/ 2025 I2DB Scientific Symposium: Bridging Causal Inference and Data Science to Advance Medicine

2025 I2DB Scientific Symposium: Bridging Causal Inference and Data Science to Advance Medicine

February 12, 2025
11:00 am - 5:00 pm
Eric P. Newman Education Center (EPNEC), 320 S. Euclid, St. Louis, Mo 63110

Join us for the I2DB Annual Scientific Symposium on Wednesday, Feb. 12, 2025. This year’s event will feature a keynote address by Miguel Hernán, MD, DrPH, focusing on formulating well-defined questions for actionable causal inference and recognizing the complementarity of randomized and observational studies. The symposium will also include research presentations and opportunities for networking with leading experts in the field. Don’t miss the chance to share insights and inspire future discoveries!

Join us in person or on Zoom:

https://wustl-hipaa.zoom.us/j/96155187692?pwd=Q3Nbc1zzQu5UqRiDJme67eI3HL7bMT.1


Agenda

Registration

11 a.m.

Lunch

11:30 a.m.

Lightning Talks

12:30 p.m.

Lightning Talk Presenters
Linying Zhang, PhD

Linying Zhang, PhD

Assistant Professor of Biostatistics

Real-world evidence generation in federated health data networks: and OHDSI approach

Dr. Linying Zhang is an Assistant Professor of Biostatistics at WashU Medicine. Her lab integrates causality with machine learning and artificial intelligence to achieve reliable real-world evidence generation from electronic health records data. She obtained her PhD in Biomedical Informatics from Columbia University and Master’s degree in computational biology from Harvard School of Public Health.

Andrew  P. Michelson, MD

Andrew P. Michelson, MD

Assistant Professor of Medicine, Division of Pulmonary and Critical Care Medicine

Bits and Bytes at the Bedside: AI’s Precision Promise in Critical Care

Dr. Michelson is an Assistant Professor in the Department of Medicine and Division of Pulmonology and Critical Care Medicine where is also serves as Director for Critical Care Informatics Research. He is a practicing critical care physician and physician-scientist with research interests in critical care outcomes, clinical informatics, and healthcare technology. 

Fuhai Li, PhD

Fuhai Li, PhD

Associate Professor of Pediatrics School of Medicine

Explainable Graph AI to Decode Complex Cell Signaling Systems

Dr. Fuhai Li is an Associate Professor in the Institute for Informatics, Data Science and Biostatistics (I2DB), and Department of Pediatrics, Washington University in St. Louis (WUSTL). He jointed I2DB since 2018 from OSU. He received Ph.D. in applied mathematics (information science) and had his pre-doctoral and postdoctoral training in the field of bioinformatics and computational biology at Harvard Medical School and Houston Methodist Research Institute.  

His research focuses on modeling and interpreting large-scale complex graphs, including the development of interpretable graph AI (xGAI) models, the generation of large-scale graph datasets, and the creation of graph foundation models and joint graph-language (LLM) models for graph understanding and reasoning. He applies these graph AI models to multi-omic data-driven studies to identify disease targets, infer signaling pathways, and predict drugs and treatment combinations for treating and preventing complex diseases such as cancer, Alzheimer’s Disease (AD), lysosomal diseases, and longevity. 

Laura Weis

Laura Weis

Project Archivist, Bernard Becker Medical Library

Revolutions in Biomedical Computing: The Washington University Computer Laboratories

Dr. Laura Weis is a project archivist in the Archives and Rare Books Division of the Bernard Becker Medical Library at WashU Medicine, where she has focused on processing the papers of biomedical computing pioneer Jerome R. Cox, Jr., and the records of the Institute for Biomedical Computing and its forerunners. She holds a PhD in History and Peace Studies from the University of Notre Dame, where she studied the relationship between culture and foreign relations.

Bohigian Lectureship in Biomedical Informatics

1 p.m.

Miguel Hernán, MD, DrPH, presents “Target trial emulation (because not all questions about comparative effectiveness can be answered by randomized trials).”

Learning objectives
  1. Formulate well-defined questions for actionable causal inference
  2. Identify why observational analyses fail or succeed
  3. Recognize the complementarity of randomized and observational studies
Keynote Speaker: Miguel Hernán, MD, DrPH
Miguel Hernán, MD, DrPH
Professor and Director of CAUSALab
Harvard University

Miguel Hernán uses health data and causal inference methods to learn what works. As Director of CAUSALab at Harvard, he and his collaborators repurpose real world data into evidence for the prevention and treatment of infectious diseases, cancer, cardiovascular disease, and mental illness. This work has contributed to shape health policy and research methodology worldwide.

Miguel is co-director of the Laboratory for Early Psychosis (LEAP) Center, principal investigator of the HIV-CAUSAL Collaboration, and co-director of the VA-CAUSAL Methods Core, an initiative of the U.S. Veterans Health Administration to integrate high-quality data and explicitly causal methodologies in a nationwide learning health system.

As the Kolokotrones Professor of Biostatistics and Epidemiology, he teaches at the Harvard T.H. Chan School of Public Health, where he has mentored dozens of trainees, and at the Harvard-MIT Division of Health Sciences and Technology. His free online course “Causal Diagrams” and book “Causal Inference: What If”, co-authored with James Robins, are widely used for the training of researchers.

Miguel has received several awards, including the Rousseeuw Prize for Statistics, the Rothman Epidemiology Prize, and a MERIT award from the U.S. National Institutes of Health. He is Fellow of the American Association for the Advancement of Science and the American Statistical Association, and Associate Editor of Annals of Internal Medicine. He is a former Special Government Employee of the U.S. Food and Drug Administration, Editor Emeritus of Epidemiology, and past Associate Editor of Biometrics, American Journal of Epidemiology, and Journal of the American Statistical Association.

Panel Discussion

2 p.m.

Panelists

Sandro Galea, MD, DrPH
Inaugural Margaret C. Ryan Dean, Planned School of Public Health, WashU

Philip R.O. Payne, PhD, FACMI, FAMIA, FAIMBE, FIAHSI
Director, Institute for Informatics, Data Science and Biostatistics (I2DB)
Janet and Bernard Becker Professor
Associate Dean for Health Information and Data Science
Chief Data Scientist
Chief Health AI Officer
WashU Medicine

Betsy Sinclair, PhD
Chair of Political Science
Professor of Political Science
Thomas F. Eagleton University Professor of Public Affairs and Political Science
WashU

Datathon Presentations and Awards

3 p.m.

I2DB Datathon: Causal Risk Prediction in Medicine

This Datathon focuses on building risk prediction models using synthetic medical data (IRB preapproved). Participants will receive a training dataset at the start of the event and will have six weeks to develop their models using causal inference and machine learning methods to analyze the synthetic data. The challenge culminates in a presentation at the I2DB Annual Symposium, where finalists will present their work to a panel of judges. Final placements will be determined based on model performance, innovation, and the quality of the presentation. 

Participants include (team name followed by team captain and fellow team members):

Fitness Freaks Sandhya Tripathi, Joshin kumar

Portal Dyad Yazan Rouphail, Sam Fallon

1966 Chicago Bulls Levi Kaster, Will Powell, Jonathan Moran Sierra, Lars Schimmelpfenning

The symposium planning committee members (see dropdown below) will be judging the datathon.

Reception and poster session

4:00 p.m.

Poster Presenters

Mark A. Fiala, PhD, MSW Assistant Professor, Division of Oncology

Steven Hartman, PhD Candidate, Computational and Systems Biology

Ethan Hillis, MS, I2DB

Levi Kaster, PhD student, I2DB

Evan Lee, MD/PhD Student, Edison Family Center for Genome Sciences and Systems Biology, Washington University

Lili Liu, PhD Postdoc Research Associate, Division of Biostatistics

Bailey Wellen Osweiler, PhD Student, Division of Computational and Data Sciences, McKelvey School of Engineering

William JB Powell, MS, I2DB

Mengyao Shi, PhD Student, Biomedical Informatics and Data Science, Division of Biology and Biomedical Sciences

Yiming Shi, Biomedical Informatics and Data Science

Daoyi Zhu, PhD Student, Computer Science & Engineering, McKelvey School of Engineering


Symposium Committee Members

Committee Co-chairs
Charles Goss, PhD

Charles Goss, PhD

Director, Center for Biostatistics and Data Science
Assistant Professor of Biostatistics

Aditi Gupta, PhD

Aditi Gupta, PhD

Assistant Professor of Biostatistics

Committee Members
Mackenzie Hofford, MD

Mackenzie Hofford, MD

Associate Chief Research Information Officer, School of Medicine
Assistant Professor of Medicine Division of General Medicine School of Medicine

Fuhai Li, PhD

Fuhai Li, PhD

Associate Professor of Pediatrics School of Medicine

Adam Wilcox, PhD, FACMI

Adam Wilcox, PhD, FACMI

Director, Center for Applied Health Informatics
Professor of Medicine, Division of General Medicine & Geriatrics