Scholar
Jacob Helwig
Google Scholar ID: NtqpyUAAAAAJ
Ph.D. Student, Texas A&M University
Citations & Impact
Citations
304
H-index
4
i10-index
4
Publications
12
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- Published multiple papers, including: 'A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling', 'A Geometry-Aware Message Passing Neural Network for Modeling Aerodynamics over Airfoils' (Best Student Submission in NeurIPS 2024 ML4CFD Competition), 'Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency' (ICML 2024), 'SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations' (ICLR 2024), 'Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction' (LoG 2023), 'Group Equivariant Fourier Neural Operators for Partial Differential Equations' (ICML 2023), and 'Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems' (Foundations and Trends in Machine Learning, 2025). Additionally, a survey paper 'Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems' was accepted to Foundations and Trends in Machine Learning.
Research Experience
- Completed two internships at LinkedIn with the CoreAI Team, one in August 2024 on pretraining large transformer models and another in January 2025 on sequential recommendation with large transformer models.
Education
- Obtained a bachelor’s degree in mathematics from the University of Texas at Austin, as well as several certificates in computing and statistical modeling through the UT Computer Science Department and the UT Statistics and Data Science Department. Advisor is Prof. Shuiwang Ji.
Background
- Currently a 5th year Ph.D. student in the Department of Computer Science & Engineering at Texas A&M University. Research interests are deep learning and its applications.
Co-authors