About Me
Yuyang Ye is a Ph.D. graduate in Information Technology from Rutgers University. His research interests span data mining, large language models, graph learning, learning to rank techniques, and reinforcement learning, as well as their applications. He has extensive experience working on real-world problems, including dynamic treatment recommendation, multi-modal personalization, and industrial optimization, with a focus on both theoretical foundations and practical deployment. Yuyang has conducted research collaborations and internships at leading industrial research labs, including Baidu Research (USA), NEC Laboratories America, and Baidu Inc. His work has been recognized through publications in top-tier conferences and journals such as ICML, KDD, AAAI, TKDE, and ICDM.
Education
-
Ph.D. in Information Technology
Rutgers University, New Jersey, United States (Sep 2020 - May 2025)
Advisor: Prof. Hui Xiong -
M.Eng. in Computer Science
University of Science and Technology of China, Hefei, China (Aug 2017 - Jun 2020)
Advisors: Prof. Hui Xiong & Prof. Qi Liu -
B.Eng. in Computer Science
University of Science and Technology of China, Hefei, China (Sep 2013 - Jun 2017)
Work Experience
- Research Assistant
Rutgers Business School, Piscataway, NJ, USA (Sep 2020 – May 2025)- Worked on influence-based graph autoencoder for university evaluation
- Developed MLLM-based multi-modal recommendation framework
- Built SAFER, a risk-aware framework for multimodal dynamic treatment recommendation
- Developed RLHF-based personalized advertisement system
- Research Intern
Baidu Research Institute, Sunnyvale, CA, USA (Jun 2024 – Sep 2024)- Built diffusion-based motion generation for virtual characters
- Research Intern
NEC Laboratories America, Princeton, NJ, USA (May 2023 – Sep 2023)- Developed the PAIL engine for imitation learning-based industrial carbon neutrality optimization
- Research Intern
Baidu Inc., Talent Intelligence Center, Beijing, China (Jul 2018 – Aug 2021)- Proposed NNDSP for dynamic graph analysis-based high-potential talent identification
- Designed MANE for organizational network embedding for talent analysis
Teaching Experience
- Independent Lecturer, Rutgers University
- Production and Operation Management (Fall 2024, undergraduate course)
- Management Information Systems (Spring 2025, undergraduate course)
Professional Activities
- Reviewer for top conferences and journals, including:
- ICLR, NeurIPS, KDD, AAAI, IJCAI, ICDM, SDM, CIKM, ICASSP
- IEEE TKDE, ACM TKDD, IEEE TBD, ACM TOIS, DMLR