New Core Services in Data and Computational Sciences
Pat Janulis, PhD
Northwestern University
Anna Hotton, PhD
University of Chicago Medicine
Jonathan Ozik, PhD
The University of Chicago
November 2, 2020
12:00 PM - 1:00 PM
Livestream Only
DETAILS:
RSVP Required
ABSTRACT:
Are you interested in doing more with your data? Do you have ideas for studies that use “big data,” modeling, or machine learning, but aren’t sure where to start? Come hear experts from the CFAR Biostatistics and Computational Resources Team present examples of using these tools to enhance HIV research.
Patrick Janulis, PhD, is an assistant professor in the Department of Medical Social Sciences at Northwestern University and serves as the quantitative methodologist for the Third Coast Center for AIDS Research. His research examines the intersection of HIV, drug use, and LGBTQ health. His early work focused on using latent variable modeling to improve the measurement of HIV risk behavior and understanding within person variation in risk behavior across different environments and social circumstances. More recently this work has shifted to leveraging modern data science approaches to measure, understand, and intervene on the root causes in the spread of HIV.
Anna Hotton, PhD is a research assistant professor in the Section of Infectious Diseases and Global Health at the University of Chicago, and an epidemiologist with expertise in complex study design and quantitative methods. Dr. Hotton applies computational modeling approaches to advance understanding of mechanisms by which structural barriers impact HIV related outcomes among diverse groups, including MSM and transgender women. Past and ongoing work has involved evaluation of primary and secondary prevention interventions, and studies aimed at understanding psychosocial and contextual influences on risk behavior, PrEP uptake, and engagement and retention in care.
Jonathan Ozik, PhD is a computational scientist at Argonne National Laboratory and senior scientist in the Consortium for Advanced Science and Engineering at the University of Chicago. Dr. Ozik develops applications of large-scale agent-based models, including models of infectious diseases, healthcare interventions, biological systems, water use and management, critical materials supply chains, and critical infrastructure. He also applies large-scale model exploration across modeling methods, including agent-based modeling, microsimulation and machine/deep learning. Dr. Ozik leads the Repast project (repast.github.io) for agent- based modeling toolkits and the Extreme-scale Model Exploration with Swift (EMEWS) framework for large-scale model exploration capabilities on high performance computing resources.
