Zhipeng Liang
PhD Candidate
Hong Kong Island, Hong Kong, China
Summary
👤
Senior🎓
Top SchoolZhipeng Liang is a PhD candidate at HKUST with nine years of experience applying statistical learning and large-scale optimization to reinforcement learning, bandit algorithms, and privacy-aware algorithm design. He has industry experience as a quantitative researcher at Citadel Securities and machine learning researcher roles at Tencent and NetEase Games, where he built privacy-preserving recommendation systems and deep RL solutions. His work bridges rigorous theory and production-ready systems, focusing on federated learning and algorithmic privacy in real-world settings. Academically strong in both management science and mathematics (top-ranked undergraduate at Sun Yat-Sen University), he brings a rare combination of theoretical depth and practical deployment experience. Zhipeng maintains an active research profile and shares his work at liangzp.github.io, signaling a commitment to reproducible, open scholarship. Colleagues describe him as someone who quickly translates complex optimization ideas into scalable code that meets industry constraints.
9 years of coding experience
1 year of employment as a software developer
Bachelor's of Management, Management Science, 4.2/5.0, Rank 1/56, Excellent Graduate of Sun Yat-Sen University, Bachelor's of Management, Management Science, 4.2/5.0, Rank 1/56, Excellent Graduate of Sun Yat-Sen University at 中山大学岭南（大学）学院Bachelor's of Science, Mathematics and Applied Mathematics, 4.4/5.0, Bachelor's of Science, Mathematics and Applied Mathematics, 4.4/5.0 at 中山大学Doctor of Philosophy - PhD, Industrial Engineering & Decision Analytics, Doctor of Philosophy - PhD, Industrial Engineering & Decision Analytics at 香港科技大学
Github Skills (37)
deterministic9
proximal-policy-optimization9
deep-reinforcement-learning8
optimization8
ddpg8
gradient8
trading7
conda7
reinforcement-learning7
options-trading7
python7
forex7
os-agnostic6
package-management6
backtesting-trading-strategies5
Programming languages (2)
Github contributions (5)
liangzp/Reinforcement-learning-in-portfolio-management-
Aug 2018 - Apr 2020
In this paper, we implement three state-of-art continuous reinforcement learning algorithms, Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO) and Policy Gradient (PG)in portfolio management.
Contributions:1 PR, 47 pushes, 1 branch in 1 year 8 months
deep-deterministic-policy-gradientpolicydeep-reinforcement-learningreinforcementportfolio-management
liangzp/DQLearning-Toolbox
Jun 2018 - Jul 2018
Contributions:41 commits, 36 pushes, 1 branch in 7 days
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