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Achievements

Achievement: QuickdrawAchievement: Starstruckx3Achievement: Pull Sharkx2Achievement: Arctic Code Vault Contributor

Organizations

@microsoft @conda-forge @BerkeleyML @Yu-Group @lumosvision @response4life @MicrosoftCopilot

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csinva/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

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

clinical-rule-vetting General pipeline for deriving clinical decision rules

iai-clinical-decision-rule Clinical decision rules for predicting intra-abdominal injury (PLOS Digital Health 2022)

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

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, ...

Pinned Loading

  1. csinva.github.io csinva.github.io Public

    Slides, paper notes, class notes, blog posts, and research on ML 📉, statistics 📊, and AI 🤖.

    HTML 565 107

  2. imodels imodels Public

    Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

    Jupyter Notebook 1.4k 125

  3. hierarchical-dnn-interpretations hierarchical-dnn-interpretations Public

    Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

    Jupyter Notebook 127 22

  4. laura-rieger/deep-explanation-penalization laura-rieger/deep-explanation-penalization Public

    Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

    Jupyter Notebook 128 14

  5. Yu-Group/covid19-severity-prediction Yu-Group/covid19-severity-prediction Public

    Extensive and accessible COVID-19 data + forecasting for counties and hospitals. 📈

    Jupyter Notebook 228 92

  6. imodelsX imodelsX Public

    Scikit-learn friendly library to interpret, and prompt-engineer text datasets using large language models.

    Python 161 26