On Uncertainty Calibration for Equivariant Functions
Edward Berman, Jacob Ginesin, Marco Pacini, Robin Walters
Transactions on Machine Learning Research (TMLR). PDF Blog
Keywords: Equivariance, Uncertainty Quantification

Smoothness Discrepancies in Dynamics Models and How to Avoid Them
Edward Berman, Luisa Li, Jung Yeon Park, Robin Walters
In preparation for submission to the International Conference on Machine Learning (ICML) 2026. Draft attached. PDF
Keywords: Dynamics Modeling, Discrete Differential Geometry

Efficient Point-spread Function Modeling with ShOpt.jl: A Point-spread Function Benchmarking Study with JWST NIRCam Imaging
Edward Berman, Jacqueline McCleary, Anton M. Koekemoer, Maximilien Franco, Nicole E. Drakos, Daizhong Liu, James W. Nightingale, Marko Shuntov, Diana Scognamiglio, Richard Massey, Guillaume Mahler, Henry Joy McCracken, Brant E. Robertson, Andreas L. Faisst, Caitlin M. Casey, Jeyhan S. Kartaltepe, and COSMOS-Web: The JWST Cosmic Origins Survey
Astronomical Journal. PDF Summary BibTex
Keywords: Weak Lensing, Computational Imaging, Differentiable Programming

On Soft Clustering for Correlation Estimators
Edward Berman, Sneh Pandya, Jacqueline McCleary, Marko Shuntov, Caitlin Casey, Nicole Drakos, Andreas Faisst, Steven Gillman, Ghassem Gozaliasl, Natalie Hogg, Jeyhan Kartaltepe, Anton Koekemoer, Wilfried Mercier, Diana Scognamiglio, and COSMOS-Web: The JWST Cosmic Origins Survey
Open Journal of Astrophysics. PDF BibTex
Keywords: Weak Lensing, Uncertainty Quantification, Differentiable Programming

The State of Julia for Scientific Machine Learning
Edward Berman, Jacob Ginesin
NeurIPS Machine Learning and the Physical Sciences Workshop (ML4PS), Perspectives Track, Oral Spotlight (4/300+ Submissions). PDF Summary BibTex
Keywords: Machine Learning, Perspectives, Julia

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