Hom-PGD+: Fast Reparameterized Optimization over Non-convex Ball-Homeomorphic Set
ICML 2026.
LIANG Enming 梁恩明
Machine learning and optimization with hard constraints.
Research Assistant Professor City University of Hong Kong
I develop methods that respect constraints and scale to real decision-making systems, motivated by applications in power grids, mobility systems, and climate resilience. I welcome collaborations and discussions about research directions.
Selected Research
Learning and Optimization with Constraints
Hom-PGD+: Fast Reparameterized Optimization over Non-convex Ball-Homeomorphic Set
Gauge Flow Matching: Efficient Constrained Generative Modeling over General
Convex Set and Beyond.
Fast Projection-Free Approach (without Optimization Oracle) for Optimization
over Compact Convex Set.
Efficient Bisection Projection to Ensure Neural-Network Feasibility for
Optimization over General Set.
ICML 2025.
Homeomorphic Projection to Ensure NN Solution Feasibility for Constrained
Optimization.
Learning and Optimization Theory
Climate Resilience
Ride-Sourcing Optimization
Learning to Dispatch and Reposition on a Mobility-on-Demand Platform.
DFF: Decision-Focused Fine-tuning for Smarter Predict-then-Optimize with
Limited Data.
A Smart Predict-then-Optimize Framework for Vehicle Rebalancing Problem.
Transportation Research Part B. 2026
Tested in DiDi (largest ride-sourcing platform in China).
Tested in DiDi (largest ride-sourcing platform in China).
An Integrated Reinforcement Learning and Centralized Programming Approach for
Online Taxi Dispatching.
IEEE TNNLS 2021.
Power Grid Operation
AI for Optimal Power Flow Tutorial.
Climate Change AI Summer School 2024 & 2026.
Partially Permutation-Invariant Neural Network for Solving Two-Stage
Stochastic AC-OPF Problem.
IEEE TPWRS 2025.
Traffic Signal Control
Homeomorphism Methods
Background
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, advised by Prof. Renxin Zhong.
I have worked on the DeepOPF project with Prof. Minghua Chen and Prof. Steven Low of Caltech, 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. I led the 2024 and 2026 revisions of the AI for Optimal Power Flow tutorial for the Climate Change AI Summer Schools. Across the two revisions, I received advisory comments from Prof. Priya L. Donti, Prof. Minghua Chen, and the CCAI Committee. I also collaborate with DiDi on decision-focused 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.