Chandan Singh csinva

Hi there 👋 I'm Chandan, a Senior Researcher at Microsoft Research working on interpretable machine learning.
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🤖 General-purpose AI packages and cheatsheets

Slides, paper notes, class notes, blog posts, and research on ML, stat, and AI

Making it easier to build stable, trustworthy data-science pipelines (JOSS 2021)

🌳 Interpretable models / dataset explanations

Interpretable and accurate predictive modeling, sklearn-compatible (JOSS 2021) -- contains FIGS (arXiv 2022) and HSTree (ICML 2022)

Library to explain a dataset in natural language -- contains Emb-GAM (arXiv 2022) and iPrompt (arXiv 2022)

adaptive-wavelets Adaptive, interpretable wavelets across domains (NeurIPS 2021)

🧠 Interpreting neural networks

deep-explanation-penalization Interpretations are useful: penalizing explanations to align neural networks with prior knowledge (ICML 2020)

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

transformation-importance Transformation Importance with Applications to Cosmology (ICLR Workshop 2020)

📊 Data-science problems

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

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

iai-clinical-decision-rule Interpretable 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

gpt2-paper-title-generator Generating paper titles with GPT-2

disentangled-attribution-curves Disentangled Attribution Curves for Interpreting Random Forests and Boosted 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 (TOPML workshop 2021)

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 , 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, ...

Created 7 commits in 2 repositories

csinva/imodelsX 5 commits

csinva/fmri 2 commits

17 contributions in private repositories Mar 1 – Mar 3

Chandan Singh csinva

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Block or report csinva

🤖 General-purpose AI packages and cheatsheets

🌳 Interpretable models / dataset explanations

🧠 Interpreting neural networks

📊 Data-science problems

Various aspects of deep learning and machine learning

Open-source contributions

Mini-projects

Pinned

1,319 contributions in the last year

Contribution activity

March 2023