PhD Candidate, Computer Science, NYU
email: goldstein [at] nyu [dot] edu
pronouns: he/him/his
Here’s my google scholar
A dynamic risk score for early prediction of cardiogenic shock using machine learning. Published in European Heart Journal: Acute Cardiovascular Care, March 2024. Also available on arxiv. Work by Yuxuan Hu*, myself, Rajesh Ranganath, and many others.
Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes. Preprint. March 2024. Work by Yifan Chen*, me*, Mengjian Hua*, Michael S. Albergo, Nicholas M. Boffi, and Eric Vanden-Eijnden.
Scalable Interpolant Transformers. Preprint. January 2024. Work by Nanye Willis Ma*, me, Michael Albergo, Nick Boffi, Eric Vanden-Eijnden, and Saining Xie.
Stochastic interpolants with data-dependent couplings. Preprint. October 2023. Work by me*, Michael Albergo*, Nick Boffi, Rajesh Ranganath, and Eric Vanden-Eijnden.
QTNet: Predicting Drug-Induced QT Prolongation with Artificial Intelligence-Enabled Electrocardiograms. To appear in 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. ICML 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.