CV
Education
- PhD in Applied Mathematics, DAMTP, University of Cambridge, 2021-2025
- MMath and BA, University of Cambridge, 2017-2021
- (Incomplete) BEng(CompSc), University of Hong Kong, 2015-2017
Work experience
- 2024 Nov-2025 Mar: GSK.ai AI/ML
- Latent geometry of neural networks.
- Supervisors: Dr. Emma Slade, Dr. Alexandra Pettet
- 2023 Jul-Aug: Research Exchange
- University of California, Los Angeles (UCLA)
- New numerical methods for sampling. See this paper.
- Supervisors: Prof. Stanley J. Osher, Prof. Wuchen Li
- 2022 Jan-Jun: External Research Assistant
- Schlumberger
- Numerical method to separate mixed signals with greedy fitting.
- Supervisor: Dr. Can Evren Yarman
- 2021 Aug-Oct: Summer Intern
- Faraday Predictive
- Signal spectral abnormality detection in the low-data regime using clustering techniques.
- Supervisors: Geoff Walker, Andrew Bates
- 2021 Jul-Aug: Summer Intern
- Applied Cryptosystems Department, ASTRI
- Testing federated machine learning frameworks for secure distributed learning.
- Supervisor: Dr. Kam Hong Shum
- 2020 Jul-Sep: Summer Research Assistant
- DAMTP, University of Cambridge
- Researching methods to predict glioblastoma subtypes using MRI and histology.
- Supervisor: Dr. Chao Li
Skills
- Programming
- Main: Python, PyTorch
- Probably OK: JAX, MATLAB
- Very rusty: C, C++, Java, Haskell
- Languages: English, Mandarin Chinese, Cantonese
Publications
Talks
Geometry Aware Particle Swarm Sampling Permalink
Department Seminar at Level Set Meeting, UCLA
Dataset Distillation as Optimal Quantization Permalink
Department Seminar at Applied Math Group Seminar, Berlin, Germany
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation Permalink
Poster at Mathematics and Image Analysis MIA’25, Paris, France
Data-Driven Geometry for Convex Optimization
Group seminar at ETH Zurich Optimization and Decision Intelligence group, Zurich
Blessing of dimensionality using low-dimensional data
PhD symposium talk at GSK.ai, London, UK
Noise-Free Sampling Algorithms via Regularized Wasserstein Proximals Permalink
Mini-symposium Talk at SIAM Conference on Imaging Science 2024, Atlanta, GA
Data-Driven Geometry for Convex Optimization Permalink
Workshop Talk at Big Data Inverse Problems Workshop, Edinburgh, UK
Deterministic Sampling with Wasserstein Proximals Permalink
Poster at Workshop on Optimal Transport: From Theory to Applications, Berlin, Germany
Data-driven Optimization via Mirror Descent
Department Seminar at Oxford-CUHK Joint Seminar Series, Oxford, UK (Online)
Data-Driven Convex Optimization via Mirror Descent
Mini-symposium Talk at ICIAM 2023, Tokyo, Japan
Learned Mirror Descent/Accelerating Plug-and-Play
Department Seminar at Level Set Meeting, Los Angeles, CA
Data-driven Mirror Descent with Acceleration and Robustness
Talk at GSK.ai PhD Symposium, Stevenage, UK
Others
- Third generation scholar of the Masason Foundation
- Reviewer for Inverse Problems and Imaging (IPI), SIAM Journal on Imaging Sciences, ICLR 2025.