Chandan Singh | chandan singh

Research

Some recent work I'm excited about! I put a lot of my code into the imodels and imodelsX packages and have a soft spot for decision trees and simple things that work.

🔎 Interpretability methods, especially LLM interpretability.

Augmented imodels - build a transparent model using LLMs
Agentic imodels - evolve agent-facing interpretability tools via autoresearch
Attention steering - mechanistically guide LLMs by emphasizing specific input spans
Explanation penalization - regularize explanations to align models with prior knowledge

💊 Clinical decision rules, can we improve them with data?

Human-AI co-design of clinical models - build clinical rules by using LLMs to analyze EHR, vetted by clinicians
Greedy tree sums - build accurate, compact tree-based clinical models
Clinical self-verification - improve LLM-based clinical information extraction with self-verirication
Clinical rule bias assessment - evaluate biases in the development of popular clinical decision instruments

🧠 Semantic brain mapping, mostly using fMRI responses to language.

Generative causal testing - causally test fMRI explanations with LLM-generated stimuli
QA encoding models - model fMRI language responses to verbal theories using LLM annotations
Summarize & score explanations - generate natural-language explanations of fMRI encoding models

Year	Title	Authors	Tags	Paper
'26	Agentic-imodels: evolving agentic interpretability tools via autoresearch	Singh, Tan, Xu, Gero, Yang, Galley, & Gao	🔎🌀	arXiv
'26	Generative causal testing	Antonello, Singh, Jain, Hsu, Gao, Yu, & Huth	🧠🔎🌀	Nature Neuroscience
'26	Sanity checks for agentic data science	Rewolinski, Zane, Huang, Singh, Wang, Gao, & Yu	🔎🌀	arXiv
'26	Test-time learning with an evolving library	Xu, Sordoni, Singh, Gero, Galley, Yuan, & Gao	🌀	arXiv
'26	Test-time recursive thinking	Zhuang, Singh, Liu, Shen, Zhang, Shang, Gao, & Chen	🌀	arXiv
'26	Selecting feature interactions by distilling foundation models	Jia, Singh, Carauana, & Lengerich	🔎🌀	arXiv
'26	Human-AI co-design for clinical prediction models	Feng, Kothari, Vossler, Bishara, Zier, Addo, Kornblith, Tan, & Singh	💊🌀	npj Digital Medicine
'26	Do explanations generalize across large reasoning models?	Pal, Bau, & Singh	💊🌀	arXiv
'26	Interpreting and steering state-space models via activation subspace bottlenecks	Mohan, Gupta, Das, & Singh	🔎🌀	ICML
'25	Evaluating scientific theories as predictive models in language neuroscience	Singh, Antonello, Guo, Mischler, Gao, Mesgarani, & Huth	🧠🔎🌀	bioRxiv
'25	Mixture of inputs	Zhuang, Liu, Singh, Shang, & Gao	🌀	NeurIPS
'25	Interpretable language modeling via induction-head ngram models	Kim, Mantena, Yang, Singh, Yoon, & Gao	🧠🔎🌀	NeurIPS
'25	Bayesian concept bottleneck models with LLM priors	Feng, Kothari, Zier, Singh, & Tan	🔎🌀	NeurIPS
'25	OmniGuard	Verma, Hines, Bilmes, Siska, Zettlemoyer, Gonen, & Singh	🌀	EMNLP
'25	Systematic bias in clinical decision instrument development	Obra, Singh, et al.	🔎💊	npj Digital Medicine
'25	Analyzing patient perspectives with LLMs	Kornblith, Singh et al.	💊🌀	Nature Scientific Reports
'25	SimDINO	Wu et al.	🌀	ICML
'25	Vector-ICL: in-context learning with continuous vector representations	Zhuang et al.	🔎🌀	ICLR
'24	Crafting interpretable embeddings by asking LLMs questions	Benara, Singh, Morris, Antonello, Stoica, Huth, & Gao	🧠🔎🌀	NeurIPS
'25	Towards understanding graphical perception in large multimodal models	Zhang et al.	🌀	arXiv
'24	Rethinking interpretability in the era of large language models	Singh, Inala, Galley, Caruana, & Gao	🔎🌀	arXiv
'24	Towards consistent natural-language explanations via explanation-consistency finetuning	Chen et al.	🔎🌀	COLING
'24	Learning a decision tree algorithm with Transformers	Zhuang et al.	🔎🌀🌳	TMLR
'24	Model tells itself where to attend: faithfulness meets automatic attention steering	Zhang, Yu, et al.	🔎🌀	arXiv
'24	Tell your model where to attend: post-hoc attention steering for LLMs	Zhang et al.	🔎🌀	ICLR
'24	Attribute structuring improves LLM-based evaluation of clinical text summaries	Gero et al.	🔎🌀	ML4H findings
'23	Tree prompting	Morris, Singh, Rush, Gao, & Deng	🔎🌀🌳	EMNLP
'23	Augmenting interpretable models with LLMs during training	Singh, Askari, Caruana, & Gao	🔎🌀🌳	Nature Communications
'23	Explaining black box text modules in natural language with language models	Singh, Hsu, Antonello, Jain, Huth, Yu & Gao	🔎🌀	NeurIPS workshop
'23	Self-verification improves few-shot clinical information extraction	Gero, Singh, Cheng, Naumann, Galley, Gao, & Poon	🔎🌀💊	ICML workshop
'22	Explaining patterns in data with language models via interpretable autoprompting	Singh, Morris, Aneja, Rush, & Gao	🔎🌀	EMNLP workshop
'22	Stress testing a clinical decision instrument performance for intra-abdominal injury	Kornblith, Singh et al.	🔎🌳💊	PLOS Digital Health
'22	Fast interpretable greedy-tree sums (FIGS)	Tan, Singh, Nasseri, Agarwal, & Yu	🔎🌳	PNAS
'22	Hierarchical shrinkage for trees	Agarwal, Tan, Ronen, Singh, & Yu	🔎🌳	ICML (spotlight)
'22	VeridicalFlow: a python package for building trustworthy data science pipelines with PCS	Duncan, Kapoor, Agarwal, Singh, & Yu	💻🔍	JOSS
'21	imodels: a python package for fitting interpretable models	Singh, Nasseri, et al.	💻🔍🌳	JOSS
'21	Adaptive wavelet distillation from neural networks through interpretations	Ha, Singh, et al.	🔍🌀🌳	NeurIPS
'21	Matched sample selection with GANs for mitigating attribute confounding	Singh, Balakrishnan, & Perona	🌀	CVPR workshop
'21	Revisiting complexity and the bias-variance tradeoff	Dwivedi, Singh, Yu & Wainwright	🌀	JMLR
'20	Curating a COVID-19 data repository and forecasting county-level death counts in the United States	Altieri et al.	🔎🦠	HDSR
'20	Transformation importance with applications to cosmology	Singh, Ha, Lanusse, Boehm, Liu & Yu	🔎🌀🌌	ICLR workshop (spotlight)
'20	Interpretations are useful: penalizing explanations to align neural networks with prior knowledge	Rieger, Singh, Murdoch & Yu	🔎🌀	ICML
'19	Hierarchical interpretations for neural network predictions	Singh, Murdoch, & Yu	🔍🌀	ICLR
'19	Interpretable machine learning: definitions, methods, and applications	Murdoch, Singh, et al.	🔍🌳🌀	PNAS
'19	Disentangled attribution curves for interpreting random forests and boosted trees	Devlin, Singh, Murdoch & Yu	🔍🌳	arXiv
'18	Large scale image segmentation with structured loss based deep learning for connectome reconstruction	Funke, Tschopp, et al.	🧠🌀	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	🧠	NeurIPS Workshop

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