Centers & Labs

Madhavi Ganapathiraju‘s ACT (Algorithms for Computational and Translational) Biomedicine Lab focuses on applying machine learning and signal processing algorithms for Computational Systems Biology. Specifically, the team is interested in discovering protein-protein interactions. They also work on predicting protein function and cellular localization. Core areas of specialization of students working in this group are machine learning and/or signal processing, and they come from the Department of Biomedical Informatics Training Program, the Intelligent Systems Program, the Joint CMU-Pitt PhD Program in Computational Biology, or internships through the TECBio Research Experiences for Undergraduates Program (www.tecbioreu.pitt.edu) or First Experiences in Research Program, at University of Pittsburgh.

Center for Clinical Research Informatics (CCRI)

The Center for Clinical Research Informatics (CCRI) is in the Department of Biomedical Informatics at the University of Pittsburgh and is directed by Shyam Visweswaran, MD, PhD. CCRI oversees the development of a research data warehouse (Neptune), electronic health record data provisioning for large national initiatives, and development of ontologies for the national Accrual to Clinical Trials (ACT) network.

The CCRI is an integral component of several national research networks that include: 1) the Accrual to Clinical Trials (ACT) network that is funded by the National Clinical and Translational Science Award (NCATS), 2) the PaTH network (University of Pittsburgh / UPMC, Penn State, Temple University, John Hopkins University, the Ohio State University and University of Michigan) that is funded by the Patient Centered Outcomes Research Institute (PCORI), 3) the National Mesothelioma Virtual Bank (NMVB) is a virtual biospecimen registry designed to support and facilitate research that in mesothelioma, 4) the All of Us Research Program that is a landmark longitudinal research effort that aims to engage 1 million or more U.S. participants to revolutionize how disease is prevented and treated based on individual differences in lifestyle, environment and genetics, and 5) the Genomic Information Commons (GIC) that is funded by NCATS.

As an inaugural member of the NIH Big Data to Knowledge (BD2K) Consortium, the Center for Causal Discovery (CCD) will:

  • Develop highly efficient causal discovery algorithms that can be practically applied to very large biomedical datasets
  • Conduct projects addressing 3 distinct biomedical questions (cancer driver mutations, lung fibrosis, brain causome) as a vehicle for algorithm development and optimization
  • Disseminate causal discovery algorithms, software, and tools
  • Train data scientists and biomedical investigators in the use of CCD tools
  • Train data scientists and biomedical investigators to collaborate in the discovery of causality

Led by Drs. Gregory Cooper, Ivet Bahar, and Clark Glymour, the Center represents a partnership among data scientists from the University of Pittsburgh (Pitt), Carnegie Mellon University (CMU), and the Pittsburgh Supercomputing Center (PSC) who will develop the algorithms, software, and system architecture needed by biomedical scientists seeking to discover and represent causality using their large and diverse data sets. We are joined by collaborators from Yale University, California Institute of Technology, Rutgers University, Stanford University, the University of Crete, and the University of North Carolina. We receive guidance and insight from an exceptional External Advisory Board.

Scientists are invited to explore CCD tutorials and projects both within the Center and with other BD2K Consortium members to find what is needed to start discovering new causal knowledge in their own data. See http://www.ccd.pitt.edu/ for more information.

UPMC Hillman Cancer Center Academy

 

Our Mission

The UPMC Hillman Cancer Center Academy, previously the UPCI Academy, strives to provide cutting edge research and career preparatory experiences to a diverse group of highly motivated high school students who are pursuing higher education and careers in STEM fields, especially research and medicine.

Our Goals

1) Provide authentic research and mentorship opportunities to diverse group of students to broaden participation in STEM.
2) Increase the quality and diversity of the biomedical workforce.
3) Expose diverse student population to quality career and academic preparatory experiences.

Faculty Researcher:

David Boone

PRoBE

 

Mission and Goals:

To harness prior knowledge for effective knowledge discovery from biomedical data.

To design and develop novel machine learning algorithms using symbolic, probabilistic and hybrid approaches to solve bioinformatics problems of clinical importance such as biomarker discovery and disease classification.

To develop complex pattern recognition tools that can be plugged into computer-aided diagnostic systems to facilitate evidence combination from heterogeneous sources such as data from imaging, de-identified clinical information and biochemical profiling.

 

Faculty Researcher:

TRIADS

 

The TRanslational Informatics Applied to Drug Safety (TRIADS) lab focuses on the design and evaluation of intelligent clinical decisions, support interventions, distributed and collaborative knowledge bases, and advanced pharmacovigilance signal detection management. Students in the lab navigate the challenging interactions that occur between intelligent systems development and clinical use cases. The lab has applied advanced computational methods to address difficult challenges such as detecting bleeding adverse drug events, automatically identifying drug safety signals, intelligent drug-drug interaction alerting, and identifying patients who have an addressable risk of falls.Research currently happening in the lab includes the application of knowledge graphs and machine learning to pharmacovigilance of natural products, machine learning informed clinical decision support, and shareable knowledge artifacts for clinical decision support that are highly specific to individual patients.The lab includes a talented group of researchers at various levels of training including Deaf and Hard of Hearing scientists. We have been working on novel approaches for helping Deaf and Hard of Hearing scientists advance in their training. .

Please contact the lab PI Dr. Richard Boyce for more information or if you would like to attend TRIADS lab meetings.

Faculty Researcher:

RODS—Real-time Outbreak and Disease Surveillance Laboratory

The RODS Laboratory is a biosurveillance research laboratory at the University of Pittsburgh, Department of Biomedical Informatics.  We are the home of the National Retail Data Monitor (NRDM), Pennsylvania RODS and the Real-time and Outbreak Surveillance Software.

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:

Yalini Senathirajah

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:

BATMAN Lab

A Message from Kayhan Batmanghelich:

 

I am an Assistant Professor of the Department of Biomedical Informatics with a secondary appointment at the School of Computing and Information at the University of Pittsburgh. My research is at the intersection of medical vision (medical image analysis), machine learning, and bioinformatics. I develop algorithms to analyze and understand medical images, genetic data, and other electrical health records such as clinical reports. The main themes of research in my lab are about the main challenges of AI in healthcare: (1) Explainability, (2) Data Efficiency, (3) Multimodal Data Fusion and Causality. My lab works on Alzheimer’s Disease, Chronic Obstructive Pulmonary Disease (COPD), and Non-Alcoholic Fatty Liver Disease (NAFLD) projects. Our research is supported by funding from NIH, NSF, and industry awards.

Wright Lab

 

The Wright Lab uses experimental and computational approaches to study how microorganisms fight for survival. In particular, we seek to tackle the problem of antibiotic resistance through understanding the evolution of both antibiotic-producing microbes and antibiotic-resistant pathogens.

Five fundamental questions motivate research in the lab:

  • How do naturally antibiotic-producing bacteria ward off resistance?
  • How do genomes evolve to compensate for newly acquired traits?
  • What is the optimal strategy for treating bacterial pathogens in the clinic?
  • Can we learn to speak the language of the microbiome?
  • What insights can be gleaned from millions of microbial genomes?

Our lab is composed of interdisciplinary scientists who complement computing with laboratory experiments. We have open positions for postdocs, graduate, and undergraduate students with interests in evolution, microbiology, bioinformatics, and quantitative biology.

Faculty Researcher:

Erik Wright

Osmanbeyoglu Lab

 

We are a multi-disciplinary hybrid wet/dry lab at the University of Pittsburgh and affiliated with the Department of Biomedical InformaticsBioengineering and UPMC Hillman Cancer Center. The primary focus of our group is developing integrative statistical and machine learning approaches for extracting therapeutic insight from highly heterogenous omic datasets, clinical and drug response data for the purpose of precision medicine. Our projects are in the areas of systems biology, epigenetics, and immunology and are executed through multi-disciplinary collaborations.