First Penn Conference on Big Data in Population Health Sciences

First Penn Conference on Big Data in Population Health Sciences

If you would like to be added to a wait list, please contact Janine Pritchard.

How can we leverage biomedical data sciences to transform population health and medicine? For every kind of challenge, big data methodology is key. The First Penn Conference on Big Data in Population Health Sciences, Sept. 23 and 24, at the Gaulton Auditorium of Perelman School of Medicine's Biomedical Research Building, 421 Curie Boulevard, Philadelphia, will offer insights from thought leaders in eight influential areas of big data.

Attendees are invited to submit abstracts and present posters about their related work during the Sept. 23 reception. Once you have registered for the conference, you may indicate your interest and submit your abstract here. We can accept a limited number of posters—first come, first served.

Accommodations: A limited number of reduced-rate rooms are currently available at the nearby Sheraton University City hotel, 3549 Chestnut St.

This event is co-presented by the Perelman School of Medicine's Department of Biostatistics, Epidemiology and Informatics; Center for Statistics in Big Data; Institute for Biomedical Informatics; Penn Center for Precision Medicine; and Center for Clinical Epidemiology and Biostastistics; and by the Penn Center for Cancer Care Innovation at the Abramson Cancer Center.


8 A.M. Registration and breakfast

8:45 A.M. Opening remarks

9 A.M. EHR, Medicare Data and Large Electronic Databases
David Madigan, PhD, Columbia University
Large-Scale Evidence Generation in a Network of Databases
Jinbo Chen, PhD, University of Pennsylvania
An Estimating Equation-Based Approach to Accounting for Case Contamination in EHR-Based Case-Control Studies
Mitesh Patel, MD, MBA, MS, University of Pennsylvania
Using Nudges in the Electronic Health Record to Improve Health Care

11 A.M. Big Data in Health Care Policy and Behavioral Economics
Ziad Obermeyer, MD
, University of California, Berkeley
Amol Navathe, MD, PhD, University of Pennsylvania

12 P.M. Lunch

1:30 P.M. mHealth, Digital Health and Social Media
Rosalind Picard, ScD, FIEEE, Massachusetts Institute of Technology
How Mobile Phones and Wearables Can Improve Mood, Stress and Health
Lyle Ungar, PhD, University of Pennsylvania:
Understanding Health Behaviors Using Social Media Language
Ian Barnett, PhD, University of Pennsylvania
Neural Networks for Clustered and Longitudinal Data

3:30 P.M. Biobanks in Genetics Research
Xihong Lin, PhD
, Harvard University
Empowering GWAS Analysis Using Surrogate Phenotypes in Biobanks
Marylyn Ritchie, PhD, University of Pennsylvania
The Power of Electronic Health Records for Genomics
Nilanjan Chatterjee, PhD, Johns Hopkins University
Leveraging Big Data in Genetic Risk Prediction and Causal Inference

5 P.M. Reception and poster session


8 A.M. 

9 A.M. Big Data in Biomedical Imaging and Neurosciences
Daniel Rueckert, PhD
, Imperial College, London
A new frontier — UK Biobank, Machine Learning & Artificial Intelligence
Russell T. Shinohara, PhD, University of Pennsylvania
Big Data and Imaging Statistics
Li Shen, PhD, University of Pennsylvania
Brain Imaging Genomics — Integrated Analysis and Machine Learning

11 AM. Big Data, Reproducibility and Privacy
Bradley Malin, PhD, FACMI, Vanderbilt University
Big Privacy for Big Populations
Casey Green, PhD, University of Pennsylvania
Strategies to Study Rare Diseases with "Big Data"

12 P.M. Lunch

1 P.M. Big Spatial Data in Population Health Studies
Lance Waller, PhD, Emory University
Zoonotic Surveillance of Plague via Point Processes in Geographic and Principal Components Space
Charles Branas, PhD, Columbia University
Promises and Pitfalls of the Geographic Health Datapalooza
Douglas Wiebe, PhD, University of Pennsylvania
Examples of Big Data in Injury Science

3 P.M. Wearable Device Data
Ciprian Crainiceanu, PhD, Johns Hopkins University
Biostatistical Methods for Wearable and Implantable Technologies
George Demiris, PhD, FACMI, University of Pennsylvania
Smart Homes and Smart Communities to Support Aging — the Potential of Behavioral Sensing Data

4 P.M. Concluding remarks

For further general information, contact Janine Pritchard.

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