Biomedical Informatics research covers a broad spectrum of inquiry – from the analysis of genomic microarray datasets to the evaluation of hospital organizations during the adoption of new technology. This spectrum reflects the many facets of Biomedical Informatics, which can be defined as “the scientific field that deals with biomedical information, data, and knowledge – their storage, retrieval, and optimal use for problem-solving and decision-making.” (Shortliffe & Blois, 2001). Below we present our faculty’s major funded research areas. We welcome your ideas for collaboration and invite individuals interested in training with us to contact us for further information.

Areas of Research

Clinical & Translational Informatics

Applications of informatics and data science principles and methods to direct patient care, such as advanced clinical decision support systems and multimedia electronic health records, to the provision of informational support to health care consumers.


Applications of informatics and data science principles and methods to support ‘bench to bedside to practice’ translational research, such as genome-phenome relationships, pharmacogenomics, or personalized medicine.

Imaging Informatics

Applications of informatics and data science principles and methods to support how medical images are used and managed in biology and medicine.

Population Informatics

Applications of informatics and data science principles and methods to build integrated resources for population research, for decision support in public health agencies, to support regional or global health research, or syndromic surveillance.

Data Warehousing & Informatics Resources

Applications of informatics and data science principles and methods to support secondary research use of clinical data, to manage information related to clinical trials, or to manage data for biological and clinical research.

Our Projects


The National Mesothelioma Virtual Bank (NMVB) is a virtual biospecimen registry designed to support and facilitate basic science, clinical, and translational research that will advance understanding of mesothelioma pathophysiology with the goal of expediting the discovery of preventive measures, novel therapeutic interventions, and ultimately, cures for mesothelioma. The current participants in the virtual bank are University of Pittsburgh, University of Pennsylvania, NYU Langone Medical Center, Roswell Park Cancer Institute and University of Maryland.
This work is funded by grant U24 OH010873 from NIOSH, NIH.

PaTH Network

PaTH is a Patient Centered Outcomes Research Institute (PCORI) Clinical Data Research Network project which is focused on building a Learning Health System (LHS) for the Mid-Atlantic region. It is comprised of Geisinger Health System, Johns Hopkins University, Johns Hopkins Health System, Penn State College of Medicine, Penn State Milton S. Hershey Medical Center, Temple Health System, Lewis Katz School of Medicine at Temple University, the University of Pittsburgh, UPMC, the University of Utah, and University of Utah Health Care. the University of Pittsburgh leads the informatics component of PaTH.
This work is funded by grant CDRN 1306-04912 from PCORI.


The Cellular Senescence Network (SenNet) Program was established to comprehensively identify and characterize the differences in senescent cells across the body, across various states of human health, and across the lifespan. SenNet will provide publicly accessible atlases of senescent cells, the differences among them, and the molecules they secrete, using data collected from multiple human and model organism tissues. To identify and characterize these rare cells, SenNet will develop innovative tools and technologies that build upon previous advances in single cell analysis. Lastly, SenNet aims to unite cellular senescence researchers by developing common terms and classifications for senescent cells.

Our Centers and Labs

Senathirajah Lab

Our long-term goal is to understand the impact of interaction design in health information technology on medical cognition, human-computer interaction/efficiency, and system development. In the shorter term, we are identifying design patterns that reduce usability problems in electronic health records. Our approach is to provide a simplifying technology platform. The strength of our approach is its decision to give the nonprogrammer clinician end-user far greater control to design, gather, mashup, visualize and share clinical information in multiple ways.


Faculty Researcher:

The Vis Lab

The Vis Lab is focused on the application of artificial intelligence and machine learning to problems in the Learning Health System (LHS) that include: 1) development of a learning Electronic Medical Record (LEMR) system, 2) precision medicine and personalized modeling, 3) reuse of Electronic Medical Record (EMR) data for clinical, translational, and informatics research, 4) data mining and causal discovery from biomedical data, and 5) automated visual analytics.


Faculty Researcher:


The Center for Clinical Artificial Intelligence (CCAI) is in the Department of Biomedical Informatics at the University of Pittsburgh and is directed by Shyam Visweswaran, MD, PhD. CCAI focuses on developing, implementing, and evaluating high performance clinical decision support (CDS) tools that are powered by artificial intelligence (AI) including machine learning (ML). AI has the potential to support, enable and improve medical decision-making to make it faster, accurate, and economical. In particular, AI-enabled predictions, monitoring, alerting will power the next generation of CDS.


Faculty Researcher: