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Leisheng Yu I am a Ph.D. candidate in the Department of Computer Science at Rice University, advised by Dr. Xia “Ben” Hu. I focus on resource-constrained deployment of specialized, trustworthy, and efficient machine learning models that cooperate with cloud-hosted foundation models (e.g., LLMs) in a privacy-preserving manner. I also have industry and academic experience building production-scale personalization systems. My research applications span recommender systems, digital advertising, language, time series, graphs, and healthcare. Previously, I earned my bachelor's degree from Emory University in 2022, with a double major in Applied Mathematics (Magna Cum Laude) and Computer Science. At Emory, I worked with Dr. Carl Yang on machine learning for personalized healthcare. I am seeking full-time Research/Applied/AI/ML Scientist/Engineer roles starting May 2026. Happy to connect about opportunities! Email / CV / Google Scholar / LinkedIn / Github |
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Applied Scientist Intern, Amazon May 2025 - Aug 2025 · Seattle, WA Mentors: Dr. Peiyao Wang, Dr. Qilin Qi Topic: Bridging the Offline-Online Gap in Recommendation with Large Language Models |
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Machine Learning Research Engineer Intern, Samsung Electronics America Jun 2024 - Aug 2024 · Mountain View, CA Mentors: Dr. Wei-Yen Day, Dr. Rui Chen Topic: Addressing Delayed Feedback in Conversion Rate Prediction |
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Machine Learning Research Engineer Intern, Samsung Electronics America Jun 2023 - Aug 2023 · Mountain View, CA Mentors: Dr. Wei-Yen Day, Dr. Rui Chen Topic: Mixture of Experts for User Response Prediction |
Publications (* equal contribution) |
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Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction Leisheng Yu, Yanxiao Cai, Minxing Zhang, Xia Hu Conference on Health, Inference, and Learning (CHIL), 2025. |
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Confident or Seek Stronger: Exploring Uncertainty-Based On-device LLM Routing From Benchmarking to Generalization Leisheng Yu*, Yu-Neng Chuang*, Guanchu Wang, Lizhe Zhang, Zirui Liu, Xuanting Cai, Yang Sui, Vladimir Braverman, Xia Hu NeurIPS Workshop on Evaluating the Evolving LLM Lifecycle, 2025. |
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LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting Yu-Neng Chuang, Songchen Li, Jiayi Yuan, Guanchu Wang, Kwei-Herng Lai, Joshua Han, Zihang Xu, Songyuan Sui, Leisheng Yu, Sirui Ding, Chia-Yuan Chang, Alfredo Costilla Reyes, Daochen Zha, Xia Hu NeurIPS Workshop on Recent Advances on Time Series Foundation Models, 2025. |
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Addressing Delayed Feedback in Conversion Rate Prediction: A Domain Adaptation Approach Leisheng Yu, Yanxiao Cai, Lucas Chen, Minxing Zhang, Wei-Yen Day, Li Li, Rui Chen, Soo-Hyun Choi, Xia Hu IEEE International Conference on Data Mining (ICDM), 2024. |
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Enhancing Personalized Healthcare via Capturing Disease Severity, Interaction, and Progression Yanchao Tan, Zihao Zhou, Leisheng Yu, Weiming Liu, Chaochao Chen, Guofang Ma, Xiao Hu, Vicki S Hertzberg, Carl Yang IEEE International Conference on Data Mining (ICDM), 2023. |
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4SDrug: Symptom-based Set-to-set Small and Safe Drug Recommendation Yanchao Tan, Chengjun Kong, Leisheng Yu, Pan Li, Chaochao Chen, Xiaolin Zheng, Vicki S. Hertzberg, Carl Yang ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2022. |
Preprints |
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Beyond Fairness: Age-Harmless Parkinson's Detection via Voice Yicheng Wang, Xiaotian Han, Leisheng Yu, Na Zou arXiv:2309.13292, 2023. |
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Thanks to Jon Barron for the amazing template. |