Yun Zhao

Research Scientist, Meta Platforms, Inc.

Research Areas: Large Language Models (LLMs), Deep Learning, Recommender Systems, Data Mining

Dr. Yun Zhao is a research scientist at Meta Platforms, Inc. in Menlo Park, California. He received his Ph.D. in Computer Science from the University of California, Santa Barbara in 2021, advised by Prof. Linda Petzold. Previously, he earned an M.E. from Tsinghua University (advisor: Prof. Zhisheng Niu) and a B.E. from Zhejiang University. His work has been recognized with the Best Paper Award (DMBIH’21) and the Best Student Paper Award (ACM-BCB’22).

Yuqing Wang, Yun Zhao, “Metacognitive prompting improves understanding in large language models”, in NAACL, 2024. PDF
Yuqing Wang, Yun Zhao, “TRAM: Benchmarking Temporal Reasoning for Large Language Models”, in ACL, 2024. PDF
Yuqing Wang, Yun Zhao, “Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models”, in submission, 2024. PDF
Yuqing Wang, Yun Zhao, Linda Petzold, “An empirical study on the robustness of the Segment Anything Model (SAM)”, in Pattern Recognition, 2024. PDF

Yuqing Wang, Yun Zhao, Linda Petzold, “Are Large Language Models Ready for Healthcare? A Comparative Study on Clinical Language Understanding”, in Machine Learning for Healthcare Conference (PMLR), 2023. PDF
Yu Deng, Lei Liu, Hongmei Jiang, Yifan Peng, Yishu Wei, Zhiyang Zhou, Yizhen Zhong, Yun Zhao, Xiaoyun Yang, Jingzhi Yu, Zhiyong Lu, Abel Kho, Hongyan Ning, Norrina B. Allen, John Wilkins, Kiang Liu, Donald Lloyd-Jones, Lihui Zhao, “Comparison of State-Of-The-Art Neural Network Survival Models With The Pooled Cohort Equations for Cardiovascular Disease Risk Prediction”, in BMC Medical Research Methodology, 2023. PDF

Haotian Xia, Rhys Tracy, Yun Zhao, Erwan Fraisse, Yuan-Fang Wang, Linda Petzold, “VREN: Volleyball Rally Dataset with Expression Notation Language”, in IEEE ICKG, 2022. PDF
Zhuowei Cheng, Franklin Ly, Tyler Santander, Elyes Turki, Yun Zhao, Jamie Yoo, Kian Lonergan, Jordan Gray, Christopher H Li, Henry Yang, Michael Miller, Paul Hansma, Linda Petzold, “Preliminary study: quantification of chronic pain from physiological data”, in Pain Reports, 2022. PDF
Yuqing Wang, Yun Zhao and Linda Petzold, “Predicting the Need for Blood Transfusion in Intensive Care Units with Reinforcement Learning”, in ACM-BCB, 2022. PDF (Best Student Paper Award)
Yuqing Wang*, Yun Zhao* and Linda Petzold, “Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis”, in IEEE ICDM, 2022. PDF
Yuqing Wang, Yun Zhao and Linda Petzold, “Enhancing Transformer Efficiency for Multivariate Time Series Classification”, in IEEE ICDM, 2022. PDF

Yun Zhao*, Yuqing Wang*, Junfeng Liu, Haotian Xia, Zhenni Xu, Qinghang Hong, Zhiyang Zhou, Linda Petzold, “Empirical Quantitative Analysis of COVID-19 Forecasting Models”, in IEEE ICDM Workshops, 2021. PDF (Best Paper Award)
Yun Zhao, “Data Mining in Neuroscience and Healthcare”, Ph.D. Thesis, UC Santa Barbara, 2021. PDF
Xinlu Zhang*, Yun Zhao*, Rachael Callcut and Linda Petzold, “Multiple Organ Failure Prediction with Classifier-Guided Generative Adversarial Imputation Networks”, in BIOKDD Workshop, 2021. PDF
Yun Zhao, Qinghang Hong, Xinlu Zhang, Yu Deng, Yuqing Wang and Linda Petzold, “BERTSurv: BERT-Based Survival Models for Predicting Outcomes of Trauma Patients”, in IEEE ICDM, 2021. PDF
Yuqing Wang*, Yun Zhao*, Rachael Callcut and Linda Petzold, “Empirical Analysis of Machine Learning Configurations for Prediction of Multiple Organ Failure in Trauma Patients”, in IEEE ICDM, 2021. PDF

Yun Zhao, Franklin Ly, Qinghang Hong, Zhuowei Cheng, Tyler Santander, Henry T. Yang, Paul K. Hansma, Linda Petzold, “How Much Does It Hurt: A Deep Learning Framework for Chronic Pain Score Assessment”, in DMBIH Workshop, 2020. PDF
Yun Zhao, Richard Jiang, Zhenni Xu, Elmer Guzman, Paul K. Hansma, Linda Petzold, “Scalable Bayesian Functional Connectivity Inference for Multi-Electrode Array Recordings”, in BIOKDD Workshop, 2020. PDF

Yun Zhao, “A Deep Learning Framework for Classification of in vitro Multi-Electrode Array Recordings”, in IEEE ICDM, 2019. PDF
Yun Zhao, “An Auxiliary Classifier Generative Adversarial Framework for Relation Extraction”, arXiv, 2019. PDF
Yun Zhao, Sheng Zhou, Zhisheng Niu, “Energy-Efficient Task Offloading for Multiuser Mobile Cloud Computing”, in IEEE ICCC, 2015. PDF
Tianchu Zhao, Sheng Zhou, Xueying Guo, Yun Zhao, Zhisheng Niu, “A Cooperative Scheduling Scheme of Local Cloud and Internet Cloud for Delay-Aware Mobile Cloud Computing”, in IEEE Globecom Workshop, 2015. PDF
Tianchu Zhao, Sheng Zhou, Xueying Guo, Yun Zhao, Zhisheng Niu, “Pricing policy and computational resource provisioning for delay-aware mobile edge computing”, in IEEE ICCC, 2016. PDF
Yangtian Yan, Bangcheng Sun, Yun Zhao, Zhenhui Huang, Hui Yang, Jian Song, “A Bi-directional Visible Light Communication System Based on DTMB-A”, in IEEE VTC, 2016. PDF
Xinyi Zhang, Shiliang Tang, Yun Zhao, Gang Wang, Haitao Zheng, Ben Y. Zhao, “Cold Hard E-Cash: Friends and Vendors in the Venmo Digital Payments System”, in ICWSM, 2017. PDF
Yun Zhao, “Energy-efficient Resource Allocation in Mobile Cloud Computing”, M.E. Thesis. PDF

Program Committee Member – IEEE International Conference on Data Mining (ICDM) 2021
Reviewer for journals and conferences in AI, machine learning, and data mining

Email: moc.liamg@ lmscnuY