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.
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.
The mission of the Center for Causal Discovery (CCD) is to develop, make available, and apply graphical causal discovery methods to help discover valid, novel, and significant causal relationships from big biomedical data that lead to new biomedical insights.
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.
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.
PRoBE Laboratory for Pattern Recognition from Biomedical Evidence
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.
Please contact the PRoBE lab PI Vanathi Gopalakrishnan for more information or if you would like to join.
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.
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.
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.
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.