Senwei Liang
Computer Science · Purdue University West Lafayette
Publications
53
Citations
427
Est. group size
~5
Recurring co-author estimate
Active years
9
Publishing since 2018
Senwei Liang works at the intersection of machine learning and scientific computing, developing methods that use neural networks and symbolic learning to solve complex mathematical problems such as differential equations, quantum many-body systems, and epidemiological models. Their work also touches on quantum computing measurement techniques and applications of neural networks in medical image analysis. This research is aimed at building more efficient computational tools that combine data-driven learning with physics- and math-based models.
Publication output has grown steadily over the past decade, rising from occasional papers before 2020 to a sustained pace of about 7-12 papers per year since 2021.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- H-FEX: A symbolic learning method for Hamiltonian systems
Neural Networks · 2026
- Identifying stochastic dynamics via finite expression methods
Journal of Computational Physics · 2026
- Exploring the nexus of many-body theories through neural network techniques: the tangent model
Machine Learning Science and Technology · 2025
- Solving high-dimensional partial integral differential equations: The finite expression method
Journal of Computational Physics · 2025
- Flat Local Minima for Continual Learning on Semantic Segmentation
Lecture notes in computer science · 2025
- Robust Multimodal Learning for Ophthalmic Disease Grading via Disentangled Representation
Lecture notes in computer science · 2025
- Exploring the Nexus of Many-Body Theories through Neural Network Techniques: the Tangent Model
arXiv (Cornell University) · 2025
- LEARNING EPIDEMIOLOGICAL DYNAMICS VIA THE FINITE EXPRESSION METHOD
Journal of Machine Learning for Modeling and Computing · 2025
- Identifying Stochastic Dynamics Via Finite Expression Methods
SSRN Electronic Journal · 2025
- QuGStep: Refining step size selection in gradient estimation for variational quantum algorithms
APL Computational Physics · 2025
- H-FEX: A Symbolic Learning Method for Hamiltonian Systems
SSRN Electronic Journal · 2025
- Identifying Unknown Stochastic Dynamics via Finite expression methods
arXiv (Cornell University) · 2025
- Optimizing Shot Assignment in Variational Quantum Eigensolver Measurement
Journal of Chemical Theory and Computation · 2024
- Effective many-body interactions in reduced-dimensionality spaces through neural network models
Physical Review Research · 2024
- Solving PDEs on unknown manifolds with machine learning
Applied and Computational Harmonic Analysis · 2024
- arXiv (Cornell University)×23
- Journal of Computational Physics×3
- Lecture notes in computer science×3
- Machine Learning Science and Technology×2
- Proceedings of the AAAI Conference on Artificial Intelligence×2
- Zijian He
Computer Science · Purdue University West Lafayette
- Peter Jin
Computer Science · Purdue University West Lafayette
- Jianyang Gu
Computer Science · The Ohio State University
- Xiangyu Zhang
Computer Science · Purdue University West Lafayette
- Jonghoon Jin
Computer Science · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile