Jacob Helwig

Ph.D. Student, Texas A&M University

jacob.a.helwig [AT] gmail.com

Bio

I'm currently a 5th year Ph.D. student in the Department of Computer Science & Engineering at Texas A&M University. My advisor is Prof. Shuiwang Ji, who leads the Data Integration, Visualization, and Exploration (DIVE) Laboratory. I obtained my bachelor’s degree in mathematics from the University of Texas at Austin in 2020, as well as several certificates in computing and statistical modeling through the UT Computer Science Department and the UT Statistics and Data Science Department. Here is my résumé.

My research interests are deep learning and its applications.

News

Publications [Google Scholar]

* indicates equal contribution.

A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling

Jacob Helwig, Sai Sreeharsha Adavi, Xuan Zhang, Yuchao Lin, Felix S. Chim, Luke Takeshi Vizzini, Haiyang Yu, Muhammad Hasnain, Saykat Kumar Biswas, John J. Holloway, Narendra Singh, N. K. Anand, Swagnik Guhathakurta, Shuiwang Ji

Preprint

Paper Code

A Geometry-Aware Message Passing Neural Network for Modeling Aerodynamics over Airfoils

Jacob Helwig, Xuan Zhang, Haiyang Yu, Shuiwang Ji

NeurIPS 2024 ML4CFD Competition

Paper Code

Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency

Yuchao Lin, Jacob Helwig, Shurui Gui, Shuiwang Ji

International Conference on Machine Learning (ICML), 2024

Paper

SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations

Xuan Zhang*, Jacob Helwig*, Yuchao Lin, Yaochen Xie, Cong Fu, Stephan Wojtowytsch, and Shuiwang Ji

International Conference on Learning Representations (ICLR), 2024

Paper Code

Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction

Cong Fu, Jacob Helwig, and Shuiwang Ji

Learning on Graphs Conference (LoG), 2023

Paper Code

Group Equivariant Fourier Neural Operators for Partial Differential Equations

Jacob Helwig*, Xuan Zhang*, Cong Fu, Jerry Kurtin, Stephan Wojtowytsch, and Shuiwang Ji

International Conference on Machine Learning (ICML), 2023

Paper Code

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Xuan Zhang*, Limei Wang*, Jacob Helwig*,Youzhi Luo*, Cong Fu*, Yaochen Xie*,..., Alan Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Lio, Rose Yu, Stephan Gunnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, and Shuiwang Ji

Foundations and Trends in Machine Learning, 2025

Paper Code

A Covariate-Dependent Approach to Gaussian Graphical Modeling in R

Jacob Helwig, Sutanoy Dasgupta, Peng Zhao, Bani K. Mallick, Debdeep Pati

Paper Code

An Approximate Bayesian Approach to Covariate-dependent Graphical Modeling

Sutanoy Dasgupta, Peng Zhao, Jacob Helwig, Prasenjit Ghosh, Debdeep Pati, Bani K. Mallick

Paper

Résumé

Here is my résumé.

Jacob Helwig
based on the templates by Martin Saveski and Paul J. Wright