Hi there 👋 . I'm Chandan, a PhD candidate at UC Berkeley working on interpretable machine learning.
🤖 I make general-purpose AI packages and cheatsheets
Interpretable and accurate predictive modeling, sklearn-compatible (JOSS 2021)

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

Making it easier to build stable, trustworthy data-science pipelines
🧠 Some of my research focuses on interpreting neural networks
hierarchical-dnn-interpretations
Hierarchical interpretations for neural network predictions (ICLR 2019)
deep-explanation-penalization
Interpretations are useful: penalizing explanations to align neural networks with prior knowledge (ICML 2020)
adaptive-wavelets
Adaptive, interpretable wavelets across domains (NeurIPS 2021)
transformation-importance
Transformation Importance with Applications to Cosmology (ICLR Workshop 2020)
📊 My research also focuses on implactful applied data-science problems
covid19-severity-prediction
Extensive and accessible COVID-19 data + forecasting for counties and hospitals (HDSR 2021)
rule-vetting
General pipeline for deriving clinical decision rules
iai-clinical-decision-rule
Interpretable clinical decision rules for predicting intra-abdominal injury
molecular-partner-prediction
Predicting successful CME events using only clathrin markers
And I also explore 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)
data-viz-utils
Functions for easily making publication-quality figures with matplotlib
matching-with-gans
Matching in GAN latent space for better bias benchmarking. (CVPR workshop 2021)
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
Mini-projects
hummingbird-tracking, imodels-experiments, nano-descriptions, news-title-bias, java-mini-games, news-balancer, arxiv-copier, dnn-experiments, max-activation-interpretation-pytorch, hpa-interp, sensible-local-interpretations, global-sports-analysis, mouse-brain-decoding, ...