About

I am an Assistant Professor at the Courant Institute at NYU in Computer Science and at the Center for Data Science. I am also part of the CILVR group. My research interests include causal, statistical, and probabilistic inference, out-of-distribution detection and generalization, deep generative modeling, interpretability, and machine learning for healthcare. Before joining NYU, I earned degrees in computer science; my PhD was completed at Princeton University working with Dave Blei and my undergraduate was done at Stanford University. I have also spent time as a research affiliate at MIT’s Institute for Medical Engineering and Science.

Papers

Survival Mixture Density Networks
Xintian Han, Mark Goldstein, and Rajesh Ranganath. MLHC 2022.
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Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets
Lily Zhang, Veronica Tozzo, John Higgins, Rajesh Ranganath. ICML 2022.
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Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations
Aahlad Puli, Lily Zhang, Eric Oermann, Rajesh Ranganath. ICLR 2022.
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FastSHAP: Real-Time Shapley Value Estimation
Neil Jethani, Mukund Sudarshan, Ian C Covert, Su-In Lee, R Ranganath. ICLR 2022.
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Learning invariant representations with missing data
Mark Goldstein, Jörn Jacobsen, Olina Chau, Adriel Saporta, Aahlad Puli, Rajesh Ranganath, Andy Miller. CLeaR 2022.
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Individual treatment effect estimation in the presence of unobserved confounding using proxies: a cohort study in stage III non-small cell lung cancer
Wouter AC van Amsterdam, Joost Verhoeff, Netanja I Harlianto, Gijs A Bartholomeus, Aahlad Manas Puli, Pim A de Jong, Tim Leiner, Anne SR van Lindert, Marinus JC Eijkemans, and Rajesh Ranganath. Scientific reports 12 (1), 2022.
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Inverse-Weighted Survival Games
Xintian Han, Mark Goldstein, Aahlad Puli, Thomas Wies, Adler Perotte, and Rajesh Ranganath. NeurIPS 2021.
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Offline rl without off-policy evaluation
David Brandfonbrener, Will Whitney, Rajesh Ranganath, and Joan Bruna. NeurIPS, 2021.
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Probabilistic machine learning for healthcare
Irene Y Chen, Shalmali Joshi, Marzyeh Ghassemi, Rajesh Ranganath. Annual Review of Biomedical Data Science 4, 2021.
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RankFromSets: Scalable set recommendation with optimal recall
Jaan Altosaar, Rajesh Ranganath, Wesley Tansey. Stat 10 (1), 2021.
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Understanding failures in out-of-distribution detection with deep generative models
Lily Zhang, Mark Goldstein, Rajesh Ranganath. ICML 2021.
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Offline contextual bandits with overparameterized models
David Brandfonbrener, William Whitney, Rajesh Ranganath, Joan Bruna. ICML 2021.
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Reproducibility in machine learning for health research: Still a ways to go
Matthew BA McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Luca Foschini, Marzyeh Ghassemi. Science Translational Medicine 13 (586), 2021.
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Contra: Contrarian statistics for controlled variable selection
Mukund Sudarshan, Aahlad Puli, Lakshmi Subramanian, Sriram Sankararaman, and Rajesh Ranganath. AISTATS, 2021.
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Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations
Neil Jethani, Mukund Sudarshan, Yindalon Aphinyanaphongs, Rajesh Ranganath. AISTATS, 2021.
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A validated, real-time prediction model for favorable outcomes in hospitalized COVID-19 patients
Narges Razavian, Vincent J Major, Mukund Sudarshan, … Rajesh Ranganath, Jonathan Austrian, Yindalon Aphinyanaphongs. NPJ digital medicine 3 (1), 1-13 44, 2020.
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The Counterfactual χ-GAN: Finding comparable cohorts in observational health data
Amelia J Averitt, Natnicha Vanitchanant, Rajesh Ranganath, Adler J Perotte. Journal of Biomedical Informatics 109, 2020.
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Data-driven physiologic thresholds for iron deficiency associated with hematologic decline
Brody H Foy, Aodong Li, James P McClung, Rajesh Ranganath, John M Higgins. American Journal of Hematology 95 (3), 2020.
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Deep learning models for electrocardiograms are susceptible to adversarial attack
Xintian Han, Yuxuan Hu, Luca Foschini, Larry Chinitz, Lior Jankelson, Rajesh Ranganath. Nature medicine 26 (3), 2020.
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General Control Functions for Causal Effect Estimation from IVs
Aahlad Puli, Rajesh Ranganath. NeurIPS 2020.
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Causal Estimation with Functional Confounders
Aahlad Puli, Adler Perotte, Rajesh Ranganath. NeurIPS 2020.
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X-CAL: Explicit Calibration for Survival Analysis
Mark Goldstein, Xintian Han, Aahlad Puli, Adler Perotte, Rajesh Ranganath. NeurIPS 2020.
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Deep Direct Likelihood Knockoffs
Mukund Sudarshan, Wesley Tansey, Rajesh Ranganath. NeurIPS 2020.
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A Review of Challenges and Opportunities in Machine Learning for Health
Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L Beam, Irene Y Chen, Rajesh Ranganath. AMIA Summits on Translational Science Proceedings 2020.
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Practical Guidance on Artificial Intelligence for Healthcare Data
Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L Beam, Irene Y Chen, and Rajesh Ranganath. The Lancet Digital Health 1 (4), 2019.
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ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
Kexin Huang, Jaan Altosaar, Rajesh Ranganath. CHIL workshop 2020.
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Kernelized complete conditional Stein discrepancy
Raghav Singhal, Xintian Han, Saad Lahlou, Rajesh Ranganath. arXiv 2019.
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Energy-Inspired Models: Learning with Sampler-Induced Distributions
Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath. Neurips 2019.
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Predicate Exchange: Inference with Declarative Knowledge
Zenna Tavares, Javier Burroni, Edgar Minasyan, Amando Solar-Lezama, Rajesh Ranganath. ICML 2019.
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The Variational Predictive Natural Gradient
Da Tang, Rajesh Ranganath. ICML 2019.
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Support and Invertibility in Domain-Invariant Representations
Fredrik Johansson, David Sontag, Rajesh Ranganath. AISTATS 2019.
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Deep Survival Analysis: Missingness and Nonparametrics
Xenia Miscouridou, Adler Perotte, Noemie Elhadad, Rajesh Ranganath. MLHC 2018.
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⌄ Expand ⌄

Max-margin Learning with the Bayes factor
Rahul Krishnan, Arjun Khandelwal, Rajesh Ranganath, David Sontag. UAI 2018.
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NOISIN: Unbiased Regularization for Recurrent Neural Networks
Adji Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei. ICML 2018.
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Variational Sequential Monte Carlo
Christian Naesseth, Scott Linderman, Rajesh Ranganath, David M. Blei. AISTATS 2018.
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Proximity Variational Inference
Jaan Altosaar, Rajesh Ranganath, David M. Blei. AISTATS 2018.
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Identifying potentially induced seismicity and assessing statistical significance in Oklahoma and California
Mark McClure, Riley Gibson, Kit‐Kwan Chiu, and Rajesh Ranganath. Journal of Geophysical Research: Solid Earth 2017.
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Variational Inference via Chi Upper Bound Minimization
Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John Paisley, and David M. Blei. Neurips 2017.
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Hierarchical Implicit Models and Likelihood-Free Variational Inference
Dustin Tran, Rajesh Ranganath, and David M. Blei. Neurips 2017.
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Correlated Random Measures
Rajesh Ranganath and David M. Blei. JASA 2017.
[fulltext] [arxiv]
Automatic Differentiation Variational Inference
Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, and David M. Blei. JMLR 2017.
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Operator Variational Inference
Rajesh Ranganath, Jaan Altosaar, Dustin Tran, and David M. Blei. Neurips 2016.
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Deep Survival Analysis
Rajesh Ranganath, Adler Perotte, Noemie Elhadad, and David M. Blei. MLHC 2016.
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Hierarchical Variational Models
Rajesh Ranganath, Dustin Tran, and David M. Blei. ICML 2016.
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Variational Tempering
Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, and David M. Blei. AISTATS 2016.
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The Variational Gaussian Process
Dustin Tran, Rajesh Ranganath, and David M. Blei. ICLR 2016.
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The Population Posterior and Bayesian Modeling on Streams
James McInerney, Rajesh Ranganath, and David M. Blei. Neurips 2015.
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Automatic Variational Inference in Stan
Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, and David M. Blei. Neurips 2015.
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Dynamic Poisson Factorization
Laurent Charlin, Rajesh Ranganath, James McInerney, and David M. Blei. RecSys 2015.
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The Survival Filter: Joint Survival Analysis with a Latent Time Series
Rajesh Ranganath, Adler Perotte, Noemie Elhadad, and David M. Blei. UAI 2015.
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Risk Prediction for Chronic Kidney Disease Progression Using Heterogeneous Electronic Health Record Data and Time Series Analysis
Adler Perotte, Rajesh Ranganath, Jamie Hirsch, David M. Blei, and Noemie Elhadad. JAMIA 2015.
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Deep Exponential Families
Rajesh Ranganath, Linpeng Tang, Laurent Charlin, and David M.Blei. AISTATS 2015.
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Hierarchical Topographic Factor Analysis
Jeremy R. Manning, Rajesh Ranganath, Waitsang Keung, Nicholas B. Turk-Browne, Jonathan D. Cohen, Kenneth A. Norman, and David M. Blei. IEEE Xplore, 4th International Workshop on Pattern Recognition in Neuroimaging.
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Bayesian nonparametric Poisson factorization for recommendation systems
Prem Gopalan, Francisco JR Ruiz, Rajesh Ranganath, and David M. Blei. AISTATS 2014.
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Black Box Variational Inference
Rajesh Ranganath, Sean Gerrish, and David M. Blei. AISTATS 2014.
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Topographic Factor Analysis: a Bayesian model for inferring brain networks from neural data
Jeremy R. Manning, Rajesh Ranganath, Kenneth A. Norman, and David M. Blei. Plos One 9(5) 2014.
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An Adaptive Learning Rate for Stochastic Variational Inference
Rajesh Ranganath, Chong Wang, David M. Blei, and Eric P. Xing. ICML 2013.
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Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates
Rajesh Ranganath, Dan Jurafsky, and Daniel A. McFarland. Computer Speech and Language. vol. 27, no. 1, pp. 89-115, 2013, doi:10.1016/j.csl.2012.01.00.
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Unsupervised Learning of Hierarchical Representations with Convolutional Deep Belief Networks
Honglak Lee, Roger Grosse, Rajesh Ranganath, and Andrew Y. Ng. Communications of the ACM, vol. 54, no. 10, pp. 95-103, 2011. (Research Highlights)
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It’s Not You, it’s Me: Detecting Flirting and its Misperception in Speed-Dates
Rajesh Ranganath, Dan Jurafsky, and Dan McFarland. EMNLP 2009.
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Extracting Social Meaning: Identifying Interactional Style in Spoken Conversation
Dan Jurafsky, Rajesh Ranganath, and Dan McFarland. NAACL HLT 2009.
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Honglak Lee, Roger Grosse, Rajesh Ranganath and Andrew Y. Ng. ICML 2009 (Best paper award: Best application paper)
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