Mentor Areas
Yong Chen is Associate Professor of Biostatistics at University of Pennsylvania. He directs a Computing, Inference and Learning Lab at University of Pennsylvania (https://penncil.med.upenn.edu/). Dr. Chen is an expert in synthesis of evidence from multiple data sources, including systematic review and meta-analysis, distributed algorithms, and data integration, with applications to comparative effectiveness studies, health policy, pharmacovigilance, and precision medicine. He is also working on developing methods to deal with suboptimal data quality issues in health system data, dynamic risk prediction, pharmacovigilance, and personalized health management. He has over 100 publications in a wide spectrum of methodological and clinical areas.
Dr. Chen has been principal investigator on a number of grants, including R01s from the National Library of Medicine and National Institute of Allergy and Infectious Diseases, and Improving Methods for Conducting Patient-Centered Outcomes Research grant from Patient-Centered Outcomes Research Institute. Dr. Chen received his bachelor’s degree in Mathematics at the University of Science and Technology of China, Master degree in Pure Mathematics and Ph.D. in Biostatistics at the Johns Hopkins University. He is an elected fellow of the Society for Research Synthesis Methodology, and the International Statistical Institute. He is a recipient of Best Paper Award by the International Medical Informatics Association (IMIA) Yearbook Section on Clinical Research Informatics, Institute of Mathematical Statistics Travel Award, Margaret Merrell Award for excellence in research at the Johns Hopkins University, and Distinguished Faculty Award at the University of Pennsylvania.
Please check out our 2 minutes video at
https://youtu.be/dvX0h5pFtIk
For more information, please visit our project website
https://pdamethods.org/
And lab website:
https://penncil.med.upenn.edu/
Description:
With the increasing availability of biomedical data, including electronic health records (EHR) data, claims data and biobank data, it is important to effectively integrate evidence from multiple data sources to enable reproducible scientific discovery. However, we are still facing practical challenges in data integration, such as protection of data privacy, high-dimensionality of features, and heterogeneity across different datasets. In this project, we will investigate novel data sharing strategies for integrating data from different data sites within a distributed research network. We aim to develop non-iterative and privacy-preserving algorithms to handle clinical heterogeneity across different sites, as well as non-standard data structure. The methods will be used to real world settings, including PCORnet and OHDSI data.
Preferred Qualifications
basic computer science knowledge, python or R
Details:
Preferred Student Year
First-year, Second-Year, Junior, Senior
Volunteer
Yes
Yes indicates that faculty are open to volunteers.Paid
Yes
Yes indicates that faculty are open to paying students they engage in their research, regardless of their work-study eligibility.Work Study
Yes
Yes indicates that faculty are open to hiring work-study-eligible students.