Yaoguang Zhai
Ph.D. Candidate
Computer Science and Engineering
University of California, San Diego
Academic
- 2018 - Present: Ph.D. Candidate at Computer Science and Engineering, University of California, San Diego
Research Interests: My focus lies in learning and search methodologies,
non-convex and global optimization, and black-box optimization.
In terms of applications, I am particularly interested in the use of machine learning algorithms in
molecular dynamics simulations, chip designs, and protein designs.
- 2016 - 2018: Master degree at Computational Science, Mathmatics and Engineering, University of California, San Diego
- 2006 - 2008: Master at Royal Institute of Technology, Stockholm, Sweden
Work and Intern Experience
- Jun.2022 - Sep.2022, Jul.2023 - Dec.2023: Applied Scientist Intern, Amazon
- Jun.2020 - Sep.2020: Data Scientist Intern, Lawrence Livermore National Lab
- Jun.2019 - Sep.2019: Data Scientist Intern, Interpreta
- Jun.2018 - Sep.2018: Data Scientist Intern, Veritone
Publications
Sample-and-Bound for Non-Convex Optimization
AAAI Conference on Artificial Intelligence, AAAI 2024
A “short blanket” dilemma for a state-of-the-art neural network potential for water:
Reproducing experimental properties or the physics of the underlying many-body interactions?
The Journal of Chemical Physics, 2023
Monte Carlo Tree Descent for Black-Box Optimization
Conference on Neural Information Processing Systems (NeurIPS), 2022
Active learning of many-body configuration space: Application to the Cs+–water MB-nrg potential energy function as a case study
The Journal of Chemical Physics, 2020
Aeroelastic Stability Assessment of an Industrial Compressor Blade Including Mistuning Effects
Journal of Turbomachinery, 2012