LabCompass

Guang Lin

Physics and Astronomy · Purdue University West Lafayette

Established · publishing since 1985

Publications

88

Citations

199

Est. group size

Recurring co-author estimate

Active years

42

Publishing since 1985

Research summary
AI-generated

This researcher works at the intersection of machine learning and computational science, developing methods such as operator learning, physics-informed neural networks, and Bayesian/federated learning approaches for simulating and analyzing complex physical systems. Applications in the publication record span partial differential equations, remote sensing, satellite systems, and some biomedical topics like cancer research, suggesting a methods-driven approach applied across varied domains. Prospective students would likely engage with topics combining statistical machine learning, uncertainty quantification, and scientific computing.

Operator learning and neural network models for PDEsBayesian inference and uncertainty quantificationFederated and distributed machine learningMachine learning applications in engineering and remote sensingInterdisciplinary applications (biomedical, satellite systems)

Publication output was minimal before 2021 but increased sharply from 2021-2023, followed by a decline in the past two years, suggesting a period of high activity that has recently slowed.

Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026

Publication cadence
Publications per year over the last 10 years — averaging 9.6/year recently
17182019: 1 publication192020: 1 publication202021: 10 publications212022: 20 publications20222023: 12 publications232024: 11 publications242025: 3 publications252026: 2 publications26
Recent publications
Publishes in
  • arXiv (Cornell University)×32
  • ChemRxiv×5
  • SSRN Electronic Journal×2
  • Proceedings of the AAAI Conference on Artificial Intelligence×2
  • Frontiers in Neuroinformatics×1
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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.

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