phd candidate at berkeley working (with the amazing prof. bin yu) on interpretable machine learning across domains 🧠💊️🦠

if you want to chat about research ideas or how to use data-science for good, drop me an e-mail!


year title authors tags paper code misc
'22 VeridicalFlow: a Python package for building trustworthy data science pipelines with PCS duncan*, kapoor*, agarwal*, singh*, & yu 🔎 ml joss
'21 Adaptive wavelet distillation from neural networks through interpretations ha, singh, et al. 🔎 ml neurips
'21 imodels: a python package for fitting interpretable models singh*, nasseri*, et al. 🔎 ml joss
'21 Matched sample selection with GANs for mitigating attribute confounding singh, balakrishnan, & perona ml cvpr workshop
'21 Revisiting complexity and the bias-variance tradeoff dwivedi*, singh*, yu & wainwright ml topml workshop
'21 developing reliable clinical decision rules: a case study in identifying blunt abdominal trauma in children kornblith*, singh* et al. 📊 🔎 💊 SAEM abstract
'21 Interpretable deep learning for accurate molecular partner prediction in clathrin-mediated endocytosis singh*, li*, et al. 📊 🦠 in prep
'20 Curating a COVID-19 data repository and forecasting county-level death counts in the United States altieri et al. 📊 🦠 hdsr
'20 interpretations are useful: penalizing explanations to align neural networks with prior knowledge rieger, singh, murdoch & yu 🔎 ml icml
'20 transformation importance with applications to cosmology singh*, ha*, lanusse, boehm, liu & yu 🔎 🌌 ml iclr workshop (spotlight)
'19 disentangled attribution curves for interpreting random forests and boosted trees devlin, singh, murdoch & yu 🔎 ml arxiv
'19 interpretable machine learning: definitions, methods, and applications Murdoch*, Singh*, et al. 🔎 ml pnas
'19 hierarchical interpretations for neural network predictions Singh*, Murdoch*, & Yu 🔎 ml ICLR
'18 large scale image segmentation with structured loss based deep learning for connectome reconstruction Funke*, Tschopp*, et al. ml 🧠 TPAMI
'18 linearization of excitatory synaptic integration at no extra cost Morel, Singh, & Levy 🧠 J Comp Neuro
'17 a consensus layer V pyramidal neuron can sustain interpulse-interval coding Singh & Levy 🧠 Plos One
'17 a constrained, weighted-l1 minimization approach for joint discovery of heterogeneous neural connectivity graphs Singh, Wang, & Qi ml 🧠 neurips Workshop ,

resources + posts

Some posts in ml research and organizing different papers.

Resources including teaching slides, research overview slides, and coding projects.

A rough set of notes which may serve as useful reference for people in machine learning / neuroscience.


I've been lucky to be advised by / collaborate with many amazing people

It has been my pleasure to help advise some incredible undergrads at berkeley -- check them out!