LIANG Enming (梁恩明)

Research Assistant Professor, City University of Hong Kong

I am a Research Assistant Professor at City University of Hong Kong. My research lies at the intersection of machine learning and optimization, with a focus on methods that respect hard constraints and scale to real decision-making systems. My work is motivated by applications in power grids, mobility systems, and climate resilience.

I received my Ph.D. from the Department of Data Science at City University of Hong Kong, supervised by Prof. Minghua Chen. My Ph.D. thesis received the 2026 ACM SIGEnergy Doctoral Dissertation Award Honorable Mention. I received my B.Eng. from SYSU, supervised by Prof. Renxin Zhong.

My recent work develops homeomorphism-based methods for constrained learning and optimization, including Homeomorphic Projection, Homeomorphic Optimization, and Gauge Flow Matching. These methods aim to make neural models efficient and reliable under constraints.

Homeomorphism

Slides for Homeomorphism Methods

I have worked on the DeepOPF project with Prof. Steven Low, focusing on machine learning methods for power-grid operation. I also visited the University of Cambridge to work with Prof. Srinivasan Keshav on power-grid resilience under extreme weather, and contributed to the AI for Optimal Power Flow tutorial at the Climate Change AI Summer School with Prof. Priya L. Donti. I also collaborate with DiDi on machine learning methods for urban mobility-on-demand systems. Previously, I was a research intern at MSRA (Beijing, 2022) and Noah's Ark Lab (Shenzhen, 2021), working on reinforcement learning and machine learning for logistics and wireless optimization.

Please feel free to contact me if you want to discuss possible collaborations or research directions.