Hi! I am a Ph.D. candidate in Data Science at the University of Virginia, advised by Dr. Thomas Hartvigsen. My research focuses on machine learning in ever-changing environments. Motivated by needs and insights in healthcare, I explore three research directions: (1) learning from dynamic data, (2) continually updating pretrained models, and (3) adapting evaluations to changed assumptions. My work has been published in ICML, KDD, Statistical Methods in Medical Research, Clinical Infectious Diseases, and other venues.
Education
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Ph.D. in Data Science, 2023–present
University of Virginia
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M.S. in Data Science, 2020
University of Virginia
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B.S. in Information and Computing Science, 2019
Soochow University
Work Experience
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Data Scientist, 2021–present
Center for Advanced Medical Analytics, School of Medicine, University of Virginia
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Graduate Research Assistant, 2020
Department of Computer Science, University of Virginia
News
- [Sep 2026] I am serving as a co-investigator on an NIH/NHLBI R01, I-HELP Sleep (R01HL186511, through 2030).
- [May 2026] I received a Gold Reviewer Award at ICML.
- [Sep 2025] I discussed data science and health research on CBS19’s Inside the Numbers.
Publications
In the constantly changing world, I enjoy questioning and learning.
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Instruction-Based Time Series Editing
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Highly Comparative Time Series Analysis of Oxygen Saturation and Heart Rate to Predict Respiratory Outcomes in Extremely Preterm Infants
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BEDTime: A Unified Benchmark for Automatically Describing Time Series
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Ordinal Outcome State-Space Models for Intensive Longitudinal Data
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Heart Rate Patterns Predicting Cerebral Palsy in Preterm Infants
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Maturation of Cardioventilatory Physiological Trajectories in Extremely Preterm Infants
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Cardiorespiratory Monitoring Data to Predict Respiratory Outcomes in Extremely Preterm Infants
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ReasonEdit: Editing Vision–Language Models Using Human Reasoning
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Test-Time Hinting for Black-Box Vision-Language Models
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Fast Leave-One-Cluster-Out Cross-Validation Using Clustered Network Information Criterion
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Cardiorespiratory Signature of Neonatal Sepsis: Development and Validation of Prediction Models in 3 NICUs
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Comparing Machine Learning Techniques for Neonatal Mortality Prediction: Insights from a Modeling Competition
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Development and Evaluation of a Prediction Model for Adult ICU Hemorrhage Using Only Continuous Cardiorespiratory Data
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The Extended Community of Diet Culture Around Eating Disorders: A Network Analysis of Reddit Forums
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Pathophysiological Responses to Bloodstream Infection in Critically Ill Transplant Recipients Compared with Non-transplant Recipients
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Ensemble of Large Language Models for Curated Labeling and Rating of Free-Text Data
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Identifying Nonlinear Relations Among Random Variables: A Network Analytic Approach