Towa Shixun Huang

Towa Shixun Huang (黄诗迅)

I am an incoming Computer Science PhD student at the University of British Columbia. I completed my BSc in Mathematics and Computer Science at the University of Toronto, where I have been fortunate to be supervised by Professor Jude Kong and Professor Eitan Grinspun. I am part of the DGP Lab, where I work with Yue Chang.

I work on Physics AI: integrating physical principles with learning methods to build scalable, stable, and generalizable models via data-assisted optimization of differential/integral equations that preserve physical structure.

Email / CV / GitHub / Google Scholar

News

May 2026 Closing Trajectories was accepted to SCA 2026. See you in Barcelona.
Apr 2026 I will join the University of British Columbia (UBC) as a Computer Science PhD student.
Jan 2026 Nominated by the UofT CS department for the UTEA Award (CAD 12,000).
Dec 2025 Completed research assistant term at AIMMlab.
Apr 2025 Joined the Dynamic Graphics Project Lab as an undergraduate research assistant.
Jan 2025 Published first-author systematic review on Avian Influenza in One Health.

Publications

Graphics Numerical Optimization

Closing Trajectories: Equation-Free Cyclic Animation via Koopman Surrogates

Shixun Huang, Siyuan Chen, Yue Chang, Zhecheng Wang, Peter Yichen Chen

ACM SIGGRAPH / Eurographics Symposium on Computer Animation (SCA), 2026

Paper Link

Closing Trajectories formulates cyclic animation as a numerical optimization problem, solving for periodic controls under hard temporal constraints through a Koopman surrogate.

Graphics Machine Learning

Odd-DC: Generalizable Neural Model Reduction via Odd Difference-of-Convex Structure

Shixun Huang, Eitan Grinspun, Yue Chang

arXiv preprint, 2025

Paper Link

Odd-DC combines convexity and odd symmetry to improve neural model reduction generalization under unseen load magnitudes and directions, while preserving compact latent spaces and real-time performance.

Epidemiology Machine Learning

A systematic review of mathematical and machine learning models of Avian Influenza

Shixun Huang, Nicola Luigi Bragazzi, Zahra Movahedi Nia, Murray Gillies, Emma Gardner, Doris Leung, Itlala Gizo, Jude D. Kong

One Health, 2025

Paper Link

A systematic review of mathematical, statistical, and machine learning models for understanding Avian Influenza dynamics, forecasting outbreaks, and evaluating intervention strategies.

Experience

Dynamic Graphics Project Lab

Apr 2025 - Present

Undergraduate Research Assistant

Advisors: Eitan Grinspun, Yue Chang

Developing input-convex model reduction methods for physics-based simulation.

AIMMlab

Jun 2024 - Dec 2025

Undergraduate Research Assistant

Advisor: Jude Dzevela Kong

Built epidemiological models with Bayesian inference for infectious disease dynamics.