Experience
Academic research, research internships, education, and selected awards.
Research and industry
SCiDaMa Group, University of Washington, Seattle, USA
Develop neural surrogate models and reinforcement learning methods for high-dimensional flow control. Audit learned action sensitivities and validate controllers in direct numerical simulation.
University of Cambridge, Cambridge, UK
Studied nonlocal state inference in neural PDE surrogates, with emphasis on reconstructing latent variables from partial and spatially coarsened observations.
Imperial College London, Data Science Institute, Cambridge and London, UK
Compared CNN and transformer architectures for next day wildfire segmentation. Used SHAP, Grad-CAM, and Integrated Gradients to analyze feature attribution.
J.P. Morgan, Quantitative Solutions, London, UK
Built transformer research pipelines to track sentiment in financial news and forecast stock returns.
European Bioinformatics Institute, Hinxton, UK
Built synthetic data simulations and trained CNN and transformer models for biological sequence classification with the Goldman group.
Cambridge iGEM Team, Cambridge, UK
Designed and modeled a synthetic gene expression circuit in Julia for a Gold Medal iGEM project.
Training
University of Washington, Seattle
AI Institute in Dynamic Systems. Research in scientific machine learning, reinforcement learning, flow control, and fluid mechanics.
University of Cambridge, Cambridge
Studied machine learning, computational science, mathematical methods, inference, control theory, and statistical signal processing.
Selected recognition
1st Place, AIGC Hackbridge UK Hackathon
2nd Place, Citadel European Datathon
Gold Medal, iGEM Synthetic Biology Competition
1st Place, AIGC Hackbridge UK Hackathon
Top 1%, IMC Quantitative Trading Competition