Two papers on AI-based physics law discovery accepted by AISTATS 2024
Multi-fidelity fusion research funded by BBSRC (UK)
Multi-fidelity fusion research funded by the University of Sheffield’s AIRE grant
Urban digital twin model awarded the Beijing Science and Technology Progress Award (Second Class)
Best Paper Nomination at ICCAD 2023 for the first transfer learning method for SRAM circuit yield estimation and optimization
Research Experience
Leads a research team at the University of Sheffield developing AI solutions for electronic design automation and energy systems
Conducts research on multi-fidelity fusion applied to biophysical modeling and autonomous driving systems
Develops urban digital twin models
Pioneers AI co-pilot methods for design optimization, including multi-fidelity Bayesian optimization, yield optimization, and knowledge transfer across designs
Works on reliable surrogate modeling and digital twin technologies