Haoran Liu (刘浩然)
Ph.D Student at Texas A&M UniversityResearch Interests: Graph Neural Networks, Deep Learning
About me: I am a Ph.D. student in the Department of Computer Science & Engineering at Texas A&M University, advised by Dr. James Caverlee. Previously, I was fortunate to work with Dr. Shuiwang Ji on geometric representation learning for biomedical data. Before coming to TAMU, I received master and bachelor degrees from Waseda University, Japan and Southeast University, China, respectively. My current research focus on temporal graph learning and adversarial attack on graphs. I also have a broad interest in graph/geometric learning, graph generation and diffusion models. I enjoy jogging and bouldering in my spare time.
Email: liuhr99 at tamu.edu
My Google Scholar / LinkedIn / Twitter / Github
Education
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Ph.D. Student at Department of Computer Science & Engineering,
Texas A&M University
June 2021 - Now College Station, TX
Advised by Prof. James Caverlee. -
Master of Engineering at Information Engineering,
Waseda University
Sep 2017 - Sep 2019 Tokyo, Japan
Advised by Prof. Kamata Sei-ichiro. -
Bachelor at School of Information Science and Engineering,
Southeast University
Sept 2014 - July 2018 Nanjing, China
Special Class for the Gifted Young, Chien-Shiung Wu Honor College.
Experiences
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Research Intern at NEC Labs America
Machine Learning Department
May 2023 - Aug 2023 Princeton, NJ
Mentor: Dr. Martin Renqiang Min. -
Research Intern at Baidu Research
Big Data Lab
June 2020 - Aug 2020 Beijing, China
Mentored by Dr. Haoyi Xiong and Prof. Dejing Dou.
Publications
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* indicates equal contribution.
- Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models
[pdf]
Meng Liu, Haoran Liu, Shuiwang Ji
International Conference on Learning Representations (ICLR), 2023
- Learning Hierarchical Protein Representations via Complete 3D Graph Networks
[pdf]
[code]
Limei Wang*, Haoran Liu*, Yi Liu, Jerry Kurtin, Shuiwang Ji
International Conference on Learning Representations (ICLR), 2023
- ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
[pdf]
[code]
Limei Wang*, Yi Liu*, Yuchao Lin, Haoran Liu, Shuiwang Ji
Conference on Neural Information Processing Systems (NeurIPS), 2022
- DIG: A Turnkey Library for Diving into Graph Deep Learning Research
[pdf]
[code]
Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan,
Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, Shuiwang Ji
Journal of Machine Learning Research (JMLR), 2022
- Exploring the Common Principal Subspace of Deep Features in Neural Networks
[pdf]
Haoran Liu, Haoyi Xiong, Yaqing Wang, Haozhe An, Dejing Dou
Machine Learning Journal (MLJ), 2022
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Edge-guided Hierarchically Nested Network for Real-time Semantic Segmentation
[pdf]
Yuqi Li, Sei-Ichiro Kamata, Haoran Liu
IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2019
Best Paper Award
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Hybrid Featured based Pyramid Structured CNN for Texture Classification
[pdf]
Haoran Liu, Sei-Ichiro Kamata, Yuqi Li
IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2019
Awards
- D. E. Shaw Research Graduate and Postdoctoral Women’s Fellowship. 2023
- Best Paper Award. ICSIPA. 2019.
- IPS Special Scholarship for International Student. Waseda University. 2017-2018.
- Foundation for the Advancement of Industry, Science and Technology (FAIS) Scholarship. 2017-2018.
- Second Prize in National Undergraduate Electronics Design Contest. 2016.
Services
Conference reviewer: NeurIPS (23'), KDD (23'), SIGIR(23'), LoG (22', 23'), NeurIPS Dataset and Benchmark Track (22', 23'), ICDM (20'), etc.
Jounal reviewer: IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Cybernetics.