Publications

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Here’s my google scholar

• Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities. NeurIPS, 2024. Work by Adriel Saporta*, Aahlad Puli, me, and Rajesh Ranganath.

• Scalable Interpolant Transformers. European Conference on Computer Vision (ECCV), 2024. Work by Nanye Willis Ma*, me, Michael Albergo, Nick Boffi, Eric Vanden-Eijnden, and Saining Xie.

• What’s the score? Automated Denoising Score Matching for Nonlinear Diffusions . International Conference on Machine Learning, 2024. Work by Raghav Singhal*, me*, and Rajesh Ranganath.

• Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes. International Conference on Machine Learning, 2024. Work by Yifan Chen*, me*, Mengjian Hua*, Michael S. Albergo, Nicholas M. Boffi, and Eric Vanden-Eijnden.

• Stochastic interpolants with data-dependent couplings. International Conference on Machine Learning, 2024. Work by me*, Michael Albergo*, Nick Boffi, Rajesh Ranganath, and Eric Vanden-Eijnden.

• A dynamic risk score for early prediction of cardiogenic shock using machine learning. European Heart Journal: Acute Cardiovascular Care, March 2024. Also available on arxiv. Work by Yuxuan Hu*, myself, Rajesh Ranganath, and many others.

• QTNet: Predicting Drug-Induced QT Prolongation with Artificial Intelligence-Enabled Electrocardiograms. Journals of the American College of Cardiology, Clinical Electrophysiology. Hao Zhang*, Constantine Tarabanis, Neil Jethani, me, Silas Smith, Larry A. Chinitz, Rajesh Ranganath, Yindalon Aphinyanaphongs, and Lior Jankelson.

• Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions. International Conference on Learning Representations. 2023. Work by myself*, Raghav Singhal*, and Rajesh Ranganath.

• Survival Mixture Density Networks. Machine Learning for Healthcare Conference. PMLR, 2022. Work by Xintian Han*, myself, and Rajesh Ranganath.

• Learning Invariant Representations with Missing Data (full version). CLeaR (Causal Learning and Reasoning) 2022. Work by myself*, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Puli, Rajesh Ranganath, and Andrew C. Miller. Work done as part of my internship at Apple Health AI.

• Learning Invariant Representations with Missing Data. NeurIPS 2021 DistShift Workshop. Work by myself*, Jörn-Henrik Jacobsen, Olina Chau, Adriel Saporta, Aahlad Puli, Rajesh Ranganath, and Andrew C. Miller. Work done as part of my internship at Apple Health AI.

• Inverse-Weighted Survival Games. NeurIPS 2021. Work by myself*, Xintian Han*, Aahlad Puli, Thomas Wies, Adler J. Perotte, and Rajesh Ranganath. A new way to estimate survival models by playing games involving both the failure and censoring distribution!

• Understanding Failures in Out-of-Distribution Detection with Deep Generative Models. International Conference on Machine Learning, 2021. Work by Lily H. Zhang*, myself, and Rajesh Ranganath.

• Understanding Out-of-Distribution Detection with Deep Generative Models. ICLR 2021 RobustML workshop. Work by Lily H. Zhang*, myself, and Rajesh Ranganath.

• X-CAL: Explicit Calibration for Survival Analysis. NeurIPS 2020. Work by myself*, Xintian Han*, Aahlad Puli*, Adler J. Perotte, and Rajesh Ranganath.