","2023-06-23","2025-02-10",0,[],"stanford-futuredata"],["HazyResearch/zoology","Understand and test language model architectures on synthetic tasks.",290,59,"Python","2023-10-17","2026-03-22",0,[],"HazyResearch"],["StanfordSpezi/SpeziLLM","Large Language Model (LLM) module for the Spezi Ecosystem",290,47,"Swift","2023-05-21","2026-09-27",0,["chatbot","gpt","ios","large-language-models"],"StanfordSpezi"],["StanfordSNR/pantheon","Pantheon of Congestion Control",285,149,"Python","2016-04-01","2024-07-13",0,["benchmark","congestion-control","imitation-learning"],"StanfordSNR"],["fancompute/neuroptica","Flexible simulation package for optical neural networks",279,51,"Python","2018-10-12","2020-04-22",0,["machine-learning","nanophotonics","neural-network","optics"],"fancompute"],["stanford-crfm/ecosystem-graphs","",275,35,"JavaScript","2022-03-10","2025-01-24",0,[],"stanford-crfm"],["stanford-oval/genie-server","The home server version of Almond",274,43,"JavaScript","2016-02-11","2024-04-14",0,["hacktoberfest","nlp","raspberrypi","voice"],"stanford-oval"],["SALT-NLP/LLaVAR","Code/Data for the paper: \"LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding\"",269,14,"Python","2023-06-27","2024-06-12",0,["chatbot","chatgpt","gpt-4","instruction-tuning"],"SALT-NLP"],["Stanford-NavLab/gnss_lib_py","Modular Python tool for parsing, analyzing, and visualizing Global Navigation Satellite Systems (GNSS) data and state estimates",269,61,"Python","2021-06-29","2026-09-15",0,["android","ekf","gnss","navigation"],"Stanford-NavLab"],["zou-group/CellVoyager","",266,46,"Python","2025-06-02","2026-06-02",0,[],"zou-group"],["kundajelab/dragonn","A toolkit to learn how to model and interpret regulatory sequence data using deep learning.",264,66,"Jupyter Notebook","2016-06-02","2023-08-01",0,["deep-learning","genomics"],"kundajelab"],["HazyResearch/lolcats","Repo for \"LoLCATs: On Low-Rank Linearizing of Large Language Models\"",262,25,"Python","2024-08-13","2025-01-31",0,[],"HazyResearch"],["stanford-futuredata/dawn-bench-entries","DAWNBench: An End-to-End Deep Learning Benchmark and Competition",262,71,"Python","2017-11-29","2026-04-13",0,["cifar10","deeplearning","imagenet","inference"],"stanford-futuredata"],["kundajelab/chrombpnet","Bias factorized, base-resolution deep learning models of chromatin accessibility (chromBPNet)",260,68,"Jupyter Notebook","2021-07-23","2026-06-09",0,[],"kundajelab"],["StanfordSpezi/Spezi","Open-source framework for rapid development of modern, interoperable digital health applications.",260,27,"Swift","2022-10-10","2026-10-07",0,["digital-health","digitalhealth","fhir","spezi"],"StanfordSpezi"],["HazyResearch/based","Code for exploring Based models from \"Simple linear attention language models balance the recall-throughput tradeoff\"",258,20,"Python","2024-02-07","2025-06-06",0,[],"HazyResearch"],["StanfordVL/ReferringRelationships","",258,79,"Python","2017-07-19","2022-11-22",0,[],"StanfordVL"],["HazyResearch/KGEmb","Hyperbolic Knowledge Graph embeddings.",257,52,"Python","2020-04-28","2021-06-17",0,[],"HazyResearch"],["stanford-cs336/assignment5-alignment","",251,511,"Python","2025-05-24","2026-06-04",0,[],"stanford-cs336"],["StanfordHCI/datavore","A small, fast, in-browser database engine written in JavaScript.",251,45,"JavaScript","2012-03-13","2021-10-30",0,[],"StanfordHCI"],["Gen-Verse/Recuris","Recursive Experiential–Working Memory Evolution for Long-Horizon Agent Harnesses",248,30,"Python","2026-08-25","2026-08-30",0,["agent-harnesses","agentmemory","claude-skills","llm-agents"],"Gen-Verse"],["guestrin-lab/deepscholar","build and benchmark deep research",246,32,"Python","2025-08-28","2026-03-28",0,["ai-evaluation","benchmark-suite","dataset-generation","deep-research"],"guestrin-lab"],["snap-stanford/GreaseLM","[ICLR 2022 spotlight]GreaseLM: Graph REASoning Enhanced Language Models for Question Answering",243,42,"Python","2021-11-09","2025-04-23",0,["biomedical-ques","commonsense-reasoning","graph-neural-networks","knowledge-graph"],"snap-stanford"],["zou-group/avatar","(NeurIPS 2024) AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning",242,28,"Python","2024-06-14","2025-06-10",0,["agents","knowledge-base","llms","retrieval"],"zou-group"],["ProteinDesignLab/protpardelle","Diffusion-based all-atom protein generative model.",239,22,"Python","2023-09-08","2025-08-27",0,[],"ProteinDesignLab"],["Stanford-AIMI/CheXagent","[Arxiv-2024] CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation",237,28,"Python","2023-12-14","2025-01-07",0,[],"Stanford-AIMI"],["stanford-cs336/spring2025-lectures","",236,62,"Python","2026-03-31","2026-03-31",0,[],"stanford-cs336"],["stanford-mast/nn_dataflow","Explore the energy-efficient dataflow scheduling for neural networks.",236,87,"Python","2017-01-19","2026-04-13",0,["dataflow","neural-network","optimizer","scheduling"],"stanford-mast"],["sisl/NeuralVerification.jl","Methods to soundly verify deep neural networks",234,50,"Julia","2018-08-09","2025-05-01",0,[],"sisl"],["som-shahlab/ehrshot-benchmark","A benchmark for few-shot evaluation of foundation models for electronic health records (EHRs)",234,29,"Jupyter Notebook","2023-06-15","2025-06-06",0,[],"som-shahlab"],["Stanford-TML/homebody","",234,10,"Python","2026-09-26","2026-10-17",0,[],"Stanford-TML"],["StanfordASL/oscbf","Safe, high-performance, and task-consistent manipulator control",231,24,"Python","2025-02-18","2026-02-17",0,[],"StanfordASL"],["sisl/BayesNets.jl","Bayesian Networks for Julia",229,51,"Julia","2014-08-21","2026-05-26",0,[],"sisl"],["computational-imaging/GSM","Gaussian Shell Maps for Efficient 3D Human Generation (CVPR 2024)",228,11,"Jupyter Notebook","2023-11-29","2024-01-03",0,[],"computational-imaging"],["StanfordASL/cbfpy","Control Barrier Functions in Python and JAX",228,21,"Python","2024-12-07","2026-09-29",0,[],"StanfordASL"],["HazyResearch/fly","",224,24,"Python","2021-10-26","2023-02-21",0,[],"HazyResearch"],["computational-imaging/neural-holography","Code and data for Neural Holography",223,60,"Python","2020-10-12","2023-12-02",0,["camera-in-the-loop","double-phase","gerchberg-saxton","gradient-descent"],"computational-imaging"],["ermongroup/MA-AIRL","Multi-Agent Adversarial Inverse Reinforcement Learning, ICML 2019.",222,27,"Python","2019-05-13","2019-06-19",0,[],"ermongroup"],["cvxgrp/CVXR","An R modeling language for convex optimization problems.",219,32,"R","2015-10-10","2026-10-19",0,[],"cvxgrp"],["HazyResearch/bootleg","Self-Supervision for Named Entity Disambiguation at the Tail",218,27,"Python","2020-08-10","2022-06-14",0,["ai","machine-learning","named-entity-disambiguation","self-supervision"],"HazyResearch"],["SALT-NLP/DyLAN","Official Implementation of Dynamic LLM-Agent Network: An LLM-agent Collaboration Framework with Agent Team Optimization",218,29,"Python","2023-10-03","2024-05-16",0,["chatgpt","gpt4","llm","llm-agent"],"SALT-NLP"],["stanford-ppl/Delite","The Delite Git Repo",218,44,"Scala","2010-09-03","2017-02-28",0,[],"stanford-ppl"],["stanfordnlp/axbench","Stanford NLP Python library for benchmarking the utility of LLM interpretability methods",218,44,"Python","2024-08-07","2026-03-12",0,["interpretability","intervention","large-language-models","llm-steering"],"stanfordnlp"],["startup-class/dotfiles","Development configuration files for Startup Engineering MOOC. Intended to be forked and customized by students.",216,1008,"","2013-07-01","2013-07-02",0,[],"startup-class"],["stanfordnlp/phrasal","A large-scale statistical machine translation system written in Java.",213,88,"Java","2015-02-16","2021-12-12",0,["java","java-nlp","natural-language-processing","statistical-machine-translation"],"stanfordnlp"],["ermongroup/Variational-Ladder-Autoencoder","Implementation of VLAE",212,47,"Python","2017-02-27","2017-12-21",0,["feature-extraction","generative-model","representation-learning","unsupervised-learning"],"ermongroup"],["HazyResearch/HypHC","Hyperbolic Hierarchical Clustering.",210,31,"Python","2020-09-28","2023-10-05",0,[],"HazyResearch"],["stanfordnlp/spinn","SPINN (Stack-augmented Parser-Interpreter Neural Network): fast, batchable, context-aware TreeRNNs",208,82,"Python","2015-10-14","2018-03-15",0,[],"stanfordnlp"],["SU-SWS/open_framework","Open Framework Drupal Theme",208,82,"CSS","2012-03-07","2019-10-16",0,["d7","theme"],"SU-SWS"],["HazyResearch/hippo-code","",206,36,"Python","2020-08-20","2024-05-27",0,[],"HazyResearch"],["GradientSpaces/Rectified-Point-Flow","[NeurIPS 2025, Spotlight] Rectified Point Flow: Generic Point Cloud Pose Estimation",205,17,"Python","2025-06-02","2025-12-02",0,["3d-deep-learning","3d-vision","flow-matching","point-cloud"],"GradientSpaces"],["stanford-esrg/lzr","LZR quickly detects and fingerprints unexpected services running on unexpected ports.",203,45,"Go","2020-02-24","2026-01-06",0,["go","golang","internet-wide-scanning","ipv4"],"stanford-esrg"],["stanford-oval/genie-toolkit","The Genie open source kit for voice assistant (formerly known as Almond)",202,35,"TypeScript","2019-02-04","2023-06-07",0,["hacktoberfest","natural-language","nlp","semantic-parsers"],"stanford-oval"],["HazyResearch/TART","TART: A plug-and-play Transformer module for task-agnostic reasoning",201,15,"Python","2023-06-12","2023-06-22",0,[],"HazyResearch"],["stanford-cs149/asst1","Stanford CS149 -- Assignment 1",200,249,"C++","2019-09-26","2025-10-15",0,[],"stanford-cs149"],["StanfordMIMI/MedVAE","[MIDL 2025] Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders",197,21,"Python","2023-06-13","2026-07-10",0,[],"StanfordMIMI"],["kundajelab/tfmodisco","TF MOtif Discovery from Importance SCOres",196,42,"Python","2016-07-01","2026-10-16",0,[],"kundajelab"],["stanford-esrg/retina","Retina is a network analysis framework  that supports 100+ Gbps traffic analysis on a single server with no specialized hardware.",196,31,"Rust","2022-06-10","2026-05-05",0,["analysis","dpdk","network","rust"],"stanford-esrg"],["stanford-futuredata/ASAP","ASAP: Prioritizing Attention via Time Series Smoothing",196,30,"Jupyter Notebook","2017-03-02","2018-04-05",0,["smoothing","time-series","visualization"],"stanford-futuredata"],["cvxgrp/proximal","Sample implementations of proximal operators",195,81,"Matlab","2013-05-07","2013-06-14",0,[],"cvxgrp"],["fancompute/angler","Frequency-domain photonic simulation and inverse design optimization for linear and nonlinear devices",195,54,"Python","2018-07-30","2019-12-14",0,["adjoint","adjoint-sensitivities","electromagnetic","fdfd"],"fancompute"],["StanfordASL/hj_reachability","Hamilton-Jacobi reachability analysis in JAX.",195,33,"Python","2021-04-26","2024-11-08",0,[],"StanfordASL"],["StanfordMIMI/DDM2","[ICLR2023] Official repository of DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models",192,25,"Python","2023-02-04","2024-01-21",0,["denoising","diffusion-models","diffusion-mri","iclr"],"StanfordMIMI"],["computational-imaging/automatic-integration","Official repo for AutoInt: Automatic Integration for Fast Neural Volume Rendering in CVPR 2021",188,20,"Python","2021-03-26","2022-11-25",0,[],"computational-imaging"],["kundajelab/bpnet","Toolkit to train base-resolution deep neural networks on functional genomics data and to interpret them",187,38,"Python","2019-07-26","2026-03-19",0,[],"kundajelab"],["snap-stanford/distance-encoding","Distance Encoding for GNN Design",186,26,"Jupyter Notebook","2020-06-08","2021-03-20",0,[],"snap-stanford"],["StanfordASL/neural-network-lyapunov","Synthesizing neural-network Lyapunov functions (and controllers) as stability certificate.",185,38,"Python","2019-09-23","2025-06-02",0,["lyapunov-functions","neural-network","robotics","verification"],"StanfordASL"],["sisl/ngsim_env","Learning human driver models from NGSIM data with imitation learning.",183,80,"Jupyter Notebook","2018-02-27","2020-02-10",0,["autonomous-vehicles","deeplearning","imitation-learning","reinforcement-learning"],"sisl"],["som-shahlab/femr","FEMR (Framework for Electronic Medical Records) provides tooling for large-scale, self-supervised learning using electronic health records",183,37,"Python","2022-06-28","2026-10-19",0,["electronic-health-records","foundation-models","healthcare","self-supervised-learning"],"som-shahlab"],["HazyResearch/butterfly","Butterfly matrix multiplication in PyTorch",182,36,"Python","2018-11-12","2023-10-05",0,[],"HazyResearch"],["computational-imaging/bacon","Official respository for \"Band-limited Coordinate Networks for Multiscale Scene Representation\" | CVPR 2022",180,20,"Python","2022-01-01","2022-11-05",0,[],"computational-imaging"],["HazyResearch/spacetime","Code for SpaceTime 🌌⏱️. Proposed in Effectively Modeling Time Series with Simple Discrete State Spaces, ICLR 2023.",180,26,"Python","2023-03-02","2023-03-17",0,[],"HazyResearch"],["cs106l/cs106l-assignments","Course assignments for CS106L",178,186,"Python","2024-09-30","2026-10-24",0,[],"cs106l"],["ISET/isetcam","Open source version of the original ISET, a complement to ISETBIO",177,60,"HTML","2018-02-18","2026-09-25",0,[],"ISET"],["snap-stanford/SATURN","",176,30,"Jupyter Notebook","2022-11-01","2024-07-03",0,[],"snap-stanford"],["Stanford-TML/robot_keyframe_kit","Create motion for any robot through editing keyframe",175,15,"Python","2025-11-21","2026-03-27",0,["keyframe-animation","locomotion","robotics","viser"],"Stanford-TML"],["stanfordnlp/coqa-baselines","The baselines used in the CoQA paper",175,49,"Python","2018-09-15","2020-04-22",0,[],"stanfordnlp"],["snap-stanford/graphwave","",174,53,"Jupyter Notebook","2017-11-03","2018-12-03",0,[],"snap-stanford"],["StanfordASL/AA203-Notes","Course notes for AA203",173,43,"TeX","2019-04-01","2026-04-12",0,[],"StanfordASL"],["HazyResearch/tanda","Learning to Compose Domain-Specific Transformations for Data Augmentation",171,28,"Python","2017-07-21","2022-11-21",0,[],"HazyResearch"],["Gen-Verse/CURE","[NeurIPS 2025 Spotlight] Co-Evolving LLM Coder and Unit Tester via Reinforcement Learning",169,15,"Python","2025-06-04","2025-09-19",0,["code-generation","llm-coder","reinforcement-learning-algorithms","unit-testing"],"Gen-Verse"],["stanford-iprl-lab/torchfilter","Bayesian filters in PyTorch",167,24,"Python","2020-04-03","2023-05-25",0,[],"stanford-iprl-lab"],["kundajelab/atac_dnase_pipelines","ATAC-seq and DNase-seq processing pipeline",166,78,"Python","2016-02-28","2022-06-21",0,["atac-seq","bioinformatics-pipeline","dnase-seq"],"kundajelab"],["drorlab/gvp","Geometric Vector Perceptron --- a rotation-equivariant GNN for learning from biomolecular structure",164,43,"Python","2020-11-13","2021-05-01",0,[],"drorlab"],["stanfordnlp/cocoa","Framework for learning dialogue agents in a two-player game setting.",164,61,"Python","2016-08-08","2022-10-04",0,[],"stanfordnlp"],["SALT-NLP/collaborative-gym","Framework and toolkits for building and evaluating collaborative agents that can work together with humans.",163,22,"Python","2024-12-18","2026-08-20",0,["agent","human-agent-collaboration","human-agent-interaction","large-language-models"],"SALT-NLP"],["stanfordmlgroup/CheXbert","Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT",163,33,"Python","2020-09-25","2025-08-01",0,["bert","deep-learning","medical-imaging","natural-language-processing"],"stanfordmlgroup"],["GoldenGait/FARM-Project","",162,18,"Python","2026-07-24","2026-08-14",0,[],"GoldenGait"],["Stanford-ILIAD/PantheonRL","PantheonRL is a package for training and testing multi-agent reinforcement learning environments. PantheonRL supports cross-play, fine-tuning, ad-hoc coordinati",162,26,"Python","2021-06-26","2023-11-06",0,["deep-reinforcement-learning","multiagent-reinforcement-learning","reinforcement-learning"],"Stanford-ILIAD"],["stanford-futuredata/index-baselines","Simple baselines for \"Learned Indexes\"",160,27,"HTML","2017-12-20","2018-01-13",0,[],"stanford-futuredata"],["StanfordMIMI/clin-summ","Clinical text summarization by adapting large language models",160,33,"Python","2023-09-13","2024-07-31",0,[],"StanfordMIMI"],["StanfordVL/JRMOT_ROS","Source code for JRMOT: A Real-Time 3D Multi-Object Tracker and a New Large-Scale Dataset",160,34,"Python","2020-03-28","2023-02-15",0,[],"StanfordVL"],["StanfordBDHG/LLMonFHIR","A Demonstration using LLMs to Explain Health Records",158,24,"Swift","2023-05-21","2026-05-07",0,[],"StanfordBDHG"],["ermongroup/TEOChat","Official code for TEOChat, the first vision-language assistant for temporal earth observation data (ICLR 2025).",157,6,"Python","2024-10-04","2025-12-01",0,[],"ermongroup"],["stanford-futuredata/FAST","End-to-end earthquake detection pipeline via efficient time series similarity search",157,55,"Jupyter Notebook","2017-01-19","2023-07-06",0,["earthquakes","minhash-lsh-algorithm","similarity-search","time-series"],"stanford-futuredata"],["stanfordmlgroup/nlc","Neural Language Correction implemented on Tensorflow",157,66,"Python","2016-04-15","2022-10-15",0,[],"stanfordmlgroup"],["vistalab/vistasoft","VISTASOFT is the main software repository of the VISTA lab at Stanford University.",157,145,"MATLAB","2013-01-17","2026-10-05",0,[],"vistalab"],["computational-imaging/opticalCNN","hybrid optical electronic convolutional neural networks",154,35,"Jupyter Notebook","2018-08-07","2019-11-11",0,[],"computational-imaging"],["fancompute/legume","🌱 Guided-mode expansion of photonic crystal slabs",153,43,"Python","2019-06-12","2026-03-26",0,["autograd","eigenmodes","optics","photonics"],"fancompute"],["drorlab/pensa","PENSA - a collection of python methods for exploratory analysis and comparison of biomolecular conformational ensembles.",152,25,"Python","2020-05-22","2025-02-12",0,["biomolecules","biophysics","molecular-dynamics","structural-biology"],"drorlab"],["ermongroup/sliced_score_matching","Code for reproducing results in the sliced score matching paper (UAI 2019)",152,22,"Python","2019-06-25","2020-01-07",0,["density-estimation","energy-based-model","score-estimation","score-matching"],"ermongroup"],["ermongroup/neuralsort","Code for \"Stochastic Optimization of Sorting Networks using Continuous Relaxations\", ICLR 2019.",151,26,"Python","2019-02-25","2023-03-24",0,[],"ermongroup"],["stanford-centaur/PyPantograph","A Machine-to-Machine Interaction System for Lean 4.",150,39,"Python","2024-04-11","2026-10-20",0,["automated-reasoning","automated-theorem-proving","lean4","machine-learning"],"stanford-centaur"],["crowdresearch/daemo","The Daemo crowdsourcing platform",147,227,"Python","2015-04-15","2022-06-21",0,["angular","crowdsourcing","material-design","postgres"],"crowdresearch"],["HazyResearch/babble","A system for generating training labels via natural language explanations",147,18,"Python","2018-05-04","2019-06-24",0,[],"HazyResearch"],["snap-stanford/covid-mobility","",147,53,"Jupyter Notebook","2020-06-19","2020-11-15",0,[],"snap-stanford"],["stanfordmlgroup/ManyICL","",147,14,"Python","2024-05-05","2024-05-23",0,[],"stanfordmlgroup"],["sisl/aa228-notebook","IJulia notebooks for AA228/CS238 Decision Making Under Uncertainty course at Stanford University",146,67,"Jupyter Notebook","2014-08-26","2023-02-22",0,[],"sisl"],["snap-stanford/roland","",146,28,"Jupyter Notebook","2022-06-08","2023-11-05",0,[],"snap-stanford"],["HazyResearch/EmptyHeaded","Your worst case is our best case.",145,31,"C++","2015-10-01","2017-03-15",0,[],"HazyResearch"],["StanfordASL/Trajectron","Code accompanying \"The Trajectron: Probabilistic Multi-Agent Trajectory Modeling with Dynamic Spatiotemporal Graphs\" by Boris Ivanovic and Marco Pavone.",144,40,"Jupyter Notebook","2018-09-15","2020-12-09",0,["deep-learning","human-robot-interaction","human-trajectory-prediction"],"StanfordASL"],["HazyResearch/domino","",143,25,"Python","2021-11-29","2023-10-30",0,[],"HazyResearch"],["stanford-centaur/smt-switch","A generic C++ API for SMT solving. It provides abstract classes which can be implemented by different SMT solvers.",143,56,"C++","2019-04-10","2026-10-26",0,[],"stanford-centaur"],["ccrma/chugins","Repository for ChuGins",142,49,"C++","2012-08-29","2026-05-31",0,[],"ccrma"],["poldracklab/pydeface","defacing utility for MRI images",142,47,"Python","2015-12-07","2026-10-19",0,[],"poldracklab"],["HazyResearch/blocking-tutorial","",141,18,"C++","2015-04-23","2023-05-30",0,[],"HazyResearch"],["StanfordSpezi/SpeziTemplateApplication","Stanford Spezi template for building apps on Apple Platforms",141,41,"Swift","2023-01-17","2026-07-15",0,["fhir","ios","spezi","stanford"],"StanfordSpezi"],["stanford-futuredata/gavel","Code for \"Heterogenity-Aware Cluster Scheduling Policies for Deep Learning Workloads\", which appeared at OSDI 2020",140,34,"Jupyter Notebook","2018-12-29","2024-07-25",0,[],"stanford-futuredata"],["snap-stanford/GIB","Graph Information Bottleneck (GIB) for learning minimal sufficient structural and feature information using GNNs",139,27,"Jupyter Notebook","2020-06-11","2022-11-29",0,["graph-neural-networks","information-bottleneck","node-classification","representation-learning"],"snap-stanford"],["SALT-NLP/GenUI","Code for the paper: Generative Interfaces for Language Models",138,19,"TypeScript","2025-07-26","2025-10-07",0,["generative-ai","genui","user-interface"],"SALT-NLP"],["Gen-Verse/DiscoveryRSI","DiscoveryRSI: Recursively Learning to Discover Valuable Hypotheses",137,11,"Python","2026-10-15","2026-10-20",0,["agent-framework","agentic-workflow","aiforscience","llm-agents"],"Gen-Verse"],["mabuchilab/Instrumental","Python-based instrumentation library from the Mabuchi Lab.",137,81,"Python","2013-10-21","2025-01-01",0,[],"mabuchilab"],["stanfordnlp/stanza-old","Stanford NLP group's shared Python tools.",137,34,"Python","2015-10-27","2018-03-14",0,["natural-language-processing","nlp","python","text-analysis"],"stanfordnlp"],["cvxgrp/dccp","A CVXPY extension for convex-concave programming",136,34,"Python","2016-04-08","2026-06-22",0,[],"cvxgrp"],["ProteinDesignLab/protein-design-tutorials","Tutorials, cheat sheets, and other resources for computational methods for protein design.",136,29,"Jupyter Notebook","2018-12-28","2023-08-15",0,[],"ProteinDesignLab"],["GradientSpaces/CrossOver","[CVPR 2025, Highlight] CrossOver: 3D Scene Cross-Modal Alignment",134,10,"Python","2025-02-19","2026-01-04",0,["3d-scene-understanding","multimodal-alignment"],"GradientSpaces"],["stanford-centaur/pono","Pono: A flexible and extensible SMT-based model checker",134,44,"C++","2019-09-06","2026-10-23",0,[],"stanford-centaur"],["stanfordnlp/chirpycardinal","Stanford's Alexa Prize socialbot",134,30,"Python","2020-10-08","2023-09-27",0,[],"stanfordnlp"],["stanfordnmbl/GaitDynamics","",134,17,"Python","2024-04-22","2026-05-18",0,[],"stanfordnmbl"],["SALT-NLP/demonstrated-feedback","",133,14,"Python","2024-05-28","2024-10-01",0,[],"SALT-NLP"],["snap-stanford/BioDiscoveryAgent","BioDiscoveryAgent is an LLM-based AI agent for closed-loop design of genetic perturbation experiments",132,26,"Python","2024-02-21","2025-07-06",0,[],"snap-stanford"],["StanfordMIMI/Comp2Comp","Computed tomography to body composition (Comp2Comp).",129,25,"Python","2020-05-03","2026-01-06",0,["abdomen-ct","abdominal","body-composition","computed-tomography"],"StanfordMIMI"],["jonfanlab/GLOnet","Global optimization based on generative neural networks",127,41,"Python","2019-05-23","2022-11-17",0,[],"jonfanlab"],["Stanford-NavLab/deep_gnss","Repository for GNSS-based position estimation using a Deep Neural Network",127,34,"Python","2021-09-20","2022-10-27",0,[],"Stanford-NavLab"],["ermongroup/a-nice-mc","Code for \"A-NICE-MC: Adversarial Training for MCMC\"",126,28,"Jupyter Notebook","2017-06-11","2018-07-20",0,["bayesian-inference","bayesian-machine-learning","generative-adversarial-network","generative-models"],"ermongroup"],["stanford-cs336/assignment3-scaling","",126,294,"Python","2025-04-30","2026-06-09",0,[],"stanford-cs336"],["stanford-mast/pocket","Elastic ephemeral storage",125,28,"Java","2018-06-27","2022-04-12",0,[],"stanford-mast"],["fancompute/workshop-invdesign","📐 Workshop material for optical inverse design and automatic differentiation",124,38,"Jupyter Notebook","2019-10-13","2020-03-20",0,["automatic-differentiation","optics","optimization","photonics"],"fancompute"],["snap-stanford/mars","Discovering novel cell types across heterogenous single-cell experiments",124,35,"Jupyter Notebook","2020-02-18","2022-12-08",0,[],"snap-stanford"],["stanfordnlp/stanfordnlp","[Deprecated] This library has been renamed to \"Stanza\". Latest development at: https://github.com/stanfordnlp/stanza",124,29,"Python","2020-03-16","2023-09-12",0,[],"stanfordnlp"],["StanfordVL/bddl-100","",124,14,"Python","2021-07-13","2025-06-23",1,[],"StanfordVL"],["tatsu-lab/opinions_qa","",124,17,"Jupyter Notebook","2023-03-25","2024-05-02",0,[],"tatsu-lab"],["cvxgrp/diffcp","Differentiation through cone programs",121,26,"Python","2019-04-18","2026-05-20",0,[],"cvxgrp"],["HazyResearch/eclair-agents","Automating enterprise workflows with multimodal agents",121,17,"Jupyter Notebook","2024-05-07","2024-10-09",0,[],"HazyResearch"],["sisl/MPOPIS","Adaptive importance sampling modification to MPPI",121,15,"Julia","2021-11-06","2024-04-01",0,["model-predictive-control","mpc","mppi","nonlinear-control"],"sisl"],["guestrin-lab/ACORN","state-of-the-art search over vector embeddings and structured data (SIGMOD '24)",120,31,"C++","2024-04-10","2025-03-06",0,[],"guestrin-lab"],["ProteinDesignLab/caliby","Potts model-based protein sequence design",120,17,"Python","2025-09-27","2026-07-01",0,[],"ProteinDesignLab"],["HazyResearch/mindbender","Tools for iterative knowledge base development with DeepDive",119,30,"CoffeeScript","2014-09-29","2018-11-09",0,[],"HazyResearch"],["StanfordAHA/garnet","Next generation CGRA generator",119,14,"Python","2018-07-12","2026-10-25",0,[],"StanfordAHA"],["stanfordnlp/wge","Workflow-Guided Exploration: sample-efficient RL agent for web tasks",118,36,"Python","2018-02-20","2023-06-05",0,[],"stanfordnlp"],["EduNLP/edu-convokit","Edu-ConvoKit: An Open-Source Framework for Education Conversation Data",117,16,"Jupyter Notebook","2023-12-27","2025-04-19",0,["data","data-analysis","data-science","education"],"EduNLP"],["StanfordHCI/termite","(development moved to new repos)",117,37,"Python","2013-02-02","2014-02-15",0,[],"StanfordHCI"],["sisl/gail-driver","",116,48,"Python","2017-01-09","2017-10-12",0,[],"sisl"],["cvxgrp/qcqp","A CVXPY extension for handling nonconvex QCQP via Suggest-and-Improve framework",115,27,"Python","2016-05-06","2020-05-04",0,[],"cvxgrp"],["ermongroup/GraphScoreMatching","Official implementation for the paper: Permutation Invariant Graph Generation via Score-Based Generative Modeling",115,21,"Python","2020-02-27","2023-07-06",0,[],"ermongroup"],["ermongroup/tile2vec","Implementation and examples for Tile2Vec",115,41,"Python","2018-02-12","2024-01-25",0,[],"ermongroup"],["snap-stanford/comet","[ICLR 2021] Concept Learners for Few-Shot Learning",115,15,"Python","2020-07-15","2023-06-06",0,[],"snap-stanford"],["Svensson-Lab/pro-hormone-predictor","Peptide Predictor - this program will annotate all proteins predicted to generate proteolytic cleavage products",115,10,"R","2020-12-18","2025-08-04",0,[],"Svensson-Lab"],["zou-group/sirius","SiriuS: Self-improving Multi-agent Systems via Bootstrapped Reasoning",113,10,"Python","2025-02-05","2025-12-01",0,["finetuning","llm","multiagent","reasoning"],"zou-group"],["fancompute/qpga","Simulations of photonic quantum programmable gate arrays",112,18,"Jupyter Notebook","2019-03-01","2020-10-10",0,["machine-learning","nanophotonics","optics","photonics"],"fancompute"],["stanford-cs348k/readings","This is a list of readings for CS348K.",112,17,"","2020-04-07","2026-05-27",0,[],"stanford-cs348k"],["stanford-futuredata/stk","",112,22,"Python","2021-06-17","2024-08-26",0,[],"stanford-futuredata"],["Gen-Verse/WideRange4D","WideRange4D: Enabling High-Quality 4D Reconstruction with Wide-Range Movements and Scenes",111,3,"Jupyter Notebook","2025-03-11","2025-03-19",0,["3d-reconstruction","4d-data-simulation","4d-database","4d-generation"],"Gen-Verse"],["kundajelab/chipseq_pipeline","AQUAS TF and histone ChIP-seq pipeline",111,39,"Java","2015-07-16","2022-06-21",0,["chip-seq","chip-seq-pipelines","genomics"],"kundajelab"],["ermongroup/flow-gan","Code for \"Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models\", AAAI 2018.",110,32,"Python","2017-09-08","2018-01-13",0,[],"ermongroup"],["HazyResearch/fm_data_tasks","Foundation Models for Data Tasks",110,21,"Python","2022-05-10","2023-05-15",0,[],"HazyResearch"],["Stanford-AIMI/chexpert-plus","",110,8,"Python","2024-03-29","2024-06-06",0,[],"Stanford-AIMI"],["stanford-star/relational-transformer","Pretraining, fine-tuning, inference and test-time scaling of relational transformer (RT) models, including RT-J and RT-PluRel",109,27,"Python","2025-07-10","2026-10-19",0,["relational-deep-learning","relational-foundation-models","tabular-foundation-models"],"stanford-star"],["HazyResearch/reef","Automatically labeling training data",108,27,"Jupyter Notebook","2017-09-25","2019-01-08",0,["machine-learning","stanford","synthesis","weakly-supervised-learning"],"HazyResearch"],["snap-stanford/prodigy","",108,14,"Python","2023-06-20","2023-07-12",0,[],"snap-stanford"],["computational-imaging/holographic-AR-glasses","",107,20,"Python","2024-03-14","2024-05-08",0,[],"computational-imaging"],["drorlab/DIPS","Database of Interacting Protein Structures (DIPS)",106,15,"Python","2019-10-21","2024-01-26",0,[],"drorlab"],["GradientSpaces/LivingScenes","[CVPR 2024, Highlight] Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments",106,8,"Python","2023-12-05","2024-07-05",0,["3d-vision","dynamic-scene-understanding","point-cloud"],"GradientSpaces"],["snap-stanford/orca","[ICLR 2022] Open-World Semi-Supervised Learning",105,11,"Python","2022-01-24","2022-02-17",0,[],"snap-stanford"],["Stanford-ILIAD/CARLO","2D Driving Simulator",105,40,"Python","2019-08-15","2024-03-11",0,[],"Stanford-ILIAD"],["stanford-cs336/assignment4-data","",104,308,"Python","2025-05-07","2026-05-07",0,[],"stanford-cs336"],["StanfordMIMI/skm-tea","Repository for the Stanford Knee MRI Multi-Task Evaluation (SKM-TEA) Dataset",104,18,"Python","2020-11-24","2023-05-28",0,["computer-vision","inverse-problems","machine-learning","mri"],"StanfordMIMI"],["HazyResearch/intelligence-per-watt","Benchmarking Intelligence Efficiency of LM Inference",103,15,"Python","2025-10-24","2026-10-25",0,[],"HazyResearch"],["som-shahlab/medalign","MedAlign is a clinician-generated dataset for instruction following with electronic medical records.",103,8,"","2023-08-31","2025-05-17",0,["benchmarking","ehr","electronic-health-records","foundation-models"],"som-shahlab"],["stanford-iprl-lab/multimodal_representation","",103,21,"Python","2020-05-12","2023-01-01",0,[],"stanford-iprl-lab"],["cvxgrp/GGS","Greedy Gaussian Segmentation",102,33,"Python","2016-10-17","2023-01-02",0,[],"cvxgrp"],["stanford-oval/wikidata-emnlp23","WikiSP, a semantic parser for Wikidata. WikiWebQuestions, a SPARQL-annotated dataset on Wikidata",102,10,"Python","2023-08-25","2025-09-15",0,[],"stanford-oval"],["stanford-ppl/Green-Marl","A DSL for efficient Graph Analysis",102,36,"C++","2012-01-04","2018-07-30",0,[],"stanford-ppl"],["GradientSpaces/ReStyle3D","[SIGGRAPH 2025] ReStyle3D: Scene-level Appearance Transfer with Semantic Correspondences",101,10,"Python","2025-02-11","2025-09-03",0,["3d-reconstruction","computer-vision","diffusion","scene-editing"],"GradientSpaces"],["HazyResearch/fonduer-tutorials","A collection of simple tutorials for using Fonduer",101,26,"Jupyter Notebook","2018-03-23","2020-10-27",0,[],"HazyResearch"],["stanford-ppl/spatial-lang","Spatial: \"Specify Parameterized Accelerators Through Inordinately Abstract Language\"",101,11,"Verilog","2017-01-12","2018-11-14",1,[],"stanford-ppl"],["StanfordVL/RubiksNet","Official repo for ECCV 2020 paper - RubiksNet: Learnable 3D-Shift for Efficient Video Action Recognition",99,8,"Python","2020-07-15","2020-09-06",0,[],"StanfordVL"],["Gen-Verse/ScoreFlow","ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization",98,9,"Python","2025-02-03","2025-05-22",0,[],"Gen-Verse"],["stanford-oval/almond-gnome","The Almond Virtual Assistant, Linux desktop version",98,11,"JavaScript","2016-11-30","2024-04-14",0,[],"stanford-oval"],["stanfordnlp/pdf-struct","Logical structure analysis for visually structured documents",98,16,"Python","2021-10-01","2022-08-20",0,[],"stanfordnlp"],["stanford-crfm/EUAIActJune15","Stanford CRFM's initiative to assess potential compliance with the draft EU AI Act",97,6,"","2023-06-13","2023-10-18",0,[],"stanford-crfm"],["stanfordhpccenter/HTR-solver","Hypersonic Task-based Research (HTR) solver for the Navier-Stokes equations at hypersonic Mach numbers including finite-rate chemistry for dissociating air and ",97,28,"C++","2020-03-24","2022-10-08",0,[],"stanfordhpccenter"],["stanfordmlgroup/stanfordmlgroup.github.io","Group Website",97,42,"HTML","2017-04-16","2026-04-24",0,[],"stanfordmlgroup"],["StanfordORB/Stanford-ORB","Official Repository of the Stanford-ORB dataset",97,2,"Jupyter Notebook","2023-09-25","2024-12-14",0,[],"StanfordORB"],["stanford-futuredata/selection-via-proxy","",96,22,"Python","2020-01-16","2021-01-14",0,[],"stanford-futuredata"],["stanford-mast/INFaaS","Model-less Inference Serving",95,21,"C++","2019-06-05","2023-11-04",0,[],"stanford-mast"],["computational-imaging/nlos-fk","Processing code for \"Wave-Based Non-Line-of-Sight Imaging using Fast f-k Migration\"",94,27,"MATLAB","2019-04-10","2022-06-19",0,[],"computational-imaging"],["Stanford-STAGES/stanford-stages","Automated sleep staging scoring and narcolepsy identification",94,28,"Python","2018-03-12","2024-06-24",0,[],"Stanford-STAGES"],["stanford-futuredata/equivariant-transformers","Equivariant Transformer (ET) layers are image-to-image mappings that incorporate prior knowledge on invariances with respect to continuous transformations group",93,9,"Jupyter Notebook","2019-01-25","2019-06-11",0,["computer-vision","convolutional-neural-networks","equivariance","invariance"],"stanford-futuredata"],["stanford-policylab/opp","Open Policing Project",93,53,"HTML","2017-09-18","2023-05-17",0,[],"stanford-policylab"],["zou-group/humanlm","HumanLM: Simulating Users with State Alignment Beats Response Imitation",93,13,"Python","2026-02-11","2026-06-04",0,[],"zou-group"],["sisl/MultiAgentAllocationTransit.jl","Efficient Large-Scale Multi-Drone Delivery Using Transit Networks",92,14,"Julia","2019-08-15","2023-07-13",0,[],"sisl"],["StanfordVL/feedback-networks","The repo of Feedback Networks, CVPR17",92,21,"Lua","2017-02-05","2019-01-13",0,[],"StanfordVL"],["eehh-stanford/SNA-workshop","Webpage materials for AAPA network workshop",91,22,"TeX","2018-04-02","2020-02-16",0,[],"eehh-stanford"],["SALT-NLP/Multi-View-Seq2Seq","Source codes for the paper \"Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization\"",91,15,"Python","2020-09-28","2023-10-27",0,["conversation","multiview","seq2seq","summarization"],"SALT-NLP"],["stanford-mast/reflex","ReFlex: Remote Flash == Local Flash",91,25,"C","2017-04-05","2018-12-21",0,[],"stanford-mast"],["stanford-crfm/fmti","The Foundation Model Transparency Index",90,12,"","2023-10-17","2025-12-09",0,[],"stanford-crfm"],["stanford-oval/genienlp","GenieNLP: A versatile codebase for any NLP task",90,24,"Python","2020-01-29","2024-03-16",0,["deep-learning","dialogue","natural-language-processing","nlp"],"stanford-oval"],["stanford-oval/thingtalk","The Programming Language of Virtual Assistants",90,21,"TypeScript","2016-01-31","2023-06-06",0,["dsl","hacktoberfest","programming-language","voice-assistant"],"stanford-oval"],["StanfordMIMI/RoentGen","",90,5,"Python","2022-11-23","2024-08-27",0,[],"StanfordMIMI"],["stanfordnmbl/mobile-gaitlab","",90,35,"Jupyter Notebook","2019-11-11","2022-12-08",0,[],"stanfordnmbl"],["biglocalnews/court-scraper","Scrapers for U.S. county court sites.",89,24,"Python","2020-08-07","2022-12-13",0,["courts","data-journalism","journalism","law-enforcement"],"biglocalnews"],["HazyResearch/TreeStructure","Table Extraction Tool",89,33,"Jupyter Notebook","2017-04-28","2018-02-28",0,[],"HazyResearch"],["ProteinDesignLab/protpardelle-1c","Updated Protpardelle models with more robust motif scaffolding and multichain support",88,15,"Python","2025-06-30","2026-10-02",0,[],"ProteinDesignLab"],["Stanford-ILIAD/explore-eqa","Public release for \"Explore until Confident: Efficient Exploration for Embodied Question Answering\"",88,8,"Python","2023-10-05","2024-07-05",0,["conformal-prediction","embodied-question-answering","habitat-sim","vision-language-model"],"Stanford-ILIAD"],["ermongroup/Sequential-Variational-Autoencoder","Implementation of Sequential Variational Autoencoder",87,15,"Python","2017-02-27","2017-08-23",0,["generative-model","variational-inference"],"ermongroup"],["snap-stanford/conformalized-gnn","Uncertainty Quantification over Graph with Conformalized Graph Neural Networks (NeurIPS 2023)",87,7,"Python","2023-09-03","2023-09-27",0,["calibration","conformal-prediction","gnn","graph"],"snap-stanford"],["ccrma/miniAudicle","miniAudicle: integrated development + performance environment for the ChucK programming language",86,15,"Objective-C++","2012-11-06","2026-04-19",0,[],"ccrma"],["cvxgrp/cocp","Source code for the examples accompanying the paper \"Learning convex optimization control policies.\"",86,18,"Jupyter Notebook","2019-12-19","2022-11-14",0,["control-systems","convex-optimization","differentiable-programming","learning"],"cvxgrp"],["Ignitus/Ignitus-client","Ignitus-client architecture . ☘️",86,119,"TypeScript","2018-05-29","2023-01-06",0,["emotion","heroku-pipeline","javascript","jest-tests"],"Ignitus"],["Stanford-AIMI/GREEN","[EMNLP, Findings 2024] a radiology report generation metric that leverages the natural language understanding of language models to identify and explain clinica",86,12,"Python","2024-05-14","2026-08-04",0,[],"Stanford-AIMI"],["sisl/Crux.jl","Julia library for deep reinforcement learning",85,21,"Julia","2020-11-07","2026-06-06",0,[],"sisl"],["snap-stanford/relgt","Relational Graph Transformer",85,21,"Python","2025-05-15","2025-07-10",0,["graph-neural-networks","graph-transformer","relational-databases","relational-deep-learning"],"snap-stanford"],["stanford-cs149/cs149gpt","",85,68,"Python","2023-11-16","2023-12-01",0,[],"stanford-cs149"],["ermongroup/multiagent-gail","",84,25,"Python","2018-10-17","2018-12-04",0,[],"ermongroup"],["mabuchilab/QNET","Computer algebra package for quantum mechanics and photonic quantum networks",84,24,"Python","2012-03-26","2018-12-18",0,["physics","python","quantum-computing","quantum-mechanics"],"mabuchilab"],["Stanford-AIMI/radgraph","",84,15,"Python","2024-06-28","2026-07-10",0,[],"Stanford-AIMI"],["stanford-iprl-lab/UniGrasp","Implementation of \" UniGrasp: Learning a Unified Model to Grasp with Multifingered Robotic Hands\"",84,12,"Python","2020-06-14","2023-01-02",0,[],"stanford-iprl-lab"],["snap-stanford/optimas","(ICLR 2026) Optimas: Optimizing Compound AI Systems",83,9,"Python","2025-07-07","2026-02-06",0,["compound-ai-systems","multiagent-systems","optimization","reward-learning"],"snap-stanford"],["stanford-star/plurel","A synthetic tabular and relational data generation framework",83,13,"Python","2026-01-29","2026-10-15",0,["relational-data","relational-foundation-models","scaling-laws","synthetic-data"],"stanford-star"],["StanfordASL/stlcg","",83,24,"Jupyter Notebook","2019-04-30","2024-02-24",0,[],"StanfordASL"],["poldracklab/fitlins","Fit Linear Models to BIDS Datasets",82,35,"Python","2017-10-23","2026-08-17",0,[],"poldracklab"],["stanford-oval/Churro","CHURRO is an OCR toolkit for historical document transcription, built to make handwritten and printed sources readable at high accuracy and lower cost.",82,5,"Python","2025-09-10","2026-04-18",0,["ai","document-parser","document-parsing","documents"],"stanford-oval"],["StanfordAHA/Halide-to-Hardware_archive","",82,14,"C++","2019-01-23","2025-02-07",0,[],"StanfordAHA"],["StanfordVL/ntp","Neural Task Programming",82,13,"","2017-09-14","2018-05-21",0,[],"StanfordVL"],["HazyResearch/HoroPCA","Hyperbolic PCA via Horospherical Projections",81,13,"Python","2021-05-27","2023-06-28",0,[],"HazyResearch"],["ProteinDesignLab/protein_seq_des","Code for our paper \"Protein sequence design with a learned potential\"",81,24,"Python","2020-10-13","2023-09-08",0,[],"ProteinDesignLab"],["StanfordVL/STR-PIP","Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction",81,18,"Python","2020-05-31","2022-03-02",0,[],"StanfordVL"],["biglocalnews/civic-scraper","Tools for downloading agendas, minutes and other documents produced by local government",80,26,"Python","2020-06-16","2026-09-11",0,["agendawatch","city-council-data","data-journalism","journalism"],"biglocalnews"],["snap-stanford/CAW","",80,18,"Python","2020-11-09","2022-09-27",0,[],"snap-stanford"],["StanfordASL/LearnedSamplingDistributions","",80,14,"Jupyter Notebook","2017-09-09","2019-01-24",0,[],"StanfordASL"],["StanfordHCI/imMens","Real-Time Visual Querying of Big Data",80,14,"JavaScript","2013-03-16","2014-03-26",0,[],"StanfordHCI"],["cvxgrp/ncvx","",79,18,"Python","2015-04-05","2022-05-26",0,[],"cvxgrp"],["Gen-Verse/HermesFlow","[NeurIPS 2025] HermesFlow: Seamlessly Closing the Gap in Multimodal Understanding and Generation",79,4,"Python","2025-02-11","2025-09-19",0,["image-to-text","multimodal-generation","multimodal-large-language-models","text-to-image"],"Gen-Verse"],["Mobisocial/EasyNFC","An NFC abstraction library for Android",79,22,"Java","2011-03-15","2023-12-16",0,[],"Mobisocial"],["snap-stanford/KGWAS","KGWAS: novel genetics discovery enabled by massive functional genomics knowledge graph",79,10,"Python","2024-06-03","2025-03-07",0,["ai","functional-genomics","genetics","knowledge-graph"],"snap-stanford"],["stanford-oval/spinach","SPINACH: SPARQL-Based Information Navigation for Challenging Real-World Questions",79,25,"Jupyter Notebook","2024-07-11","2026-02-26",0,[],"stanford-oval"],["stanford-rc/ibswinfo","Command-line tool to retrieve information and monitor Mellanox un-managed Infiniband switches",79,18,"Shell","2020-04-20","2026-10-23",0,["infiniband","monitoring"],"stanford-rc"],["ermongroup/markov-chain-gan","Code for \"Generative Adversarial Training for Markov Chains\" (ICLR 2017 Workshop)",78,18,"Python","2017-06-03","2017-10-25",0,["generative-adversarial-network","generative-model","iclr2017","markov-chain"],"ermongroup"],["yeatmanlab/AFQ","Automated Fiber Quantification",78,53,"MATLAB","2012-05-24","2024-03-06",0,[],"yeatmanlab"],["ermongroup/MetaIRL","Meta-Inverse Reinforcement Learning with Probabilistic Context Variables",77,8,"Python","2019-10-24","2023-03-16",0,[],"ermongroup"],["ermongroup/ssdkl","Code that accompanies the paper Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance",77,29,"Python","2018-10-24","2019-01-22",0,[],"ermongroup"],["HazyResearch/epoxy","Interactive Model Iteration with Weak Supervision and Pre-Trained Embeddings",77,5,"Python","2020-06-29","2022-07-26",0,[],"HazyResearch"],["sisl/OptimizationCourse","",77,36,"Jupyter Notebook","2015-07-24","2017-07-29",0,[],"sisl"],["stanford-esrg/gps","GPS is a scanning platform that learns and predicts the location of IPv4 services across all 65K ports.",77,10,"Python","2021-11-09","2023-02-07",0,["bigquery","internet-wide-scanning","ipv4","network"],"stanford-esrg"],["ermongroup/smile-mi-estimator","PyTorch implementation for the ICLR 2020 paper \"Understanding the Limitations of Variational Mutual Information Estimators\"",76,9,"Jupyter Notebook","2020-02-11","2020-02-12",0,[],"ermongroup"],["HazyResearch/CaffeConTroll","",76,18,"C++","2015-01-14","2016-07-25",0,[],"HazyResearch"],["computational-imaging/DepthFromDefocusWithLearnedOptics","ICCP2021: Depth from Defocus with Learned Optics for Imaging and Occlusion-aware Depth Estimation",75,9,"Python","2021-03-31","2021-06-10",0,[],"computational-imaging"],["snap-stanford/multiscale-interactome","",74,14,"Python","2020-04-28","2023-07-06",0,[],"snap-stanford"],["stanford-cs336/spring2024-assignment1-basics","",74,81,"Python","2024-04-01","2024-07-13",0,[],"stanford-cs336"],["StanfordHCI/c3","C3 (Categorical Color Components) is a JavaScript library for modeling color naming data. It can be used to create a variety of applications, including improved",74,17,"JavaScript","2011-12-21","2011-12-21",0,[],"StanfordHCI"],["stanfordmlgroup/nlm-noising","",74,13,"Python","2017-03-09","2017-05-12",0,[],"stanfordmlgroup"],["SUwonglab/CausalEGM","A General Causal Inference Framework by Encoding Generative Modeling",74,12,"Python","2022-06-07","2024-06-01",0,["causal-inference","causal-machine-learning","causality","deep-generative-model"],"SUwonglab"],["cvxgrp/fastpathplanning","A fast algorithm for finding an optimal path in a collection of safe boxes",73,5,"Python","2023-02-02","2025-02-28",0,[],"cvxgrp"],["cvxgrp/signal-decomposition","A simple and general framework for signal decomposition",73,10,"Jupyter Notebook","2019-11-28","2025-01-16",0,[],"cvxgrp"],["jonfanlab/GAN_for_metasurface","GAN-enabled metasuraface design",73,25,"Python","2019-09-30","2021-01-14",0,[],"jonfanlab"],["snap-stanford/stellar","",73,21,"Jupyter Notebook","2021-10-25","2023-01-18",0,[],"snap-stanford"],["StanfordASL/Adaptive-Control-Oriented-Meta-Learning","Adaptive control-oriented meta-learning for nonlinear systems",73,12,"Python","2021-03-07","2021-06-12",0,[],"StanfordASL"],["cvxgrp/cov_pred_finance","",72,19,"Jupyter Notebook","2023-05-07","2026-07-13",0,[],"cvxgrp"],["Gen-Verse/Diffusion-Sharpening","Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory Sharpening",72,6,"Python","2025-02-11","2025-05-18",0,["diffusion-models","inference-scaling","rlhf","text-to-image-generation"],"Gen-Verse"],["sisl/Chimp","General purpose framework for deep reinforcement learning",72,15,"Python","2015-08-19","2017-03-06",0,[],"sisl"],["som-shahlab/trove","Weakly supervised medical named entity classification",72,21,"Python","2020-11-26","2022-12-23",0,["bert","biomedical","learning-with-noisy-labels","ner"],"som-shahlab"],["stanford-oval/genie-worksheets","Specification for creating reliable LLM-based conversational agents",72,29,"HTML","2024-10-31","2025-10-20",0,[],"stanford-oval"],["StanfordPL/strata","Automatic inference of a formal specification of the x86_64 instruction set",72,14,"Assembly","2015-09-15","2016-04-11",0,[],"StanfordPL"],["lockss/lockss-daemon","Classic LOCKSS System (LOCKSS 1.x)",71,15,"Java","2016-01-13","2026-10-24",0,["classic-lockss","digital-preservation","lockss"],"lockss"],["snap-stanford/plato","",71,3,"Python","2023-10-27","2024-03-22",0,[],"snap-stanford"],["stanford-rc/sasutils","Serial Attached SCSI (SAS) Linux utilities and Python library",71,19,"Python","2016-09-07","2024-11-12",0,["linux","sas","scsi","storage"],"stanford-rc"],["drorlab/combind","Integrated physics-based and ligand-based modeling.",70,15,"Python","2021-12-08","2025-10-27",0,[],"drorlab"],["fancompute/fdfdpy","Pure Python implementation of the finite difference frequency domain (FDFD) method for electromagnetics",70,20,"Jupyter Notebook","2018-03-05","2018-11-05",0,["eigenvalues","eigenvectors","electromagnetics","fdfd"],"fancompute"],["stanfordnqp/maxwell-b","Multi-GPU FDFD solver",70,29,"Python","2019-05-02","2022-05-19",0,[],"stanfordnqp"],["Gen-Verse/GenEnv","[NeurIPS 2026] GenEnv: Difficulty-Aligned Co-Evolution Between LLM Agents and Environment Simulators",69,5,"Python","2025-12-09","2026-09-25",0,["llm-agents","rl"],"Gen-Verse"],["GradientSpaces/respace","Code for \"ReSpace: Text-Driven Autoregressive 3D Indoor Scene Synthesis and Editing\"",69,6,"Python","2025-06-02","2026-09-15",0,["3d-indoor-scene-synthesis","3d-scene-synthesis","indoor-scene-synthesis","indoor-scenes"],"GradientSpaces"],["stanfordroboticsclub/Pupper","Build instructions and code documentation for the Stanford Pupper project.",68,36,"","2020-05-26","2021-09-18",0,[],"stanfordroboticsclub"],["cvxgrp/cvxflow","",67,21,"Python","2016-09-12","2018-05-07",0,[],"cvxgrp"],["EngreitzLab/scE2G","Pipeline to run scE2G",67,8,"Python","2023-10-30","2026-10-16",0,[],"EngreitzLab"],["ermongroup/PatchDrop","PyTorch Implementation of `Learning to Process Fewer Pixels` - [CVPR20 (Oral)]",67,9,"Python","2019-09-07","2021-08-05",0,[],"ermongroup"],["HazyResearch/structured-nets","Structured matrices for compressing neural networks",67,23,"Python","2018-08-18","2023-10-05",0,[],"HazyResearch"],["Stanford-AIMI/MASS","[CVPR 2026] A mask-guided self-supervised learning method for 3D medical image representation learning",67,15,"Python","2026-03-10","2026-05-17",0,["in-context-learning","medical-image-analysis","representation-learning","self-supervised-learning"],"Stanford-AIMI"],["Stanford-Online/edx-theme","",67,456,"CSS","2013-04-29","2018-11-20",1,[],"Stanford-Online"],["jonfanlab/Metagrating-Topology-Optimization","",66,28,"MATLAB","2019-09-27","2022-03-15",0,[],"jonfanlab"],["Stanford-TML/PDP","Official code for PDP",66,4,"Python","2024-12-17","2025-02-03",0,[],"Stanford-TML"],["StanfordHCI/wrangler","",66,60,"JavaScript","2012-05-12","2012-05-30",0,[],"StanfordHCI"],["StanfordVL/robovat","RoboVat: A unified toolkit for simulated and real-world robotic task environments.",66,15,"Python","2020-02-06","2022-11-22",0,["deep-learning","deep-reinforcement-learning","physics-simulation","robotics"],"StanfordVL"],["computational-imaging/AcousticNLOS","Processing code for acoustic non-line-of-sight imaging",65,20,"Python","2018-11-16","2023-07-06",0,[],"computational-imaging"],["computational-imaging/neural-3d-holography","Code and data for Neural 3D Holography | SIGGRAPH Asia 2021",65,15,"Python","2022-04-19","2023-08-02",0,[],"computational-imaging"],["HazyResearch/prefix-linear-attention","",65,2,"Python","2024-06-26","2024-07-09",0,[],"HazyResearch"],["SALT-NLP/Structure-Aware-BART","Source codes for \"Structure-Aware Abstractive Conversation Summarization via Discourse and Action Graphs\"",65,8,"Python","2021-04-10","2023-10-27",0,["conversation","graphs","summarization"],"SALT-NLP"],["snap-stanford/snapvx","",65,33,"Python","2015-01-16","2020-07-12",0,[],"snap-stanford"],["StanfordASL/GuSTO.jl","Guaranteed Sequential Trajectory Optimization (GuSTO), using sequential convex programming for trajectory optimization with strong theoretical guarantees",65,16,"Julia","2018-09-15","2024-02-16",0,[],"StanfordASL"],["ccrma/music220a","The code examples for Music 220A",64,22,"HTML","2012-10-05","2024-10-14",0,["audio","ccrma","computermusic","dsp"],"ccrma"],["ermongroup/generative_adversary","Code for the unrestricted adversarial examples paper (NeurIPS 2018)",64,16,"Python","2018-09-10","2019-07-16",0,["adversarial-attacks","adversarial-examples","neurips2018"],"ermongroup"],["snap-stanford/miner-data","",64,14,"Python","2016-04-29","2019-11-21",0,[],"snap-stanford"],["StanfordASL/RobustMP","Robust Online Motion Planning using Contraction Theory",64,15,"MATLAB","2019-01-02","2019-01-02",0,[],"StanfordASL"],["StanfordVL/mini_behavior","MiniGrid Implementation of BEHAVIOR Tasks",64,12,"Python","2022-07-11","2025-09-20",0,[],"StanfordVL"],["TheStanfordDaily/stanforddaily","The Stanford Daily website frontend is proudly powered by Expo.",64,9,"TypeScript","2019-08-10","2023-06-19",0,["expo","react-native","stanford-daily","wordpress-headless"],"TheStanfordDaily"],["HazyResearch/hidden-stratification","Combating hidden stratification with GEORGE",63,21,"Jupyter Notebook","2020-07-01","2021-05-18",0,[],"HazyResearch"],["sisl/SatelliteDynamics.jl","SatelliteDynamics.jl is a satellite dynamics modeling package written for Julia.",63,10,"Julia","2018-11-21","2026-10-26",0,[],"sisl"],["ccrma/webchuck","ChucK on the Web",62,11,"JavaScript","2021-10-20","2026-04-19",0,["emscripten","webassembly","webaudio"],"ccrma"],["EduNLP/textbook-analysis","Code for the paper \"Content Analysis of Textbooks via Natural Language Processing\".",62,23,"Jupyter Notebook","2019-10-08","2023-07-06",0,[],"EduNLP"],["ermongroup/pirank","PiRank: Learning to Rank via Differentiable Sorting",62,10,"Python","2019-10-04","2023-03-24",0,[],"ermongroup"],["kundajelab/phantompeakqualtools","This package computes informative enrichment and quality measures for ChIP-seq/DNase-seq/FAIRE-seq/MNase-seq data. It can also be used to obtain robust estimate",62,16,"R","2016-07-28","2020-07-20",0,[],"kundajelab"],["cvxgrp/auto_ks","Repository for \"Fitting a Kalman Smoother to Data\"",61,10,"Python","2019-10-09","2024-02-22",0,[],"cvxgrp"],["GradientSpaces/HouseTour","[ICCV 2025] HouseTour: A Virtual Real Estate A(I)gent",61,4,"Python","2024-12-20","2025-10-22",0,[],"GradientSpaces"],["SALT-NLP/positive-frames","Data and code for the paper \"Inducing Positive Perspectives with Text Reframing\"",61,10,"Python","2022-03-31","2023-07-18",0,[],"SALT-NLP"],["sisl/AutomotiveDrivingModels.jl","Driving simulation architecture for Julia",61,29,"Julia","2014-11-20","2020-03-22",0,[],"sisl"],["StanfordAHA/aha","",61,11,"Python","2019-12-04","2026-10-25",0,[],"StanfordAHA"],["stanfordnlp/cs224n-web","http://cs224n.stanford.edu",61,32,"HTML","2015-09-24","2015-12-20",0,[],"stanfordnlp"],["computational-imaging/DeepOpticsHDR","Code associated with the paper \"Deep Optics for Single-shot High-dynamic-range Imaging\"",60,8,"Python","2020-03-29","2020-08-18",0,[],"computational-imaging"],["scpd-proed/XCS224N-Handouts","Contains Lecture Notes and Suggested Readings for XCS224N",60,94,"TeX","2021-05-14","2025-07-01",0,[],"scpd-proed"],["Stanford-ILIAD/APReL","A Library for Active Preference-based Reward Learning Algorithms",60,12,"Python","2021-08-09","2023-12-16",0,[],"Stanford-ILIAD"],["stanford-mast/a1","The agent-to-code compiler that optimizes AI for speed 🏎️ and safety 🏁",60,4,"Python","2025-11-10","2026-02-23",0,["agent","ai","framework","python"],"stanford-mast"],["yeatmanlab/pyAFQ","Please visit our new location: https://github.com/tractometry/pyAFQ",60,32,"Python","2016-02-03","2024-10-10",1,[],"yeatmanlab"],["cvxgrp/CuClarabel","",59,6,"Julia","2023-05-23","2026-06-05",1,[],"cvxgrp"],["ermongroup/graphite","Code for Graphite iterative graph generation",59,25,"Python","2018-05-31","2019-06-07",0,[],"ermongroup"],["snopt/snopt-matlab","Matlab interface for sparse nonlinear optimizer SNOPT",59,24,"MATLAB","2014-10-20","2021-10-19",0,["matlab","nlp","nonlinear-optimization","snopt"],"snopt"],["stanford-cs324/winter2022","Website",59,11,"Python","2021-12-23","2023-01-24",0,[],"stanford-cs324"],["StanfordBioinformatics/SJM","Simple Job Manager",59,25,"C++","2013-02-28","2017-07-20",0,[],"StanfordBioinformatics"],["startup-class/bitstarter-ssjs-db","Very simple SSJS app that writes crowdfunder preorders to a local database. Uses Sequelize, Express, Async.",59,282,"JavaScript","2013-08-09","2013-08-09",0,[],"startup-class"],["ermongroup/subsets","Code for Reparameterizable Subset Sampling via Continuous Relaxations, IJCAI 2019.",58,8,"Python","2019-05-29","2023-10-03",0,[],"ermongroup"],["Stanford-Health/wearipedia","Wearipedia python package",58,28,"Python","2022-10-13","2025-07-22",0,[],"Stanford-Health"],["stanford-mast/iBench","Suite of contentious microbenchmarks",58,23,"C","2016-06-14","2017-03-03",0,[],"stanford-mast"],["SALT-NLP/FLANG","When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain",57,14,"Python","2021-10-26","2025-02-11",0,[],"SALT-NLP"],["stanford-ppl/hyperdsl","",57,16,"Python","2014-02-21","2017-02-19",0,[],"stanford-ppl"],["StanfordASL/AA203-Examples","Examples shown in class and recitation from AA203: Optimal and Learning-Based Control.",57,27,"Jupyter Notebook","2019-05-11","2025-04-30",0,[],"StanfordASL"],["stanfordLINQS/SQcircuit","Superconducting quantum circuit analyzer",57,11,"Python","2020-12-08","2026-08-28",0,[],"stanfordLINQS"],["StanfordVL/iGibsonChallenge2021","",57,8,"Python","2021-02-17","2021-04-06",0,[],"StanfordVL"],["braindynamicslab/dyneusr","Dynamical Neuroimaging Spatiotemporal Representations (DyNeuSR)",56,18,"Python","2019-04-24","2025-11-25",0,["brain-dynamics","brain-networks","mapper-algorithm","neuroimaging"],"braindynamicslab"],["sisl/AttentionAugmentedConvLSTM","Implementation of TAAConvLSTM and SAAConvLSTM used in \"Attention Augmented ConvLSTM for Environment Prediction\"",56,15,"Python","2020-10-04","2022-01-18",0,[],"sisl"],["sisl/SCoBA.jl","Stochastic Conflict-Based Allocation",56,6,"Julia","2020-02-07","2020-10-07",0,["conflict-based-search","multiagent-planning","sequential-decisions","task-allocation"],"sisl"],["som-shahlab/INSPECT_public","INSPECT dataset/benchmark paper, accepted by NeurIPS 2023",56,8,"Python","2023-11-06","2025-05-20",0,[],"som-shahlab"],["stanford-oval/almond-dialog-agent","The Open Virtual Assistant",56,6,"JavaScript","2016-02-10","2021-06-09",1,[],"stanford-oval"],["ermongroup/CalibratedModelBasedRL","Code for \"Calibrated Model-Based Deep Reinforcement Learning\", ICML 2019.",55,11,"Python","2019-05-13","2019-05-15",0,[],"ermongroup"],["StanfordASL/ALPaCA","Code for \"Meta-Learning Priors for Efficient Online Bayesian Regression\" by James Harrison, Apoorva Sharma, and Marco Pavone",55,12,"Jupyter Notebook","2018-07-23","2023-02-15",0,[],"StanfordASL"],["computational-imaging/GraphPDE","",54,18,"Jupyter Notebook","2022-06-15","2022-06-16",0,[],"computational-imaging"],["cvxgrp/simulator","Tool to support backtests",54,4,"HTML","2023-04-21","2026-10-20",0,[],"cvxgrp"],["ermongroup/necst","Neural Joint-Source Channel Coding",54,16,"Python","2019-04-28","2019-05-13",0,[],"ermongroup"],["fancompute/ising-compiler","🍰 Compiling your code to an Ising Hamiltonian so you don't have to!",54,3,"Jupyter Notebook","2019-11-06","2020-04-30",0,[],"fancompute"],["SALT-NLP/PrivacyLens","A data construction and evaluation framework to quantify privacy norm awareness of language models (LMs) and emerging privacy risk of LM agents. (NeurIPS 2024 D",54,9,"Python","2024-05-25","2025-03-04",0,["language-model-agent","language-model-evaluation","large-language-model","neurips-2024"],"SALT-NLP"],["scpd-proed/General_Handouts","",54,128,"TeX","2020-11-04","2024-03-06",0,[],"scpd-proed"],["sisl/GridInterpolations.jl","Multidimensional grid interpolation in arbitrary dimensions",54,13,"Julia","2015-02-12","2025-03-25",0,[],"sisl"],["som-shahlab/hf_ehr","Training HuggingFace models on EHR data",54,10,"Jupyter Notebook","2023-10-05","2025-11-02",0,["electronic-health-records","foundation-models","healthcare","machine-learning"],"som-shahlab"],["sul-dlss/SearchWorks","SearchWorks (Stanford University Libraries)",54,11,"Ruby","2014-05-02","2026-10-26",0,["access","application","blacklight-application","rails"],"sul-dlss"],["cs106l/cs106l-lecture-code","Lecture code for CS106L",53,48,"C++","2024-10-14","2026-05-19",0,[],"cs106l"],["kundajelab/DART-Eval","",53,6,"Jupyter Notebook","2024-02-05","2026-03-22",0,[],"kundajelab"],["kundajelab/dfim","Deep Feature Interaction Maps (DFIM)",53,7,"Python","2018-02-19","2019-04-09",0,[],"kundajelab"],["SALT-NLP/PopupAttack","Code repo for the paper: Attacking Vision-Language Computer Agents via Pop-ups",53,3,"Python","2024-11-04","2024-12-23",0,["attack","claude-3-5-sonnet","computer-use","llm-agent"],"SALT-NLP"],["stanford-cs336/assignment1-basics-leaderboard","",53,156,"","2025-03-29","2026-04-19",0,[],"stanford-cs336"],["stanford-futuredata/sinkhorn-label-allocation","Sinkhorn Label Allocation is a label assignment method for semi-supervised self-training algorithms. The SLA algorithm is described in full in this ICML 2021 pa",53,3,"Python","2021-02-16","2021-06-15",0,["few-shot-learning","image-classification","optimal-transport","semi-supervised-learning"],"stanford-futuredata"],["cvxgrp/cvxpower","Power Network Optimization and Simulation.",52,22,"Python","2017-03-05","2023-06-30",0,[],"cvxgrp"],["ermongroup/ODS","Code for \"Diversity can be Transferred: Output Diversification for White- and Black-box Attacks\"",52,5,"Python","2020-02-12","2020-11-02",0,[],"ermongroup"],["Gen-Verse/Paper2Video","[ICCV 2025] Preacher: Paper-to-Video Agentic System",52,3,"Python","2025-09-01","2025-09-01",0,["multiagent-systems","video-agents","video-generation"],"Gen-Verse"],["kundajelab/3DChromatin_ReplicateQC","Software to compute reproducibility and quality scores for Hi-C data",52,17,"Python","2017-08-28","2019-04-12",0,[],"kundajelab"],["scslab/hails","The security-centric web platform framework",52,8,"Haskell","2011-12-21","2016-09-17",0,[],"scslab"],["stanford-ppl/Forge","A prototype meta DSL that generates Delite DSL implementations from a specification-like program.",52,23,"Scala","2012-10-19","2017-02-28",0,[],"stanford-ppl"],["stanfordnqp/jaxwell","Jaxwell is JAX + Maxwell",52,20,"Jupyter Notebook","2020-11-21","2023-09-27",1,[],"stanfordnqp"],["SALT-NLP/CultureBank","",51,9,"Python","2024-04-05","2025-09-29",0,[],"SALT-NLP"],["StanfordVL/atp-video-language","Official repo for CVPR 2022 (Oral) paper: Revisiting the \"Video\" in Video-Language Understanding. 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A Data-Driven Skills Framework for Understanding and Training Language Models",49,8,"Jupyter Notebook","2023-10-27","2023-10-31",0,[],"HazyResearch"],["sisl/SecureForge","",49,8,"Python","2025-10-20","2026-09-22",0,[],"sisl"],["stanford-gps-lab/maast","MATLAB Algorithm Availability Simulation Tool",49,11,"MATLAB","2019-02-28","2025-06-03",0,[],"stanford-gps-lab"],["Stanford-ILIAD/droc","Public release for \"Distillation and Retrieving Generalizable Knowledge for Robot Manipulation via Language Corrections\"",49,7,"Python","2023-09-26","2024-06-16",0,[],"Stanford-ILIAD"],["stanford-oval/chainlite","LangChain + LiteLLM that works",49,8,"Python","2024-05-19","2025-09-01",1,["agent","anthropic","gpt","langchain"],"stanford-oval"],["StanfordASL/elastiqp","An always-feasible QP solver for constrained robot control",49,1,"C++","2026-08-03","2026-10-26",0,[],"StanfordASL"],["GradientSpaces/WildPose","[CVPR 2026] WildPose: A Unified Framework for Robust Pose Estimation in the Wild",48,4,"Python","2026-05-11","2026-05-14",0,[],"GradientSpaces"],["jonfanlab/metachat","",48,11,"Python","2025-03-31","2025-12-22",0,[],"jonfanlab"],["SALT-NLP/implicit-hate","",48,4,"","2021-09-08","2023-06-29",0,[],"SALT-NLP"],["sisl/deep_flow_control","Source code for \"Deep Dynamical Modeling and Control of Unsteady Fluid Flows\" from NeurIPS 2018",48,19,"Python","2018-10-25","2018-12-03",0,[],"sisl"],["stanford-mast/shinjuku","",48,18,"C","2019-02-12","2023-04-11",0,[],"stanford-mast"],["StanfordGeospatialCenter/MSA","The Mass Shootings in America Database. Maintained by The Stanford Geospatial Center",48,60,"","2016-04-09","2019-02-14",0,[],"StanfordGeospatialCenter"],["stanfordmlgroup/MoCo-CXR","MoCo-based unsupervised training for Chest X-Ray Interpretation",48,7,"Python","2021-04-24","2022-01-09",0,[],"stanfordmlgroup"],["StanfordMSL/Splat-MOVER","Splat-MOVER: Multi-Stage, Open-Vocabulary Robotic Manipulation via Editable Gaussian Splatting",48,3,"Python","2024-09-28","2026-06-11",0,[],"StanfordMSL"],["TreeHacks/hackpack-ios","An intro to iOS development in Swift",48,15,"Swift","2016-02-02","2019-01-26",0,["ios","swift","tutorial"],"TreeHacks"],["fancompute/rcwa4d","Rigorous Coupled Wave Analyses (RCWA) for layered structures with incommensurate periodicities",47,14,"Python","2024-02-27","2026-10-02",0,[],"fancompute"],["HazyResearch/numbskull","Numba-based version of DimmWitted Gibbs sampler",47,14,"Python","2016-08-24","2018-05-14",0,[],"HazyResearch"],["sisl/ExprOptimization.jl","Algorithms for optimization of Julia 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Notebook","2017-09-04","2018-02-25",0,[],"stanford-futuredata"],["stanford-futuredata/Willump","Willump Is a Low-Latency Useful Machine learning Platform.",45,6,"Python","2018-10-31","2023-03-24",0,[],"stanford-futuredata"],["Stanford-TML/SpringGrasp_release","Official implemnetation of SpringGrasp: Synthesizing Compliant, Dexterous Grasp under Shape Uncertainty",45,8,"Python","2024-04-20","2025-10-15",0,[],"Stanford-TML"],["StanfordASL/soft-robot-control","Implementation of reduced-order optimal control schemes for finite element models of Soft Robots",45,5,"Python","2020-10-29","2024-10-03",0,[],"StanfordASL"],["StanfordMIMI/villa","[ICCV 2023] ViLLA: Fine-grained vision-language representation learning from real-world data",45,3,"Python","2023-07-19","2023-10-15",0,[],"StanfordMIMI"],["WagnerLabPapers/Waskom_PNAS_2017","Analysis code for Waskom and Wagner (2017) PNAS",45,11,"Python","2017-01-25","2017-02-25",0,[],"WagnerLabPapers"],["cvxgrp/vwap_opt_exec","Volume Weighted Average 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Render an ISET scene or optical image.",38,7,"HTML","2017-10-09","2022-08-31",0,[],"ISET"],["SALT-NLP/SWE-chat","SWE-chat: Coding Agent Interactions From Real Users in the Wild",38,2,"Jupyter Notebook","2026-04-22","2026-10-02",0,[],"SALT-NLP"],["sisl/InteractionImitation","Imitation Learning with the INTERACTION Dataset",38,5,"Python","2021-07-02","2024-04-01",0,[],"sisl"],["snap-stanford/mambo","",38,13,"Jupyter Notebook","2017-07-19","2017-12-03",0,[],"snap-stanford"],["stanford-gps-lab/spoofing-detection","Algorithms and simulations on spoofing detection.",38,20,"MATLAB","2020-05-08","2021-05-26",0,[],"stanford-gps-lab"],["Stanford-ILIAD/active-preference-based-gpr","Companion code for RSS 2020 paper: \"Active Preference-Based Gaussian Process Regression for Reward Learning\"",38,11,"Python","2020-05-06","2024-03-24",0,[],"Stanford-ILIAD"],["Stanford-TML/HEAD_release","Official Implementation of CoRL 2025 paper: Hand-Eye Autonomous Delivery: Learning Humanoid Navigation, Locomotion and Reaching",38,3,"","2025-08-08","2025-08-14",0,[],"Stanford-TML"],["StanfordASL/CoCo","",38,15,"Jupyter Notebook","2020-03-29","2023-02-10",0,[],"StanfordASL"],["StanfordHCI/FeedMonitor","",38,11,"JavaScript","2024-02-04","2026-05-06",0,[],"StanfordHCI"],["StanfordMIMI/LieRE","[ICML-2025] We introduce Lie group Relative position Encodings (LieRE) that goes beyond RoPE in supporting n-dimensional inputs.",38,0,"Python","2025-05-30","2025-08-13",0,[],"StanfordMIMI"],["stanfordnlp/contract-nli-bert","A baseline system for ContractNLI (https://stanfordnlp.github.io/contract-nli/)",38,14,"Python","2021-09-18","2023-03-10",0,[],"stanfordnlp"],["stanfordpython/python-assignments","Assignments for CS41",38,88,"","2016-04-06","2021-06-01",0,[],"stanfordpython"],["cvxgrp/cvxmarkowitz","Markowitz portfolio construction on CVXPY — DPP-compliant builders that solve long sequences of related problems without recompiling as assets and factors come ",37,9,"Python","2023-06-09","2026-10-26",0,["convex-optimization","cvxpy","markowitz","portfolio-optimization"],"cvxgrp"],["cvxgrp/markowitz-reference","This repository contains a reference implementation of the Markowitz portfolio optimization problem discussed in the paper Markowitz Portfolio Construction at S",37,10,"Python","2023-09-08","2026-07-13",0,[],"cvxgrp"],["EngreitzLab/ENCODE_rE2G","Enhancer-Gene Prediction Pipeline based on logistic regression and ABC model",37,6,"Python","2023-07-12","2026-09-25",0,[],"EngreitzLab"],["fancompute/vtmm","Vectorized transfer matrix method (TMM) for computing the optical reflection and transmission of multilayer planar stacks",37,9,"Python","2020-01-18","2020-09-02",0,["optics","photonics","tensorflow","transfer-matrix-method"],"fancompute"],["Ignitus/Ignitus-rest-api","Ignitus server architecture ☘️",37,23,"TypeScript","2018-05-27","2024-04-23",0,["ignitus-scholars","mongodb","moongose","nodejs"],"Ignitus"],["mtzgroup/ChemPixCH","Recognising hand-drawn molecules with neural networks",37,8,"Python","2021-02-05","2022-11-05",0,["papers-with-code"],"mtzgroup"],["SALT-NLP/Efficient_Unlearning","",37,1,"Python","2023-10-18","2023-10-18",0,[],"SALT-NLP"],["snap-stanford/llm-social-network","",37,13,"Jupyter Notebook","2023-06-26","2025-06-30",0,[],"snap-stanford"],["som-shahlab/Clinfo.AI","This is Clinfo.AI Demo Instruction",37,10,"Jupyter Notebook","2023-05-12","2024-08-20",0,[],"som-shahlab"],["stanford-iris-lab/ScholarCatalyst","A Benchmark for Retrieving Papers that Inspire New Research",37,3,"Python","2026-09-29","2026-10-02",0,["benchmark","retrieval","scientific-discovery"],"stanford-iris-lab"],["stanford-oval/thingengine-core","A Modular, Powerful Virtual Assistant Engine",37,6,"JavaScript","2016-02-11","2021-05-31",1,[],"stanford-oval"],["stanfordmlgroup/disentanglement","Official repository for our ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology",37,4,"Python","2021-01-13","2021-03-22",0,[],"stanfordmlgroup"],["stgl/poly3d","Boundary element method for modeling faults and fractures discretized into polygonal elements, reproduced from M.S. thesis of A. 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Multi-task deep learning predicts cognition, disea",20,2,"Python","2025-11-26","2026-10-24",0,[],"bdsp-core"],["cicl-stanford/moca","Language model evaluation for morality and causality",20,0,"Python","2021-08-11","2023-11-14",0,[],"cicl-stanford"],["cvxgrp/pdos","Primal-Dual Operator Splitting Method for Conic Optimization",20,5,"C","2013-03-11","2014-11-01",0,[],"cvxgrp"],["EngreitzLab/gene_network_evaluation","Evaluation framework for computationally inferred gene networks from single-cell data.",20,7,"Jupyter Notebook","2023-11-28","2025-10-23",0,["enhancer-prediction","gene-co-expression-network","gene-regulatory-network","single-cell-genomics"],"EngreitzLab"],["ermongroup/best-arm-delayed","Code for \"Best arm identification in multi-armed bandits with delayed feedback\", AISTATS 2018.",20,3,"Python","2018-04-03","2018-04-03",0,[],"ermongroup"],["ermongroup/bgm","Code for \"Boosted Generative Models\", AAAI 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Estimating the Prevalence of Surveillance Cameras with Street View Data\"",20,2,"Python","2021-05-19","2021-07-15",0,["computer-vision","fairness","society","surveillance"],"stanford-policylab"],["stanford-rc/ct_gdrive","Lustre/HSM Google Drive copytool",20,6,"Python","2016-04-28","2020-09-15",0,["google-drive","hsm","lustre","python2"],"stanford-rc"],["StanfordAHA/doc","Papers, Posters, Presentations, Documentation...",20,5,"","2018-07-05","2024-01-09",0,[],"StanfordAHA"],["StanfordASL/chreach","Convex hulls of reachable sets",20,0,"Python","2023-03-27","2026-04-14",0,[],"StanfordASL"],["StanfordASL/pora-exercises","Code Exercises for Principles of Robot Autonomy",20,7,"Jupyter Notebook","2025-07-26","2026-03-29",0,[],"StanfordASL"],["StanfordBDHG/ResearchKitOnFHIR","HL7 FHIR Structured Data Capture with ResearchKit on iOS",20,2,"Swift","2022-08-30","2026-04-14",0,["fhir","questionnaire","researchkit","stanford"],"StanfordBDHG"],["StanfordBioinformatics/Hummingbird","Hummingbird: Efficient Performance Prediction for Executing Genomic Applications in the Cloud: https://doi.org/10.1093/bioinformatics/btab161",20,5,"Python","2018-08-06","2024-07-19",0,[],"StanfordBioinformatics"],["stanfordio/skyfall","Bluesky ingest tool",20,4,"Go","2023-11-13","2025-11-20",0,[],"stanfordio"],["StanfordMIMI/MedVAL","Toward Expert-Level Medical Text Validation with Language Models",20,2,"Python","2025-06-25","2025-10-23",0,["llm-as-judge","medical-text"],"StanfordMIMI"],["stanfordmlgroup/cdr-mimic","Official Repository for our UAI paper Countdown Regression on the MIMIC-III Dataset",20,7,"Python","2018-07-14","2019-06-21",0,[],"stanfordmlgroup"],["stanfordmlgroup/dq","Queue system (jobs) on the deep cluster",20,5,"Shell","2016-01-11","2016-10-02",0,[],"stanfordmlgroup"],["stanfordnlp/python-corenlp-protobuf","Python bindings for Stanford CoreNLP's protobufs.",20,7,"Python","2017-04-28","2018-07-23",0,["corenlp"],"stanfordnlp"],["StanfordSpezi/SpeziBluetooth","Connect and 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JavaScript",20,9,"TypeScript","2022-07-11","2026-06-11",0,[],"yeatmanlab"],["biglocalnews/prefect-flow-template","A template repository with all the fundamentals needed to develop and deploy a Python data-processing routine for Prefect pipelines.",19,0,"Python","2022-03-28","2022-03-29",0,["data-journalism","etl","etl-pipeline","pipeline"],"biglocalnews"],["ccrma/faust2ck","A FAUST wrapper-generator for ChucK Ugens",19,4,"Faust","2012-11-11","2026-02-21",0,[],"ccrma"],["codexstanford/LawScript","",19,3,"JavaScript","2022-11-11","2024-01-16",0,[],"codexstanford"],["computational-imaging/PixelRNN","Official Implementation of PixelRNN: In-Pixel Recurrent Neural Networks for End-to-end--optimized Perception with Neural Sensors",19,4,"Python","2023-01-06","2024-10-27",0,[],"computational-imaging"],["cvxgrp/rsw","rsw: optimal representative sample weighting.",19,7,"Python","2020-05-14","2020-05-21",0,[],"cvxgrp"],["deisseroth-lab/multifiber","A GUI and library for doing multi-fiber photometry",19,5,"Matlab","2015-06-30","2017-10-12",0,[],"deisseroth-lab"],["drorlab/GPCR-mining","Functions to scrape GPCR data from the web.",19,2,"Python","2021-04-22","2021-11-18",0,[],"drorlab"],["ermongroup/WikipediaPovertyMapping","Implementation of Geolocated Articles Processing and Poverty Mapping - [KDD19]",19,10,"Jupyter Notebook","2019-05-17","2021-04-24",0,[],"ermongroup"],["HazyResearch/ddlog","Compiler for writing DeepDive applications in a Datalog-like language — ⚠️🚧🛑 REPO MOVED TO DEEPDIVE 👇🏿",19,4,"Scala","2015-04-09","2017-01-23",0,[],"HazyResearch"],["HazyResearch/smallfry","",19,4,"Python","2019-03-08","2019-09-10",0,[],"HazyResearch"],["HazyResearch/tabi","Code release for Type-Aware Bi-Encoders for Open-Domain Entity Retrieval",19,3,"Python","2022-03-13","2022-09-24",0,[],"HazyResearch"],["Helix-Research-Lab/Pool_PaRTI","Pooling by PageRank Token Importance. This is a weighted average pooling method where the weights are derived from the internal attention matrices of the encode",19,4,"Jupyter Notebook","2024-10-02","2025-06-11",0,[],"Helix-Research-Lab"],["kundajelab/basepairmodels","",19,7,"Python","2020-04-30","2023-03-24",0,[],"kundajelab"],["SALT-NLP/DARG","The official repo for DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph",19,3,"Python","2024-06-08","2024-10-13",0,[],"SALT-NLP"],["SALT-NLP/Persuasive-Orderings","Source codes for the paper \"Examining the Ordering of Rhetorical Strategies in Persuasive Requests\"",19,3,"Jupyter Notebook","2020-09-30","2021-09-12",0,["computationalsocialscience","persuasion"],"SALT-NLP"],["sisl/LOPR","This is the official implementation of LOPR used in \"LOPR: Latent Occupancy PRediction using Generative 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Notebook","2020-11-07","2020-11-07",0,[],"stanfordmlgroup"],["StanfordMSL/QuadsManip","Cooperative object transport using multiple quadrotors without communication",19,6,"C","2018-02-25","2018-05-19",0,[],"StanfordMSL"],["StanfordVL/cavin","",19,4,"Python","2020-03-12","2020-03-13",0,[],"StanfordVL"],["sul-dlss/sul-embed","An oEmbed Service for Stanford University Libraries",19,6,"Ruby","2014-10-06","2026-10-26",0,["access","application","rails-application"],"sul-dlss"],["TreeHacks/hackpack-arkit","",19,4,"Swift","2018-01-28","2020-02-10",0,[],"TreeHacks"],["bdsp-core/IIIC-SPaRCNet","",18,8,"MATLAB","2023-04-27","2026-07-06",0,[],"bdsp-core"],["bdsp-core/SpikeNet2","",18,3,"Jupyter Notebook","2024-09-20","2026-07-07",0,[],"bdsp-core"],["cs106l/textbook","",18,10,"TypeScript","2024-12-16","2025-05-08",0,[],"cs106l"],["EngreitzLab/ABC-GWAS-Paper","Code to reproduce analyses in Nasser, Bergman, Fulco, Guckelberger, Doughty et al Nature 2021",18,2,"PostScript","2021-02-23","2021-04-08",0,[],"EngreitzLab"],["fancompute/simphox","Another inverse design library (wip)",18,4,"Python","2020-08-08","2023-01-07",0,[],"fancompute"],["Helix-Research-Lab/PGxPOP","PGxPOP",18,2,"Python","2020-05-05","2023-01-25",1,[],"Helix-Research-Lab"],["kundajelab/scATAC-reprog","Code for the analysis performed in the paper \"Deep learning the cis-regulatory code of chromatin dynamics during cellular reprogramming\" by Nair, Ameen et al.",18,2,"Jupyter Notebook","2020-01-19","2025-10-16",0,[],"kundajelab"],["sisl/Double-Prong-Occupancy","Double-Prong ConvLSTM for Spatiotemporal Occupancy Prediction in Dynamic Environments",18,2,"Python","2020-11-06","2021-08-12",0,[],"sisl"],["sisl/MineralExploration","",18,6,"Julia","2021-09-27","2024-02-14",0,[],"sisl"],["sisl/pygments-julia","Pygments lexer for Julia",18,5,"Python","2018-02-23","2026-03-10",0,[],"sisl"],["sisl/vehicleshapes","Vehicle (car) shapes for use with the Tikz LaTeX package.",18,4,"TeX","2019-03-02","2023-12-16",0,[],"sisl"],["snap-stanford/exposure-segregation","",18,0,"Python","2023-08-24","2023-11-28",0,[],"snap-stanford"],["Stanford-ILIAD/easy-active-learning","Companion code to CoRL 2019 paper: E Bıyık, M Palan, NC Landolfi, DP Losey, D Sadigh. \"Asking Easy Questions: A User-Friendly Approach to Active Reward Learning",18,1,"Python","2019-06-03","2020-10-13",0,[],"Stanford-ILIAD"],["Stanford-NavLab/uwb-firmware","",18,2,"C","2021-05-14","2022-07-27",0,[],"Stanford-NavLab"],["Stanford-Online/xblock-in-video-quiz","",18,25,"Python","2016-07-14","2024-01-17",0,[],"Stanford-Online"],["stanford-ppl/argon","Scala staging framework",18,1,"Scala","2016-12-08","2018-07-13",0,[],"stanford-ppl"],["stanford-rc/www.sherlock.stanford.edu","The Sherlock website",18,28,"CSS","2018-04-04","2026-10-26",0,["cluster","documentation","hpc","sherlock"],"stanford-rc"],["Stanford-TML/HEAD_rl_deploy","Official implementation of HEAD CoRL 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projects.",18,5,"C++","2011-11-10","2018-11-04",0,[],"StanfordPL"],["stanfordroboticsclub/fusion360EquationCurve","",18,6,"Python","2018-03-15","2018-12-02",0,[],"stanfordroboticsclub"],["susom/redcap-extras","Custom plugins, hooks, DET, and API examples",18,16,"PHP","2014-05-01","2015-10-07",0,["needs-review","redcap"],"susom"],["WagnerLabPapers/Waskom_JNeurosci_2014","Analysis repository for Waskom et al., (2014) J Neurosci",18,4,"Python","2013-12-13","2014-08-06",0,[],"WagnerLabPapers"],["bdsp-core/icare-dl","Predicting Neurological Outcome in Comatose Patients after Cardiac Arrest with Deep Neural Networks",17,6,"MATLAB","2021-03-12","2025-06-04",0,[],"bdsp-core"],["CliffS/coinex.com","API Interface for coinex.com Bitcoin Cash exchange.",17,7,"CoffeeScript","2018-01-13","2023-12-13",1,[],"CliffS"],["computational-imaging/spad_pileup","",17,3,"MATLAB","2018-11-10","2019-02-26",0,[],"computational-imaging"],["cvxgrp/l1_ls","This is the repository for the l1_ls, a simple Matlab solver for l1-regularized least squares problems.",17,15,"Matlab","2015-03-22","2015-03-22",0,[],"cvxgrp"],["DavisPL/cargo-sherlock","Cargo Sherlock 🕵️ is an automated reasoning tool for quantify trust in Rust software.",17,1,"Rust","2024-06-22","2026-09-04",0,[],"DavisPL"],["ermongroup/Neural-PDE-Solver","",17,2,"Python","2020-05-21","2020-05-21",0,[],"ermongroup"],["ermongroup/SPN_Variational_Inference","PyTorch implementation for \"Probabilistic Circuits for Variational Inference in Discrete Graphical Models\", NeurIPS 2020",17,2,"Python","2020-10-22","2021-10-11",0,["approximate-inference","graphical-models","probabilistic-circuits","pytorch"],"ermongroup"],["HazyResearch/augmentation_code","Reproducible code for Augmentation paper",17,5,"Python","2018-03-16","2019-01-23",0,[],"HazyResearch"],["HazyResearch/sampler","DimmWitted Gibbs Sampler in C++ — ⚠️🚧🛑 REPO MOVED TO DEEPDIVE 👉🏿",17,10,"C++","2015-01-11","2017-01-23",0,[],"HazyResearch"],["kundajelab/ataqc","",17,7,"Python","2016-11-18","2019-01-03",0,[],"kundajelab"],["kundajelab/labelshiftexperiments","Label shift experiments",17,3,"Jupyter Notebook","2019-09-21","2020-12-03",0,[],"kundajelab"],["kundajelab/MotifCompendium","",17,3,"HTML","2024-07-10","2026-10-22",0,[],"kundajelab"],["sisl/AutomotiveVisualization.jl","Visualize automotive simulation from AutomotiveSimulator.jl",17,4,"Julia","2020-03-23","2022-01-12",0,[],"sisl"],["sisl/POMDPStressTesting.jl","Adaptive stress testing of black-box systems within POMDPs.jl",17,7,"Julia","2020-02-11","2024-02-06",0,["black-box","pomdps","simulation","solvers"],"sisl"],["sisl/PyroRL","An RL environment made for wildfire evacuation.",17,1,"Python","2023-07-13","2025-04-02",0,[],"sisl"],["sisl/SignalTemporalLogic.jl","Signal temporal logic (STL) formulas and robustness in Julia",17,3,"Julia","2022-01-04","2025-01-17",0,[],"sisl"],["sisl/TaskGraphs.jl","Precedence constrained multi-agent task assignment and path finding",17,4,"Julia","2019-05-18","2023-12-26",0,[],"sisl"],["snap-stanford/F-FADE","",17,6,"Python","2021-01-11","2021-01-12",0,[],"snap-stanford"],["stanford-cs149/biggraphs-ec","Stanford CS149 - Programming Assignment 5 (Extra Credit)",17,47,"C++","2023-11-16","2024-12-06",0,[],"stanford-cs149"],["stanford-futuredata/tasti","Semantic Indexes for Machine Learning-based Queries over Unstructured Data (SIGMOD 2022)",17,5,"Python","2020-08-04","2024-01-17",0,[],"stanford-futuredata"],["stanford-iprl-lab/fact","",17,2,"Python","2026-07-28","2026-10-26",0,[],"stanford-iprl-lab"],["stanford-rc/dell_warranty","CLI tool + REST API to check Dell hardware warranty information",17,5,"Shell","2020-07-03","2026-05-29",0,["api","bash","dell","rest"],"stanford-rc"],["stanford-rc/slurm-spank-lua","Slurm Lua SPANK 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objectives",17,1,"Python","2025-08-11","2026-07-31",0,["food-systems","optimization","sustainability"],"Sustainable-Solutions-Lab"],["TreeHacks/hackpack-web-frontend","Learn how to make a React+Redux app",17,8,"JavaScript","2017-01-04","2020-12-19",0,[],"TreeHacks"],["bil/timestamping","Trusted Timestamping Framework for Scientific Research",16,1,"HTML","2024-11-21","2025-09-23",0,["rfc3161","trusted-timestamping"],"bil"],["CARDAMOM-framework/CARDAMOM_v2.2","",16,6,"C","2021-05-26","2021-06-04",0,[],"CARDAMOM-framework"],["ccrma/webchuck-ide","A Web-Based Programming Sandbox for ChucK",16,6,"TypeScript","2023-04-15","2026-10-25",0,["web-ide","webchuck"],"ccrma"],["cvxgrp/torch_linops","A library to define abstract linear operators, and associated algebra and matrix-free algorithms, that works with pyTorch Tensors.",16,4,"Python","2022-04-27","2026-08-22",0,["linear-algebra","matrix","pytorch"],"cvxgrp"],["DavisPL/lhs","Yet another Rust MIR analysis tool",16,0,"Rust","2024-08-05","2026-09-29",0,[],"DavisPL"],["helena-project/beetle","A BLE/GATT based data and control plane for the Internet of Things.",16,2,"C++","2016-05-26","2017-02-04",0,[],"helena-project"],["SALT-NLP/agent-collusion","Code for the paper \"Emergent Collusion in Long-Horizon LLM Agent Interaction\"",16,4,"Python","2026-09-21","2026-09-24",0,["ai-agents","ai-safety","llm","multi-agent"],"SALT-NLP"],["SALT-NLP/Disfluency-Generation-and-Detection","Code for \"Planning and Generating Natural and Diverse Disfluent Texts as Augmentation for Disfluency Detection\"",16,5,"Python","2020-10-04","2022-04-25",0,["dataaugmentation","disfluency-detection","textgeneration"],"SALT-NLP"],["SALT-NLP/multi-value","Complete set of English dialect transformation rules and evaluation code",16,5,"Python","2022-10-20","2024-06-07",0,[],"SALT-NLP"],["SALT-NLP/PersuationGames","[ACL2023] Source codes for the paper \"Werewolf Among Us: Multimodal Resources for Modeling Persuasion Behaviors in Social Deduction Games\"",16,5,"Python","2023-05-24","2025-02-22",0,[],"SALT-NLP"],["sisl/AutonomousRiskFramework.jl","Framework for autonomous vehicle risk assessment",16,4,"Julia","2020-09-18","2023-05-11",0,[],"sisl"],["sisl/DecNashPlanning","",16,2,"Python","2021-06-11","2022-04-06",0,[],"sisl"],["sisl/DreamrHHP","",16,1,"Julia","2019-02-04","2019-10-22",0,[],"sisl"],["sisl/EmergencyBrakingSystem.jl","Autonomous emergency braking system for pedestrian avoidance",16,6,"Julia","2018-10-26","2020-04-03",0,[],"sisl"],["snap-stanford/MAG","Programs for Microsoft Academic Graph",16,5,"Python","2016-04-01","2016-06-08",0,[],"snap-stanford"],["snap-stanford/MetroMaps","MetroMaps Release",16,6,"Python","2014-01-10","2014-05-08",0,[],"snap-stanford"],["stanford-cs336/spring2024-assignment5-alignment","",16,19,"Python","2024-05-30","2024-06-12",0,[],"stanford-cs336"],["stanford-futuredata/pytorch-distributed","Fork of diux-dev/imagenet18",16,5,"Python","2018-09-17","2018-10-04",0,[],"stanford-futuredata"],["Stanford-ILIAD/Diverse-Conventions","Exploring techniques to generate diverse conventions in multi-agent settings",16,1,"JavaScript","2021-10-24","2023-11-14",0,["cooperative-ai","marl","overcooked"],"Stanford-ILIAD"],["Stanford-Online/js-input-samples","JS Input samples for using with edX platform",16,19,"JavaScript","2014-06-10","2023-02-03",0,[],"Stanford-Online"],["stanford-web-security/assign1","CS 253 Assignment 1 – Journey to the Dark Side 🌘",16,20,"JavaScript","2019-09-15","2021-10-18",0,[],"stanford-web-security"],["StanfordASL/pmpc","High-level Python Particle Sequential Convex Programming Model Predictive Control (SCP PMPC) interface",16,0,"Jupyter Notebook","2021-08-20","2023-10-30",0,[],"StanfordASL"],["StanfordASL/stlhj","Code for “Signal Temporal Logic meets Hamilton-Jacobi Reachability: Connections and Applications” by Mo Chen, Qizhan Tam, Scott C. Livingston, and Marco Pavone.",16,7,"C++","2018-03-15","2018-09-19",0,[],"StanfordASL"],["StanfordLegion/realm","Realm runtime system",16,20,"C++","2025-06-18","2026-10-26",0,[],"StanfordLegion"],["StanfordMSL/SACBP.jl","Stochastic Sequential Action Control for Continuous-Time Belief Space Planning in Julia",16,2,"Julia","2020-05-22","2022-06-22",0,[],"StanfordMSL"],["stanfordroboticsclub/PupperKeyboardController","Pygame-based keyboard controller for Stanford Pupper",16,9,"Python","2020-07-03","2020-10-30",0,[],"stanfordroboticsclub"],["StanfordVL/keto","",16,0,"Python","2019-07-12","2023-01-13",0,[],"StanfordVL"],["tatsu-lab/mlm_inductive_bias","Code Release for \"On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies\"",16,4,"Python","2020-12-30","2021-04-13",0,[],"tatsu-lab"],["zou-group/metatextgrad","metaTextGrad: Automatically optimizing language model optimizers. Published in NeurIPS 2025.",16,2,"Python","2025-11-05","2025-11-05",0,["llm","reasoning","self-improving"],"zou-group"],["ccrma/chunity","ChucK in Unity",15,4,"C++","2022-05-16","2026-03-25",0,[],"ccrma"],["cvxgrp/cvx_opt_risk_neutral","Convex optimization over risk-neutral probabilities.",15,8,"Jupyter Notebook","2020-02-29","2020-04-22",0,[],"cvxgrp"],["cvxgrp/lpcf","Learning parametric convex functions",15,2,"Python","2024-10-14","2026-07-05",0,[],"cvxgrp"],["drorlab/GLOW_IVES","",15,0,"Python","2023-09-19","2023-12-04",0,[],"drorlab"],["eehh-stanford/baydem","Bayesian tools for reconstructing past demography",15,1,"R","2019-07-15","2024-05-27",0,[],"eehh-stanford"],["HazyResearch/bazaar","",15,10,"JavaScript","2015-06-17","2016-12-06",0,[],"HazyResearch"],["HazyResearch/ddbiolib","DeepDive Biomedical Tools",15,4,"Python","2016-03-09","2017-04-03",0,[],"HazyResearch"],["HazyResearch/random_embedding","",15,6,"Python","2019-11-07","2022-06-10",0,[],"HazyResearch"],["HazyResearch/smoothie","",15,5,"Jupyter Notebook","2024-06-18","2024-12-10",0,[],"HazyResearch"],["HazyResearch/snorkel-biocorpus","",15,7,"Python","2016-11-11","2018-06-09",0,[],"HazyResearch"],["hivdb/codfreq","FASTQ-to-CodFreq pipeline for HIV-1 and SARS-CoV-2",15,4,"Python","2018-06-20","2025-08-12",0,["hiv","ngs-pipeline","sars-cov-2"],"hivdb"],["PrecisionNeuroLab/NaviNIBS","Comprehensive software for neuronavigated brain stimulation",15,5,"Python","2022-04-16","2026-10-20",0,[],"PrecisionNeuroLab"],["ProteinDesignLab/SLAE","",15,1,"Python","2026-05-29","2026-06-29",0,[],"ProteinDesignLab"],["sisl/AdversarialDriving.jl","Adversarial driving simulator for testing safety validation algorithms",15,7,"Julia","2019-11-05","2021-06-13",0,[],"sisl"],["sisl/latent_driver","Jointly learning policies and latent representations for driver behavior.",15,4,"Julia","2017-01-12","2017-06-06",0,[],"sisl"],["sisl/POMDPModelChecking.jl","Perform Model Checking and POMDP Planning from LTL specifications using POMDPs.jl",15,3,"Julia","2018-05-11","2024-08-08",0,[],"sisl"],["snap-stanford/snap-dev","SNAP repository for Ringo",15,21,"C++","2013-02-12","2017-07-25",0,[],"snap-stanford"],["som-shahlab/med-nota","",15,3,"Python","2025-03-17","2025-06-11",0,[],"som-shahlab"],["som-shahlab/motor_tutorial","Tutorial repository for the official MOTOR release",15,0,"Jupyter Notebook","2024-04-29","2025-02-22",0,[],"som-shahlab"],["som-shahlab/survlearners","Metalearners for Survival Data",15,1,"R","2022-04-03","2023-07-31",0,["causal-inference","survival-analysis"],"som-shahlab"],["stanford-cs149/asst5-kernels","",15,33,"Python","2025-11-21","2026-03-08",0,[],"stanford-cs149"],["Stanford-ILIAD/Conventions-ModularPolicy","PyTorch implementation for \"On the Critical Role of Conventions in Adaptive Human-AI Collaboration\", ICLR 2021",15,4,"Python","2021-03-08","2021-03-09",0,["collaboration","conventions","human-ai-interaction","modular-networks"],"Stanford-ILIAD"],["Stanford-ILIAD/RL_Routing","Companion code to TRC paper: Daniel A. Lazar, Erdem Bıyık, Dorsa Sadigh, Ramtin Pedarsani. \"Learning how to Dynamically Route Autonomous Vehicles on Shared Road",15,2,"Python","2018-08-13","2021-08-09",0,[],"Stanford-ILIAD"],["stanford-mast/CS349D-miniengine","",15,14,"Python","2026-04-08","2026-05-16",0,[],"stanford-mast"],["stanford-mast/ShEF","Shielded Enclaves for Cloud FPGAs",15,7,"VHDL","2021-11-22","2021-11-24",0,[],"stanford-mast"],["stanford-oval/datatalk","Agentic system for real-world SQL databases",15,5,"Python","2025-03-29","2026-09-21",0,[],"stanford-oval"],["stanford-oval/genie-parser","Neural Network Semantic Parser for Almond",15,5,"Python","2017-07-22","2019-04-11",1,[],"stanford-oval"],["stanford-oval/node-pulseaudio","A fork of https://bitbucket.org/kayo/node-pulseaudio, which was unmantained. Home of pulseaudio2 npm module",15,4,"C++","2019-01-16","2023-03-05",0,[],"stanford-oval"],["stanford-ppl/pir","",15,2,"Scala","2017-09-07","2023-03-24",0,[],"stanford-ppl"],["stanford-rc/slurm-spank-gpu_cmode","Slurm SPANK plugin to let users change GPU compute mode in jobs",15,5,"Lua","2018-01-22","2023-03-04",0,["gpu","lua","plugin","slurm"],"stanford-rc"],["StanfordASL/NASA_ULI_Xplane_Simulator","Dataset and Simulator for the NASA ULI Project",15,11,"Python","2021-03-15","2022-06-07",0,[],"StanfordASL"],["StanfordASL/NHumanModeling","Contains the code for \"Generative Modeling of Multimodal Multi-Human Behavior\" by Boris Ivanovic, Edward Schmerling, Karen Leung, and Marco Pavone.",15,10,"Jupyter Notebook","2018-03-03","2019-02-10",0,["deep-learning","human-robot-interaction","multi-agent-modeling","trajectory-prediction"],"StanfordASL"],["StanfordASL/stochasticSCP","Sequential Convex Programming For Non-Linear Stochastic Optimal Control",15,2,"Julia","2021-01-04","2022-11-19",0,[],"StanfordASL"],["StanfordHCI/invis","A tool for exploring  high-dimensional RNA sequences from in vitro selection",15,6,"JavaScript","2013-09-26","2014-07-29",0,[],"StanfordHCI"],["StanfordMIMI/dspy-helm","Structured Prompts Improve Evaluation of Language Models",15,6,"Python","2025-09-26","2026-06-05",0,["llm-benchmarking","structured-prompting"],"StanfordMIMI"],["stanfordmlgroup/planet-parasol-demo","A tool to explore the effects of stratospheric aerosol injection on the climate",15,1,"Python","2024-06-04","2024-06-04",0,[],"stanfordmlgroup"],["stanfordnlp/huggingface-models","Scripts for pushing models to huggingface repos",15,6,"Python","2021-09-07","2026-09-29",0,[],"stanfordnlp"],["stanfordnmbl/imu-fog-detection","",15,9,"Python","2021-08-18","2022-02-22",0,[],"stanfordnmbl"],["stanfordroboticsclub/PupperCommand","",15,20,"Python","2019-10-20","2020-03-09",0,[],"stanfordroboticsclub"],["StanfordSpezi/SpeziFHIR","The Spezi FHIR Standard & Related Modules",15,3,"Swift","2023-02-16","2026-04-20",0,["fhir","ios","spezi","stanford"],"StanfordSpezi"],["TreeHacks/hackpack-arduino-app","",15,7,"C++","2016-10-27","2019-01-26",0,[],"TreeHacks"],["vistalab/PRFmodel","",15,10,"MATLAB","2019-05-21","2026-05-07",0,[],"vistalab"],["vistalab/vistadisp","",15,16,"Matlab","2013-01-18","2019-09-20",0,[],"vistalab"],["cancersysbio/SCIMET","Spatial Computational Inference of MEtastatic Timing (SCIMET)",14,11,"Python","2019-04-15","2019-05-28",0,[],"cancersysbio"],["cancersysbio/VirtualTumorEvolution","Spatial Simulation of Tumor Growth",14,11,"Python","2016-10-10","2017-02-24",0,[],"cancersysbio"],["CARDAMOM-framework/CARDAMOM","",14,4,"Jupyter Notebook","2020-07-01","2026-10-22",0,[],"CARDAMOM-framework"],["cvxgrp/cvx-finance-examples","",14,4,"Makefile","2024-06-16","2024-09-01",0,[],"cvxgrp"],["cvxgrp/cvxpyrepair","Code for \"Automatic repair of convex optimization problems\".",14,4,"Python","2020-01-28","2020-02-04",0,[],"cvxgrp"],["cvxgrp/robust_bond_portfolio","Robust Bond Portfolio Construction via Convex-Concave Saddle Point Optimization",14,1,"Python","2022-11-30","2024-05-13",0,[],"cvxgrp"],["DavisPL-Teaching/119","Data processing pipelines",14,18,"Python","2024-08-01","2026-10-24",0,[],"DavisPL-Teaching"],["drorlab/tf-singularity","Tensorflow singularity container",14,6,"Shell","2016-11-01","2017-02-21",0,[],"drorlab"],["ermongroup/acl","Code for \"Adversarial Constraint Learning for Structured Prediction\"",14,7,"Python","2018-05-30","2018-05-30",0,[],"ermongroup"],["ermongroup/f-wgan","Code for \"Bridging the Gap between f-GANs and Wasserstein GANs\", ICML 2020",14,5,"Jupyter Notebook","2020-06-16","2020-07-18",0,[],"ermongroup"],["public-affairs-data-journalism/padjo","Padjo.org",14,0,"HTML","2014-10-15","2016-09-08",0,[],"public-affairs-data-journalism"],["SALT-NLP/counseling-feedback","",14,4,"Python","2024-03-12","2025-09-25",0,[],"SALT-NLP"],["SALT-NLP/Persuasion_Strategy_WVAE","",14,1,"Python","2020-12-14","2021-01-08",0,["persuasion","variational-autoencoder","weakly-supervised-learning"],"SALT-NLP"],["sisl/SBO_AIPPMS","Gaussian Process-based Adaptive Informative Path Planning with Multimodal Sensing",14,2,"Julia","2022-09-13","2024-08-08",0,[],"sisl"],["som-shahlab/motor_code_release","",14,1,"Python","2024-03-27","2024-03-27",0,[],"som-shahlab"],["Stanford-Advanced-Fintech-Lab/missing_data_core","This module contains the core code for the missing data imputation proposed in the the paper \"Missing Financial Data\". It is intended for external distribution.",14,11,"Python","2023-01-04","2024-01-20",0,[],"Stanford-Advanced-Fintech-Lab"],["Stanford-AIMI/LieRE","[ICML-2025] We introduce Lie group Relative position Encodings (LieRE) that goes beyond RoPE in supporting n-dimensional inputs.",14,1,"Python","2025-01-22","2025-08-08",0,[],"Stanford-AIMI"],["stanford-cs248/project","Final Project Instructions for Stanford CS248",14,3,"","2019-02-21","2026-03-03",0,[],"stanford-cs248"],["Stanford-ILIAD/DPP-Batch-Active-Learning","Companion code to the preprint: E Bıyık, K Wang, N Anari, D Sadigh, \"Batch Active Learning using Determinantal Point Processes\". arXiv preprint arXiv:1906.07975",14,3,"Jupyter Notebook","2019-06-19","2024-07-25",0,[],"Stanford-ILIAD"],["Stanford-ILIAD/TREX-pytorch","A PyTorch implementation for the paper 'Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations'.",14,2,"Python","2021-07-26","2021-09-22",0,[],"Stanford-ILIAD"],["stanford-iris-lab/segmenting_feats","",14,0,"Python","2022-09-13","2023-11-01",0,[],"stanford-iris-lab"],["stanford-policylab/polling-errors","Replication materials for \"Disentangling Bias and Variance in Election Polls\"",14,4,"R","2018-02-04","2018-02-07",0,[],"stanford-policylab"],["Stanford-TML/cloak","",14,0,"Python","2026-06-09","2026-09-25",0,[],"Stanford-TML"],["Stanford/AWS-SSO","",14,8,"Shell","2015-09-16","2019-05-15",0,[],"Stanford"],["StanfordASL/matsim-AMoD","A fork of MATSIM's DVRP module for rebalancing of self-driving vehicles in an Autonomous Mobility on Demand (AMoD) framework",14,7,"Jupyter Notebook","2017-02-14","2022-01-25",0,[],"StanfordASL"],["StanfordASL/Pi-MPC","Code accompanying \"Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer.\"",14,4,"Jupyter Notebook","2024-03-15","2024-04-03",0,[],"StanfordASL"],["StanfordASL/Principles-of-Robot-Autonomy","Principles of Robot Autonomy",14,7,"TeX","2020-12-15","2021-03-08",0,[],"StanfordASL"],["StanfordASL/UP","Sampling-based Reachability Analysis: A Random Set Theory Approach with Adversarial Sampling",14,3,"Jupyter Notebook","2020-11-19","2021-01-04",0,[],"StanfordASL"],["StanfordBDHG/ChatPPC","AI assistant for Stanford Pediatric Primary Care",14,2,"TypeScript","2025-02-10","2026-04-23",0,[],"StanfordBDHG"],["StanfordHCI/indie-label","End-User Audits: A System Empowering Communities to Lead Large-Scale Investigations of Harmful Algorithmic Behavior",14,6,"Svelte","2022-08-22","2024-06-20",0,[],"StanfordHCI"],["StanfordHCI/SpatioTemporalNarrative","TECH DEMO: This project enables storytelling in the style of the NYT \"Snow Fall: The Avalanche at Tunnel Creek\" article. We visualize and provide interaction al",14,6,"JavaScript","2013-06-26","2016-03-10",0,[],"StanfordHCI"],["stanfordio/publications","Stanford Internet Observatory publications",14,1,"","2020-10-07","2021-12-02",0,[],"stanfordio"],["stanfordmlgroup/CheXseg","Code used in the paper \"CheXseg: Combining Expert Annotations with DNN-generated Saliency Maps for X-ray Segmentation\"",14,3,"Python","2021-04-27","2021-10-14",0,[],"stanfordmlgroup"],["stanfordmlgroup/spf","Official repository for Spatiotemporal Pyramid Flows (CVPR 2026).",14,0,"Python","2025-12-01","2026-06-17",0,[],"stanfordmlgroup"],["stanfordnlp/contract-nli","ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts",14,2,"HTML","2021-09-03","2022-02-11",0,[],"stanfordnlp"],["stanfordnlp/cs224n_gpt","",14,20,"Python","2026-01-27","2026-01-27",0,[],"stanfordnlp"],["stanfordnmbl/mocopaper","Generate the results for the publication on OpenSim Moco.",14,6,"TeX","2019-08-09","2024-06-08",0,[],"stanfordnmbl"],["StanfordVL/causal_induction","Codebase for \"Causal Induction from Visual Observations for Goal-Directed Tasks\"",14,5,"Python","2019-02-19","2020-02-25",0,[],"StanfordVL"],["sul-cidr/dh2018-word-vector-workshops","Workshop materials for our DH2018 workshop on word vectors. Created by Eun Seo Jo, Javier de la Rosa, and Scott Bailey",14,1,"Jupyter Notebook","2018-06-19","2018-07-26",1,[],"sul-cidr"],["SUwonglab/CoupledNMF","Coupled clustering of single cell genomic data",14,1,"Python","2018-08-16","2021-09-21",0,["atac-seq","clustering-methods","non-negative-matrix-factorization","rna-seq"],"SUwonglab"],["TreeHacks/hackpack-researchkit","",14,4,"Swift","2016-11-10","2019-02-08",0,[],"TreeHacks"],["vistalab/teachmri","Routines for teaching MRI principles in Stanford's Psych 204A",14,9,"MATLAB","2018-01-09","2026-08-06",0,[],"vistalab"],["altemoselab/fiberFold","Predict 3D genome structure from Fiber-seq data.",13,1,"Python","2025-02-26","2025-08-05",0,[],"altemoselab"],["biglocalnews/usps","US Addresses Sampler",13,0,"Svelte","2023-01-12","2023-04-11",0,[],"biglocalnews"],["cestastanford/geographypost","Geography of the Post, Cameron Blevins",13,9,"CSS","2013-06-19","2014-10-29",0,[],"cestastanford"],["cvxgrp/lrsm_portfolio","Portfolio Construction using Stratified Models",13,4,"Jupyter Notebook","2021-01-27","2021-03-25",0,[],"cvxgrp"],["cvxgrp/osmm","oracle-structured minimization method",13,2,"Python","2021-03-17","2021-09-01",0,[],"cvxgrp"],["DavisPL/rust-counterexamples","Examples violating memory safety in safe Rust.",13,0,"Rust","2024-02-01","2024-10-05",0,[],"DavisPL"],["ermongroup/HyperSPN","PyTorch implementation for \"HyperSPNs: Compact and Expressive Probabilistic Circuits\", NeurIPS 2021",13,2,"Python","2021-10-05","2021-10-26",0,["density-estimation","probabilistic-circuits","pytorch","regularization"],"ermongroup"],["HazyResearch/Omnivore","Omnivore Optimizer and Distributed CcT",13,3,"C++","2015-11-03","2016-06-17",0,[],"HazyResearch"],["hivdb/sierra-client","HIVdb webservice client",13,5,"Python","2016-09-15","2026-01-22",0,[],"hivdb"],["kundajelab/MPRA-DragoNN","Code accompanying the paper \"Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assa",13,6,"Python","2019-02-25","2026-05-09",0,[],"kundajelab"],["mtzgroup/chemcloud-client","Python client for TeraChem Cloud",13,3,"Python","2021-01-06","2026-07-16",0,["cloud-computing","quantum-chemistry"],"mtzgroup"],["poldracklab/poldracklab-learning","Tutorials for learning imaging analysis (and other things), typically in ipython Notebook or R Markdown",13,4,"Python","2014-11-25","2015-05-23",0,[],"poldracklab"],["sisl/MPHRL","Model Primitive Hierarchical Reinforcement Learning",13,4,"Python","2019-02-06","2022-12-08",0,[],"sisl"],["sisl/MPPITutorial","A codebase for running the MPPI algorithm on OpenAI gym style environments",13,4,"Python","2021-05-02","2021-05-24",0,[],"sisl"],["sisl/Vec.jl","2D and 3D vectors and their operations for Julia",13,2,"Julia","2016-01-25","2025-02-10",0,[],"sisl"],["som-shahlab/tte-pretraining","Code for the paper \"Time-to-event pretraining for 3D medical imaging\"",13,0,"Python","2024-11-14","2025-05-08",0,[],"som-shahlab"],["Stanford-Blockchain-Club/treehouse-sdk","Starter SDK for full-stack EVM applications, built for TreeHacks 2025 Web3 Workshop",13,2,"TypeScript","2025-02-03","2025-02-14",0,[],"Stanford-Blockchain-Club"],["stanford-futuredata/cs245-as1","Student files for CS245 Programming Assignment 1: In-memory data layout",13,49,"Java","2019-04-09","2022-11-16",0,[],"stanford-futuredata"],["Stanford-ILIAD/vla","Code and Experiments for L4DC 2021 Paper: \"Learning Visually Guided Latent Actions for Assistive Teleoperation\"",13,1,"Python","2021-04-27","2021-05-04",0,[],"Stanford-ILIAD"],["stanford-iris-lab/batch-exploration","",13,2,"Python","2020-11-04","2022-04-25",0,[],"stanford-iris-lab"],["stanford-mast/viva-vldb23-artifact","",13,4,"Python","2022-11-16","2022-11-16",0,[],"stanford-mast"],["stanford-ssi/samwise-avionics","Avionics for 2U Rev of Sputnik Satellite",13,0,"HTML","2022-10-19","2026-05-26",0,[],"stanford-ssi"],["StanfordLegion/gasnet","",13,13,"Makefile","2015-12-04","2026-06-02",0,[],"StanfordLegion"],["StanfordMIMI/SSL_IMU","",13,1,"Python","2022-07-27","2024-01-25",0,[],"StanfordMIMI"],["stanfordmlgroup/Auto-Generate-WLs","Code repository supporting the paper \"Auto-Generating Weak Labels for Real & Synthetic Data to Improve Label-Scarce Medical Image Segmentation\" - 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Rank Allocation, and Partitioning in Multilevel Low Rank Matrices",8,3,"Jupyter Notebook","2023-10-28","2025-01-27",0,[],"cvxgrp"],["cvxgrp/pdmcf","",8,1,"Python","2025-01-11","2025-03-26",0,[],"cvxgrp"],["cvxgrp/spcqe","Smooth periodic consistent quantile estimation",8,2,"Jupyter Notebook","2023-07-18","2025-03-14",0,[],"cvxgrp"],["cvxgrp/SURE-CR","Tractable evaluation of Stein's Unbiased Risk Estimator on convexly regularized estimators",8,2,"Python","2022-05-10","2023-04-03",0,[],"cvxgrp"],["eehh-stanford/MinMaj-Adaptation-Diffusion","Agent-based model to evaluate which social factors affect the sustainable evolution of community-based adaptations to climate change",8,0,"Julia","2022-09-23","2024-10-19",0,[],"eehh-stanford"],["EngreitzLab/eQTLEnrichment","",8,3,"R","2021-03-12","2026-01-21",0,[],"EngreitzLab"],["ermongroup/EfficientObjectDetection","PyTorch Implementation of Efficient Object Detection in Large Images",8,3,"Python","2020-02-13","2020-02-13",0,[],"ermongroup"],["fancompute/Isolators_CMT",":performing_arts: Coupled-mode theory for some modulation-based optical isolators",8,3,"Jupyter Notebook","2020-05-27","2020-05-27",0,[],"fancompute"],["Gen-Verse/DFM-Bench","DFM-Bench: Five-level evaluation of cumulative scientific research progress",8,0,"Python","2026-10-19","2026-10-19",0,[],"Gen-Verse"],["HazyResearch/icij-maude","Weakly supervised classification of adverse event reports from the FDA's MAUDE database.",8,3,"Python","2019-11-25","2019-11-25",0,[],"HazyResearch"],["HazyResearch/ivy-tutorial","An Introductory Tutorial  for Ivy",8,1,"Jupyter Notebook","2020-02-18","2020-10-11",0,[],"HazyResearch"],["HazyResearch/liger","Liger: Fusing Weak Supervision and Model Embeddings",8,4,"Python","2022-07-24","2022-08-01",0,[],"HazyResearch"],["HazyResearch/treedlib","",8,8,"Jupyter 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Builds off kundajelab/lsgkm (which has gkmexplain), which in turn builds off Dongwon-Lee/lsgkm (the original lsg",8,3,"C","2020-08-27","2020-12-01",0,["gkm-svm","regression","regulatory-genomics","svr"],"kundajelab"],["kundajelab/tronn","Transcriptional Regulation (Optimized) Neural Nets (TRoNN)",8,1,"Python","2016-12-08","2021-08-04",0,[],"kundajelab"],["MicrosystemsLab/PiezoD","Python design and optimization code for piezoresistors",8,4,"Python","2009-07-03","2026-05-03",0,[],"MicrosystemsLab"],["SALT-NLP/DADA","This is the oficial repository for \"DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules\".",8,0,"Python","2022-12-18","2023-10-11",0,["dialect-adaptation","nlp"],"SALT-NLP"],["SALT-NLP/FormalityStyleTransfer","Code for \"Semi-supervised Formality Style Transfer using Language Model Discriminator and Mutual Information 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(SVs)",8,0,"Python","2023-07-26","2026-01-26",0,[],"sgtc-stanford"],["sisl/CEEM","Certainty-Equivalent Expectation Maximization: a scalable algorithm for system identification of partially observed systems",8,1,"Python","2020-05-25","2023-07-06",0,["machine-learning","partially-observable-environment","robotics","system-identification"],"sisl"],["sisl/DiFS","Diffusion-based Failure Sampling",8,1,"Python","2024-04-25","2024-07-02",0,[],"sisl"],["sisl/rltools","A collection of tools for conducting deep reinforcement learning experiments",8,4,"Python","2016-06-30","2016-10-03",0,[],"sisl"],["sisl/SatelliteTasking.jl","Satellite Satellite Tasking and Planning Tools and Analysis",8,1,"Julia","2019-01-03","2020-02-28",0,[],"sisl"],["snap-stanford/reddit-processing","preprocessing of Reddit data",8,3,"Python","2018-03-15","2018-04-23",0,[],"snap-stanford"],["som-shahlab/ITE-model-selection","",8,0,"Jupyter Notebook","2018-03-02","2018-08-03",0,[],"som-shahlab"],["stanford-cs221/autumn2019-extra","For big files.",8,3,"","2019-09-24","2021-09-14",0,[],"stanford-cs221"],["stanford-cs221/stanford-cs221.github.io","",8,2,"HTML","2019-08-19","2026-09-16",0,[],"stanford-cs221"],["stanford-cs336/spring2025","",8,2,"","2025-02-12","2026-02-12",0,[],"stanford-cs336"],["stanford-futuredata/cs245-as3-public","Durable transactions assignment for CS245",8,24,"Java","2019-05-13","2021-02-18",0,[],"stanford-futuredata"],["stanford-futuredata/training_on_a_dime","Scripts and logs for \"Analysis and Expoitation of Dynamic Pricing in the Public Cloud for ML Training\", which is to appear at DISPA 2020",8,7,"Jupyter Notebook","2020-02-20","2020-07-27",0,[],"stanford-futuredata"],["stanford-futuredata/Willump-Simple","Willump Is a Low-Latency Useful Machine learning Platform.",8,2,"Python","2020-06-05","2020-07-19",0,[],"stanford-futuredata"],["Stanford-ILIAD/MultilaneInteractions","",8,0,"Jupyter 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implementation of the time-varying, charge-aware network flow model for AMoD systems.",8,3,"MATLAB","2018-07-30","2021-01-31",0,[],"StanfordASL"],["StanfordASL/cloud_robotics","",8,7,"Python","2019-02-11","2019-05-20",0,[],"StanfordASL"],["StanfordASL/RandUP","A Simple and Efficient Sampling-based Algorithm for General Reachability Analysis",8,1,"Python","2021-12-10","2021-12-14",0,[],"StanfordASL"],["StanfordASL/Second-OrderSensitivityAnalysisForBilevelOptimization","",8,1,"Python","2021-06-09","2022-02-24",0,[],"StanfordASL"],["StanfordBioinformatics/bigquery-genomics-qc","Genomics QC pipeline",8,3,"Python","2015-05-08","2017-01-27",0,[],"StanfordBioinformatics"],["StanfordGeospatialCenter/arcgis-install-batch","Batch files and supporting documents for installing ArcGIS Desktop and Extensions",8,2,"Batchfile","2015-03-15","2019-08-08",0,[],"StanfordGeospatialCenter"],["StanfordHCI/gabm_interviewer_platform","",8,8,"JavaScript","2024-11-04","2025-02-05",0,[],"StanfordHCI"],["StanfordHCI/ModelSketchBook","",8,0,"Jupyter Notebook","2023-01-20","2023-04-25",0,[],"StanfordHCI"],["stanfordmlgroup/MedAug","Public release for MedAug",8,3,"Jupyter Notebook","2021-09-17","2021-10-08",0,[],"stanfordmlgroup"],["stanfordnlp/handparsed-treebank","Extra hand parsed data for training models",8,1,"Perl","2020-04-14","2026-10-25",0,[],"stanfordnlp"],["stanfordnmbl/knee_OA_staging","",8,5,"Python","2018-05-10","2022-01-24",0,[],"stanfordnmbl"],["StanfordSpezi/SpeziQuestionnaire","Spezi Questionnaire Module",8,0,"Swift","2023-03-06","2026-04-14",0,["fhir","questionnaire","researchkit","spezi"],"StanfordSpezi"],["StanfordVL/bullet3","",8,3,"C++","2020-11-23","2022-02-10",0,[],"StanfordVL"],["sul-dlss/blacklight_heatmaps","Heatmaps for Blacklight",8,3,"Ruby","2016-06-22","2026-10-20",0,["access","gem"],"sul-dlss"],["sul-dlss/gis-robot-suite","Robots for GIS accessioning and delivery",8,4,"XSLT","2014-07-15","2026-10-26",0,["application","infrastructure","robot"],"sul-dlss"],["sul-dlss/library_hours_rails","Library hours management and API",8,1,"Ruby","2015-05-20","2026-10-26",0,["access","application","rails"],"sul-dlss"],["sul-dlss/purl","URL resolver that translates a reference to a digital object in the form of a druid, into a full content representation of that object as available",8,2,"Ruby","2014-07-30","2026-10-26",0,["access","application"],"sul-dlss"],["sul-dlss/sul_pub","SUL system for harvest and managing publications for Stanford CAP, with controlled API access.",8,3,"Ruby","2016-05-25","2026-10-26",0,["application","infrastructure","rails-ui"],"sul-dlss"],["susom/vertx-template","Sample Vert.x web application with a relational database",8,6,"Java","2016-05-06","2024-11-06",0,["open-source"],"susom"],["TheStanfordDaily/stanforddaily-mobile","Breaking news from the Farm right on your phone!",8,11,"JavaScript","2017-08-29","2024-05-15",0,["expo","mobile","news","newspaper"],"TheStanfordDaily"],["thinkSRS/srsgui","Framework to run instrument-controlling Python scripts in GUI",8,1,"Python","2023-02-10","2024-08-31",0,[],"thinkSRS"],["TreeHacks/hackpack-react-native","",8,3,"JavaScript","2023-07-11","2023-08-07",0,[],"TreeHacks"],["WagnerLab/fmri_notes","Notes from Poldrack lab fMRI meeting",8,3,"","2015-01-07","2015-02-22",0,[],"WagnerLab"],["bdsp-core/PAT-PaperAssessmentTool","",7,1,"Python","2026-03-26","2026-06-24",0,[],"bdsp-core"],["biglocalnews/bln-python-client","Python client for the biglocalnews.org API",7,5,"Python","2020-03-05","2026-09-01",0,["api","api-client","data","data-journalism"],"biglocalnews"],["cancersysbio/gastric_organoid_evolution","Deterministic evolution and stringent selection during pre-neoplasia",7,1,"HTML","2020-07-16","2023-06-03",0,["cancer-genomics","gastric","genomics","organoids"],"cancersysbio"],["ccrma/audicle","graphical IDE for ChucK, once upon a time (currently dormant)",7,0,"C++","2012-10-23","2015-11-05",0,[],"ccrma"],["ccrma/sndpeek","sndpeek real-time audio visualizer",7,1,"C","2025-04-01","2025-04-01",0,[],"ccrma"],["cicl-stanford/inference_from_explanation","Materials for the paper \"Inference from Explanation\" by Lara Kirfel, Thomas Icard, and Tobias Gerstenberg",7,3,"","2020-04-17","2021-09-17",0,[],"cicl-stanford"],["computational-imaging/DeepS3PR","Code associated with the paper \"Deep S3PR: Simultaneous Source Separation and Phase Retrieval Using Deep Generative Models\"",7,3,"Python","2020-02-14","2020-02-14",0,[],"computational-imaging"],["crowd-course/interactivelearningsystem","Infrastructure of creating an interactive learning system",7,3,"HTML","2016-03-14","2016-03-30",0,[],"crowd-course"],["cvxgrp/crypto_portfolio","",7,3,"Jupyter Notebook","2024-11-17","2025-02-03",0,[],"cvxgrp"],["cvxgrp/l1_tf","This is the repository for the l1_tf, software for l1 trend filtering.",7,6,"C","2015-03-22","2015-03-22",0,[],"cvxgrp"],["cvxgrp/qss","QSS: Quadratic-Separable Solver",7,2,"Jupyter Notebook","2022-03-17","2026-02-09",0,[],"cvxgrp"],["cvxgrp/sigopt","Solvers for sigmoidal programming problems",7,5,"Python","2013-09-05","2014-11-09",0,[],"cvxgrp"],["datademofun/matplotlibsampler","Datasets and Matplotlib/pandas examples",7,4,"Jupyter Notebook","2016-05-02","2016-05-02",0,[],"datademofun"],["drorlab/gert","",7,0,"Jupyter Notebook","2022-07-30","2022-09-15",0,[],"drorlab"],["eehh-stanford/yada","Yet Another Demographic Analysis package",7,0,"R","2018-05-04","2020-06-15",0,[],"eehh-stanford"],["EngreitzLab/V2G","",7,2,"R","2023-04-28","2025-10-28",0,[],"EngreitzLab"],["ermongroup/streamline-vi-csp","",7,3,"C","2018-10-19","2018-11-27",0,["belief-propagation","constraint-satisfaction-problem","nips-2018","sat-solver"],"ermongroup"],["fancompute/electro-optic-activation","This notebook contains code for the machine learning tasks in the electro-optic activation function paper",7,0,"Jupyter Notebook","2019-07-12","2019-07-29",0,[],"fancompute"],["FordyceLab/geppetto","Python-based control module for microfluidics pneumatics setups",7,0,"Python","2016-08-09","2017-04-22",0,[],"FordyceLab"],["GradientSpaces/FAMOS","[WACV 2027] FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations",7,0,"Python","2026-10-25","2026-10-25",0,[],"GradientSpaces"],["HazyResearch/hyperE","",7,6,"HTML","2018-06-27","2018-07-28",0,[],"HazyResearch"],["HazyResearch/precision-ls","",7,4,"Jupyter Notebook","2025-03-08","2026-03-10",0,[],"HazyResearch"],["helena-project/squall","Low cost 1 inch round BLE sensor tag.",7,1,"Eagle","2014-10-02","2017-09-30",0,[],"helena-project"],["hivdb/nucamino","A nucleotide to amino acid alignment program optimized for virus gene sequences.",7,9,"Go","2016-07-13","2019-01-31",0,[],"hivdb"],["ISET/isetlens","Ray trace lens calculations in Matlab; works with ISETCam",7,3,"MATLAB","2018-03-05","2026-07-05",0,[],"ISET"],["kundajelab/chip-nexus-pipeline","ChIP-nexus pipeline",7,0,"Python","2019-02-05","2020-11-06",0,[],"kundajelab"],["kundajelab/kerasAC","keras accessibility models: code to train, predict, interpret",7,7,"Python","2018-07-10","2022-02-03",0,[],"kundajelab"],["kundajelab/neural_motif_discovery","Framework for interrogating transcription-factor motifs and their syntax/grammars from neural-network interpretations",7,2,"Jupyter Notebook","2019-10-19","2022-04-27",0,[],"kundajelab"],["kundajelab/neuro-variants","This repository contains all scripts for the manuscript \"Mapping the regulatory effects of common and rare non-coding variants across cellular and developmental",7,1,"Jupyter Notebook","2025-02-19","2026-04-27",0,[],"kundajelab"],["lockss/lockss-installer","LOCKSS Installer (LOCKSS 2.x)",7,0,"Shell","2019-01-18","2026-10-16",0,["laaws","lockss"],"lockss"],["mcnablab/NIFTI_toolbox","MATLAB nifti toolbox (orig. from https://www.mathworks.com/matlabcentral/fileexchange/8797-tools-for-nifti-and-analyze-image) with a couple wrappers",7,6,"MATLAB","2019-03-19","2019-03-19",0,[],"mcnablab"],["Mobisocial/Musubi-iOS-OSS","Musubi iOS Open Source",7,2,"C","2012-09-09","2012-09-21",0,[],"Mobisocial"],["ncsys-lab/FUSE","",7,0,"Python","2024-12-03","2025-01-21",0,[],"ncsys-lab"],["SALT-NLP/DAMP","Baselines for TOP V2, MTOP, Hinglish TOP and CSTOP",7,1,"Python","2022-11-17","2022-12-18",0,[],"SALT-NLP"],["SALT-NLP/SUBS","",7,0,"Python","2022-05-03","2022-09-09",0,[],"SALT-NLP"],["scsnl/YZ_HCP_DNN_Gender_2023","",7,0,"Python","2023-02-13","2024-02-20",0,[],"scsnl"],["sisl/AutoUrban.jl","Additional packages for AutomotiveDrivingModels.jl in urban driving simulation",7,4,"Julia","2017-06-01","2021-05-28",0,[],"sisl"],["sisl/CrossEntropyMethod.jl","An implementation of the cross entropy method that works well for time series",7,3,"Julia","2020-05-31","2022-01-13",0,[],"sisl"],["sisl/GBDTs.jl","Grammar-Based Decision Trees is a machine learning model for the interpretable classification and categorization of data.",7,1,"Julia","2017-10-26","2022-08-14",0,[],"sisl"],["sisl/PerceptionAdaptiveStressTesting","",7,1,"Python","2021-04-16","2022-03-29",0,[],"sisl"],["sisl/ProbabilisticGraphicalModels.jl","A Julia interface for Probabilistic Graphical Models",7,1,"Julia","2017-02-08","2017-07-19",0,[],"sisl"],["sisl/VerifyGAN","Companion code for \"Verification of Image-based Controllers Using Generative Models\"",7,1,"Julia","2021-05-10","2021-05-11",0,[],"sisl"],["sisl/zero_order_rnn","This is the official repo of \"Scaling Recurrent Neural Networks to a Billion Parameters with Zero-Order Optimization\"",7,4,"Python","2025-05-23","2025-12-26",0,[],"sisl"],["snap-stanford/news-search","search Internet news archive",7,5,"Java","2014-09-11","2018-12-23",0,[],"snap-stanford"],["stanford-cnjc/cnjc-code","Code for and from meetings of the Stanford CNJC",7,1,"Jupyter Notebook","2019-01-12","2023-02-02",0,[],"stanford-cnjc"],["stanford-cnjc/cnjcx-course-materials","Public course materials for CNJCx: Practical Python",7,3,"Jupyter Notebook","2020-08-15","2022-03-08",0,[],"stanford-cnjc"],["stanford-crfm/data-overlap","",7,1,"Python","2023-12-23","2024-03-27",0,[],"stanford-crfm"],["stanford-crfm/lm-evaluation-harness","Fork of lm-evaluation-harness",7,1,"Python","2025-02-04","2026-02-05",0,[],"stanford-crfm"],["stanford-cs329a/cs329a-homework2-fall2025-public","",7,9,"Python","2025-10-11","2025-10-19",0,[],"stanford-cs329a"],["stanford-cs336/spring2024-assignment1-basics-leaderboard","",7,25,"","2024-04-02","2024-04-27",0,[],"stanford-cs336"],["stanford-cs336/spring2024-assignment3-scaling","",7,17,"Python","2024-05-01","2024-05-02",0,[],"stanford-cs336"],["stanford-futuredata/supg","",7,4,"Python","2020-05-18","2021-07-29",0,[],"stanford-futuredata"],["stanford-gps-lab/maast_for_araim_2","Evaluates ARAIM performance",7,2,"MATLAB","2024-06-14","2024-07-17",0,[],"stanford-gps-lab"],["Stanford-ILIAD/multimodal-rewards-from-rankings","Companion code to CoRL 2021 paper \"Learning Multimodal Rewards from 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For example, it converts some modern Japanese Kanji characters to the",7,7,"Java","2013-10-07","2026-08-31",0,["access","solr"],"sul-dlss"],["sul-dlss/dor-services-app","A Rails application exposing Digital Object Registry functions as a RESTful HTTP API",7,2,"Ruby","2014-02-07","2026-10-26",0,["application","hacktoberfest","infrastructure","rails-api"],"sul-dlss"],["susom/redcap-em-giftcard-reward","An EM to help automate the reward of gift cards for survey and other participation",7,1,"PHP","2020-05-25","2026-09-14",0,["redcap","redcap-external-module","redcap-repo"],"susom"],["SUwonglab/cRegulon","cRegulon is an optimization model to identify combinatorial regulon from single cell expression and chromatin accessibility data.",7,1,"Python","2023-03-13","2025-09-28",0,["cell-type-annotation","combinatorial-regulation","tf-combination","tf-module"],"SUwonglab"],["SUwonglab/ProVADA","ProVADA: conditional variant generation via ensemble-guided test-time steering",7,0,"Python","2025-07-21","2026-10-20",0,[],"SUwonglab"],["TreeHacks/hackpack-android","",7,5,"Java","2016-12-18","2019-01-26",0,[],"TreeHacks"],["yeatmanlab/create-roar-app","Set up a ROAR app by running one command",7,2,"JavaScript","2022-08-29","2025-12-01",0,[],"yeatmanlab"],["bdsp-core/rass_delirium_eeg_prediction","",6,3,"Python","2019-02-25","2019-09-20",0,[],"bdsp-core"],["biglocalnews/california-wildfires","Big Local News: investigation into the costs of wildfires across the U.S. with a focus on California",6,2,"Jupyter Notebook","2019-01-11","2019-12-12",0,[],"biglocalnews"],["biglocalnews/sync-gsa-properties","Demonstration of syncing General Services Administration lease & property data with Big Local News API",6,0,"Jupyter Notebook","2025-02-18","2025-02-19",0,[],"biglocalnews"],["biglocalnews/warn-transformer","Consolidate, enrich and republish the data gathered by warn-scraper",6,5,"Python","2021-07-02","2026-10-26",0,["data-journalism","etl","journalism","labor"],"biglocalnews"],["cicl-stanford/causal_language","This repository contains materials for the paper \"A counterfactual simulation model of causal language\" by Ari Beller and Tobias Gerstenberg.",6,3,"JavaScript","2023-04-25","2025-10-04",0,[],"cicl-stanford"],["cicl-stanford/mental_jenga","Materials of the paper \"Mental Jenga - A counterfactual simulation model of physical support\" by Liang Zhou, Kevin A. 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