Haoyu Geng

Haoyu Geng

Ph.D. candidate

Shanghai Jiao Tong University

Haoyu Geng (耿 皓宇)

I am a PhD candidate in computer science at Shanghai Jiao Tong University (SJTU), supervised by Prof. Junchi Yan. Prior to this, I earned bachelor degree from IEEE Honor Class in SJTU, with major in Computer Science.

My research interests lie in theory and applications of graph learning and combinatorial optimization. (1) In graph learning, I aspire to develop novel algorithms in graph signal processing and graph neural networks with applications to recommender systems, urban computing, etc. (2) In combinatorial optimization (CO), I wish to explore end-to-end systems for the interplay of areas in machine learning and operational research (ML4CO).

I am always open for discussion and potential collaborations and please feel free to contact me via email: genghaoyu98 at sjtu dot edu dot cn

What's New
  • One paper on predict-and-optimize is accepted by NeurIPS 2024 Dataset & Benchmark Track!
  • EasyDGL is accepted by TPAMI 2024!
  • Two papers are accepted by KDD 2023!
  • Our EasyDGL preprint is available on arXiv and code is also available!
  • One paper on graph signal sampling is accepted by ICLR 2023!

Academic Services: I serve as reviewer for NeurIPS 2023~2024, ICLR 2024~2025, LOG 2023~2024, ICML 2024, IJCAI 2024, AAAI 2025, AISTATS 2025

Interests

  • Graph Learning
  • Combinatorial Optimization

Education

  • Ph.D. in Computer Science, 2020-2025 (expected)

    Shanghai Jiao Tong University

  • BSc in Computer Science, 2016-2020

    Shanghai Jiao Tong University

Recent Publications

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Projects

Awesome Machine Learning for Combinatorial Optimization Resources

A list of resources that utilize machine learning technologies to solve combinatorial optimization problems.

Experience

 
 
 
 
 

Algorithm Intern

Didi

Dec 2021 – Aug 2022 Beijing
  • Deployed graph learning algorithms for Estimated Time of Arrival (ETA) in ride-hailing services.
 
 
 
 
 

Algorithm Intern

Meituan

May 2021 – Aug 2021 Beijing
  • Deployed Continuous-time attention on Meituan Recommendations with temporal point process
  • Our program Won 2020-2021 Best Research Collaboration Award at Meituan

Contact