Archives

Brian Lee, PhD, is a new member of the Department of Biostatistics, Epidemiology, and Informatics whose work focuses on population brain health, including the developmental origins of autism and other neurodevelopmental conditions. In this Q&A, Brian shares what inspired his work, the real-world challenges it addresses, and what drew him to DBEI.

Xinran Miao is a postdoctoral researcher at the University of Pennsylvania, where she works with Alex Levis and Nandita Mitra. She received Ph.D. in Statistics from the University of Wisconsin-Madison […]

Yue Wu is a Postdoctoral Researcher in the Department of Biostatistics, Epidemiology and Informatics at the University of Pennsylvania’s Perelman School of Medicine. Her research focuses on statistical machine learning […]

Vijay is an Application Developer in the Wagenaar Lab. He focuses on designing, building, and maintaining robust software for Pennsieve–a cloud-based data management platform built to accelerate collaborative, reproducible biomedical […]

Mingling Ge, MS, is a Research Coordinator in the Wagenaar Lab at the Center for Neuroengineering & Therapeutics, where he builds the data infrastructure behind Epilepsy.Science – an NIH-funded initiative […]

Dr. Rasika Venkatesh is an NRSA Postdoctoral Fellow working with Dr. Dokyoon Kim in Informatics Division of DBEI. She completed her PhD in Genomics and Computational Biology at the University […]

Jie Hu is a Research Associate at the Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania. His research focuses on developing statistical and machine learning […]

Manu Shivakumar, PhD, MA, MS, is currently a postdoctoral researcher in Kim’s Lab, led by Dr. Dokyoon Kim, Associate Professor of Informatics. He completed his PhD in Genomics and Computational […]

Dr. Dong-gi Lee is a Postdoctoral Researcher in the Informatics Division at DBEI. He leverages artificial intelligence to untangle complex biological and clinical data, effectively bridging advanced computational methodologies with […]

Dr. Bingyu Zhang is a Postdoctoral Researcher in the Biostatistics Division at the University of Pennsylvania. She has extensive experience in clinical evidence generation and evidence synthesis using real-world data, […]

Aristeidis (Aris) Sotiras, PhD, is an Associate Professor of Radiology at the Perelman School of Medicine at the University of Pennsylvania, with secondary appointments in the Departments of Electrical and […]

Dr. Hanieh Razzaghi is an informatics researcher focused on advancing the quality as well as reproducibility and replicability of clinical data for research within learning health systems and multi-institutional research […]

Dr. Dominique Duncan is an Associate Professor of Neurology, Bioengineering, Informatics, and Neurosurgery at the University of Pennsylvania. Her research focuses on biomedical data infrastructure and multimodal data integration for […]

Andrew J. King, PhD, is an Assistant Professor of Informatics in the Department of Biostatistics, Epidemiology, and Informatics (DBEI) and the Director of Precision Education Technology at the University of […]

Dr. Ari Z. Klein is a Research Assistant Professor of Informatics. Broadly, his work involves developing, evaluating, and applying natural language processing, machine learning, artificial intelligence, and mixed methods to […]

Dr. Sinha is a neuroscientist whose research bridges neural data science, clinical neuroscience, and scalable computational tools to advance personalized care for neurological disorders. His lab integrates multimodal neural data, […]

Dr. Yi Lian is a Postdoctoral Research Fellow in the Biostatistics Division at the University of Pennsylvania. His methodological research spans high-dimensional statistical learning, federated learning, missing-data methods, and integrative […]

Sriharsha Mopidevi is a Senior Application Developer in the Artificial Intelligence for Ambulatory Care Innovation (AI-4-AI) Lab in the Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania. […]

Dr. Yingying Lu is a biostatistician at the Palliative and Advanced Illness Research (PAIR) Center. She received her PhD in Statistics from the University of Arizona, where she specialized in bioinformatics and […]

Miranda is an MD/PhD student from the Graduate Group in Genomics and Computational Biology (GCB). She is interested in developing integrative approaches to leverage multi-omic and clinical data towards precision […]

Akira Nair is a PhD student in Genomics and Computational Biology and a member of the Kim Lab. He received his BS in Computer Science from Brown University. His research […]

Thomas Westbrook is a PhD Candidate in Genomics & Computational Biology, Machine Learning & Multi-Omics, and has joined the Kim Lab at the Center for AI-Driven Translational Informatics (CATI). His […]

Lina is a PhD student in the Genomics and Computational Biology program at Penn. She received her BS in Biometry and Statistics and her MPS in Applied Statistics from Cornell University. She […]

Yidi Huang is a graduate student from the Graduate Group in Genomics and Computational Biology (GCB) and has joined the Kim Lab at the Center for AI-Driven Translational Informatics (CATI) […]

Matthew Lee is a PhD student in Computational Immunology and a member of the Kim Lab. His research interests include machine learning and deep learning, multimodal data integration across omics, […]

Amber Rhee is a PhD student in Epidemiology and Biostatistics. Her research focuses on multimodal data integration for pain phenotyping and the application of precision public health to chronic pain. […]

Miranda is an MD/PhD student from the Graduate Group in Genomics and Computational Biology (GCB). She is interested in developing integrative approaches to leverage multi-omic and clinical data towards precision […]

Erica Suh is a graduate student from the Graduate Group in Genomics and Computational Biology (GCB) and has joined the Kim Lab in July, 2021. She is interested in applying […]

Chang-Uk Jeong is a PhD student in Bioengineering in the Department of Biostatistics, Epidemiology, and Informatics (DBEI). He joined the Kim Lab at the Center for AI-driven Translational Informatics (CATI) […]

Dr. Jaehyun Joo is a bioinformatician in the Center for AI-Driven Translational Informatics (CATI) within the Department of Biostatistics, Epidemiology, and Informatics. His research focuses on biomedical data science, leveraging […]

Assistant Professor of Pathology and Laboratory MedicineMember, Institute of Diabetes, Obesity and Metabolism – University of Pennsylvania Department: Pathology and Laboratory Medicine Contact 713B Stellar-Chance Laboratories BuildingPhiladelphia, PA 19104 Office: 215 […]

Assistant Professor of Pathology and Laboratory MedicinePerelman School of Medicine at the University of Pennsylvania Department: Pathology Contact Department of Pathology and Laboratory MedicinePerelman School of Medicine at the University […]

Professor of Medicine (Renal-Electrolyte and Hypertension)Hospital Appointment, Philadelphia VA Medical CenterMember, Institute of Diabetes and Obesity and MetabolismMember, Institute of Translational Medicine and TherapeuticsMember of MD PhD admission Committee, PSOMCodirectorof […]

Dr. Huang is an Assistant Professor of Pathology and Laboratory Medicine, with a secondary appointment in the Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania Perelman School of Medicine. […]

Dr. Shefali Setia‑Verma is an Assistant Professor in the Department of Pathology and Laboratory Medicine and the Division of Informatics within the Department of Biostatistics, Epidemiology, and Informatics at the Perelman […]

Yonghyun Nam is a Research Assistant Professor of Informatics in the University of Pennsylvania’s Perelman School of Medicine. He joined the faculty in 2025 after completing postdoctoral and research associate […]

Jordana B. Cohen, MD, MSCE is an Associate Professor of Medicine and Epidemiology in the Department of Biostatistics, Epidemiology, and Informatics, a Senior Scholar in the Center for Clinical Epidemiology and Biostatistics (CCEB), and a Fellow at the Leonard Davis Institute of Health Economics.

Joseph Romano, PhD, MPhil, MA is an Assistant Professor of Informatics in the Department of Biostatistics, Epidemiology and Informatics, Senior Fellow in the Institute for Biomedical Informatics (IBI) and an investigator in the Center of Excellence in Environmental Toxicology (CEET).

Dr. Xie’s current research goals are twofold. First, she continues to develop new statistical methods for survival analysis, missing data, measurement error problems, high dimensional data, biomarker evaluations, and longitudinal […]

Dr. Zheng is an assistant professor of Biostatistics. His research focuses on the development of novel statistical methods to address important scientific problems, and the application of sound statistical approaches […]

Dr. Shen is a Professor and the Interim Director of the Informatics Division in the Department of Biostatistics, Epidemiology and Informatics at the Perelman School of Medicine in the University […]

Dr. Shou’s methodological research focuses mainly on developing novel functional data analysis and machine learning models for multimodal and longitudinal imaging measures, wearable computing sensor data, and assessing biomarker reproducibility. […]

Dr. Wagenaar’s research is focused on the mechanisms of data integration, analysis and sustainable scientific collaboration through technology. The challenges of integrating complex scientific data are accompanied by a need […]

Joseph D. Romano is an Assistant Professor of Informatics and Pharmacology in the University of Pennsylvania’s Perelman School of Medicine. He joined the faculty in 2023 after completing postdoctoral and […]

Dr. Linn’s methodological research addresses barriers to incorporating complex, high-dimensional data into models for personalized medicine and biomarker development using neuroimaging data. As a co-investigator, she provides biostatistical leadership on […]

Dr. Long’s research program marries innovative data science, informatics, and ML/AI research with impactful biomedical research. His lab has focused on developing robust statistical and ML/AI methods and models for […]

In addition to his professorial role, Dr. Morris serves as Director of the Division of Biostatistics. His research interests focus on creating hybrid ML-AI/statistical tools for extracting knowledge from large-scale […]

Dr. Danielle Mowery is a collaborative investigator that develops natural language processing (NLP) solutions for processing clinical texts – i.e., clinical notes, chatbots, and transcribed texts – to support clinical […]

Dr. Jing Huang is an Associate Professor of Biostatistics in the Department of Biostatistics, Epidemiology, and Informatics at the Perelman School of Medicine, University of Pennsylvania. She is also a […]

Dr. Jin’s research focuses on developing statistical methods and computational tools to address cutting-edge problems in public health and medicine by integrating large-scale, multi-source datasets. Her research areas include health […]

Dr. Dokyoon Kim is an Associate Professor of Informatics and serves as the Director of the Center for AI-Driven Translational Informatics (CATI). He also holds the role of Associate Director […]

John H. Holmes, PhD, is Professor of Medical Informatics in Epidemiology at the University of Pennsylvania Perelman School of Medicine. He is the Associate Director for Education and Training and […]

Dr. Jinbo Chen is Professor of Biostatistics in the Department of Biostatistics, Epidemiology & Informatics at the University of Pennsylvania Perelman School of Medicine. Her research focuses on statistical methods […]

Dr. Yong Chen is Professor of Biostatistics and Informatics at Department of Biostatistics, Epidemiology, and Informatics (DBEI), and a Senior Scholar at Center for Clinical Epidemiology & Biostatistics (CCEB) at […]

Dr. Demiris is the Mary Alice Bennett University Professor in the School of Nursing and Perelman School of Medicine, and Associate Dean for Research and Innovation in the School of […]

Dr. Cao has established himself as a leader in the field of collaborative analytics, particularly in high-dimensional and imaging science. He has worked on problems in the study of the […]

Masking policies during Covid-19 have inspired plenty of political debate, but scientific evidence about the policies’ effects has been very limited. This isn’t surprising, given that it is not practical […]

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple […]

While many findings in neuroimaging studies pertain to multiple imaging modalities, statistical methods underlying intermodal comparisons have varied. In our recently published paper in Human Brain Mapping, we propose the […]

While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce […]

NEXT SEMINAR Time: Friday, May 14, 1:30pm-3pm EST Topic: Broader and deeper engagement in biomedical data science Moderator: Dr. Hongzhe Li, Vice Chair of Research Integration, DBEI Our panelists are: […]

The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using […]

The central vein sign is a promising MR imaging diagnostic biomarker for multiple sclerosis. Recent studies have demonstrated that patients with MS have higher proportions of white matter lesions with […]

As the field of neuroimaging grows, it can be difficult for scientists within the field to gain and maintain a detailed understanding of its ever-changing landscape. While collaboration and citation […]

Estimating scaled treatment effects with multiple outcomes Edward H Kennedy, Shreya Kangovi, Nandita Mitra Link In classical study designs, the aim is often to learn about the effects of a […]

Sensitivity analysis and power for instrumental variable studies Wang X, Jiang Y, Zhang NR, Small DS. Link In observational studies to estimate treatment effects, unmeasured confounding is often a concern. […]

A general approach to evaluating the bias of 2‐stage instrumental variable estimators Fei Wan, Dylan Small, and Nandita Mitra Unmeasured confounding is a common concern when researchers attempt to estimate […]

Bayesian nonparametric generative models for causal inference with missing at random covariates Jason Roy, Kirsten J. Lum, Bret Zeldow , Jordan D. Dworkin, Vincent Lo Re III, and Michael J. […]

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WML) in multiple sclerosis. While WML have been studied for over two decades using […]

With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, […]

Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer […]

MR imaging can be used to measure structural changes in the brains of individuals with multiple sclerosis and is essential for diagnosis, longitudinal monitoring, and therapy evaluation. The North American […]

A doubly robust approach for cost–effectiveness estimation from observational data Li, Vachani, Epstein, Mitra Estimation of common cost–effectiveness measures, including the incremental cost–effectiveness ratio and the net monetary benefit, is […]

A framework for Bayesian nonparametric inference for causal effects of mediation. Kim C, Daniels MJ, Marcus BH, Roy JA We propose a Bayesian non-parametric (BNP) framework for estimating causal effects […]

A Bayesian nonparametric approach to marginal structural models for point treatments and a continuous or survival outcome. Roy J, Lum KJ, Daniels MJ. Marginal structural models (MSMs) are a general […]