Biographical Info

Titles:

Associate Professor, Department of Biomedical Informatics
Associate Professor, Intelligent Systems Program
Associate Professor, Clinical and Translational Sciences
Member, UPMC Hillman Cancer Center
Director, Biomedical Informatics Training Program
University of Pittsburgh School of Medicine
Pitt Cyber Affiliate Scholar
ISP
CTSI
UPMC Hilman Cancer Center

Biography

My research takes a broad perspective on the challenge of making biomedical data more useful and informative to clinicians, researchers,  public health officials, and individuals.  Drawing on my training in Computer Science and Human-Computer Interaction, my work combines a human-centered design perspective with biomedical informatics techniques including machine learning, natural language processing,  knowledge representation, and data science. Reflecting the inter-disciplinary nature of these efforts, I have worked extensively in team science efforts including the management of craniofacial development data, development of tools for linking animal models to human phenotypes, and infectious disease modeling research. My current (as of July 2023) projects include:

• DeepPhe Cancer Deep Phenotype Extraction from Electronic Medical Records.To help researchers identify cohorts of patients matching research study criteria, we are developing Natural Language Processing tools for extracting longitudinal summaries of patient histories, along with web-based visualization tools for analyzing this information at the cohort and individual patient levels. We are also developing tools to apply these approaches to cancer registry patient summarization efforts. Funding: NCI Grants U24CA248010 and UH3CA243120.
• Bio-digital Rapid Alert to Identify Neuromorbidity (BRAIN-AI): In collaboration with colleagues at Children’s Hospital of Pittsburgh, we are developing predictive models capable of identifying children in the pediatric ICU who might be at risk of developing neurological problems. We are also working closely with clinicians to understand information needs and to design interfaces capable of providing actionable information. Funding: NINDS Grant R01NS118716. A companion project with this team is using similar techniques to identify inpatient children at risk of transition to the ICU.
• Models of Infectious Disease Agent Study (MIDAS) Coordination Center The MIDAS network is a consortium of infectious disease modeling research.  The MIDAS Coordination Center supports this community through webinars , meetings, guidance on data science and modeling best practices, and participation in community modeling efforts.  Funding:  NIGMS U24GM132013

I am also Director of the University of Pittsburgh’s Biomedical Informatics Training Program and Principal Investigator of the program’s T15 training grant (NLM T15LM007059).

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