Data

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Open Source Science

https://github.com/csinva/csinva

readme

https://github.com/csinva/csinva

Science Score: 49.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 4 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: arxiv.org, medrxiv.org, scholar.google, nature.com, plos.org, joss.theoj.org
  • ○
    Academic email domains
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    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.2%) to scientific vocabulary
Last synced: 7 months ago · JSON representation

Repository

readme

Basic Info
  • Host: GitHub
  • Owner: csinva
  • Default Branch: main
  • Size: 93.8 KB
Statistics
  • Stars: 1
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 5 years ago · Last pushed 10 months ago
Metadata Files
Readme

README.md 

Hi there 👋 I'm Chandan, a Senior Researcher at Microsoft Research working on interpretable machine learning.
Homepage / Twitter / Google Scholar / LinkedIn

🌳 Interpretable models / dataset explanations

Interpretable and accurate predictive modeling, sklearn-compatible (JOSS 2021). Contains FIGS (PNAS 2022) and HSTree (ICML 2022)

Interpretability for text. Contains Aug-imodels (Nature Communications 2023) , Tree-Prompt (EMNLP 2023) , iPrompt (ICLR workshop 2023) , SASC (NeurIPS workshop 2023) , and QA-Embs (NeurIPS 2024)

adaptive-wavelets Adaptive, interpretable wavelets (NeurIPS 2021)

🤖 General-purpose AI packages and cheatsheets

Notes and resources on AI

Utilities for trustworthy data-science (JOSS 2021)

🧠 Interpreting neural networks

deep-explanation-penalization Penalizing neural-network explanations (ICML 2020)

hierarchical-dnn-interpretations Hierarchical interpretations for neural network predictions (ICLR 2019)

transformation-importance Feature importance for transformations (ICLR Workshop 2020)

📊 Data-science problems

automated-brain-explanations Building natural-language explanations for the brain. Contains GCT (arxiv 2024)

clinical-rule-development Building and vetting clinical decision rules, including vetting an intraabdominal rule (PLOS DH, 2022), analyzing patient perspectives for approving rules (Nature SR, 2025), or analyzing bias across CDIs (medRxiv, 2025). See also general PECARN data preprocessing (clinical-rule-vetting )

covid19-severity-prediction Extensive COVID-19 data + forecasting for counties and hospitals (HDSR 2021)

molecular-partner-prediction Predicting successful CME events using only clathrin markers

Various aspects of deep learning and machine learning

gan-vae-pretrained-pytorch Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch

gpt-paper-title-generator Generating paper titles with GPT-2

disentangled-attribution-curves Attribution curves for interpreting tree ensembles trees (arxiv 2019)

matching-with-gans Matching in GAN latent space for better bias benchmarking. (CVPR workshop 2021)

data-viz-utils Functions for easily making publication-quality figures with matplotlib

mdl-complexity Revisiting complexity and the bias-variance tradeoff (JMLR 2021)

Projects advised

pasta Post-hoc Attention Steering for LLMs (ICLR 2024), led by Qingru Zhang

meta-tree Learning a Decision Tree Algorithm with Transformers (TMLR 2024), led by Yufan Zhuang

sim-dino Simplifying DINO via coding rate regularization (ICML 2025), led by Ziyang Wu

explanation-consistency-finetuning Consistent Natural-Language Explanations (COLING 2025), led by Yanda Chen

induction-gram Interpretable Language Modeling via Induction-head Ngram Models (arXiv 2024), led by Eunji Kim & Sriya Mantena

Open-source contributions

Major: autogluon , big-bench , nl-augmenter

Minor: conference-acceptance-rates , iterative-random-forest , interpretable-ml-book , awesome-interpretable-machine-learning , awesome-machine-learning-interpretability , awesome-llm-interpretability , executable-books , deep-fMRI-dataset

Mini-projects

hummingbird-tracking, imodels-experiments, cookiecutter-ml-research, nano-descriptions, news-title-bias, java-mini-games, imodels-data, news-balancer, arxiv-copier, dnn-experiments, max-activation-interpretation-pytorch, acronym-generator, hpa-interp, sensible-local-interpretations, global-sports-analysis, mouse-brain-decoding, ...

Owner

  • Name: Chandan Singh
  • Login: csinva
  • Kind: user
  • Location: Microsoft research
  • Company: Senior researcher

Senior researcher @Microsoft interpreting ML models in science and medicine. PhD from UC Berkeley.

GitHub Events

Total
  • Push event: 9
Last Year
  • Push event: 9

Issues and Pull Requests

Last synced: about 1 year ago

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Past Year
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