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Program Director CPCB, Â鶹´å
Hebert A. Simon Professor of Computer Science, Computational Biology, Â鶹´å

• Designing ML, combinatorial, and optimization algorithms to extract insight from genomics data.

• I work to quantitatively understand the evolutionary architecture of intelligent, collective systems, using the tools of dynamical systems, network theory, population genetics, machine learning and statistical inference, and widely available, yet underused, datasets.

Program Director CPCB, Pitt
Associate Professor, Computational & Systems Biology, Pitt

• We develop computational algorithms and full-scale systems to support rapid and inexpensive drug discovery and apply these methods to develop novel therapeutics with a focus on integrating machine learning, deep neural networks, and structure-based biophysical approaches.

Program Director Emeritus CPCB, Pitt
Associate Professor, Computational & Systems Biology, Pitt

• Developing mathematical models of biological regulatory processes that integrate specific knowledge about protein-protein interactions. 

Associate Professor, Computational Biology, Â鶹´å

• The neurogenomics laboratory studies how genome sequence differences influence behavior and neurological disorder predisposition.

• The evolution of pathogens such as HIV and SARS-CoV-2 presents a major threat to public health. My group works to combat this threat by studying how pathogens evolve and how they interact with the immune system. We’re particularly interested in questions related to the predictability of evolution and coevolution of hosts and pathogens. For example, which strains of a pathogen will become dominant in the near future? Can we control evolution to prevent pathogens from escaping immune control or developing resistance to drugs? Our work combines mathematical modeling, data analysis, and collaborations with experimentalists and clinicians to pursue these questions.

Assistant Professor, Computational Biology, Â鶹´å

• I study problems at the intersections of human genetics, statistics, quantitative psychology, and applied mathematics.

Drew Bridges

Assistant Professor, Department of Biological Sciences, Â鶹´å

• Lab we use diverse approaches to study how bacteria make developmental decisions based on extracellular sensory information. Read about our  for more information.

• We develop new technologies to predict and model protein structures, their physical interactions, and substrates.

• We develop statistical and computational methods for analyzing bulk and single-cell multi-omics data and understanding complex diseases such as childhood asthma and age-related macular degeneration.

• We aim to establish comprehensive high-throughput multi-omics single-cell analysis including genome, epigenome, transcriptome, proteome, functional, and morphological methods. With large amounts of data collected from high-throughput single-cell multi-omics analysis, machine learning techniques can predict patient prognosis and suggest treatments for precision medicine.

Assistant Professor, Computational & Systems Biology, Pitt

• The goal of Dr. Chikina’s research is to use genome scale data to advance our understanding of how genes contribute to the function of a complex organism, in health and disease.

Professor, Chemistry, Pitt

• Research in the Chong lab involves the development and application of molecular simulation approaches to model a variety of biophysical processes.

Associate Professor, Department Biological Sciences, Pitt

• The Clark lab uses comparative genomics to construct genetic networks and infer the functions of genes and regulatory regions underlying key adaptive and health-related traits. 

Professor, Microbiology and Molecular Genetics, Pitt

• We study evolution-in-action in the laboratory, in infections, and in cancers using genomics to identify and ultimately predict adaptations.

Assistant Professor, Computational Biology, Â鶹´å

• As an interdisciplinary research laboratory, our goal is to make the paradigms of Machine Learning and Feedback Control accessible and readily applicable in Biology. We focus on developing computational/theoretical and experimental tools to facilitate this integration across various biological systems, ranging from single cell to multicellular levels.

Assistant Professor, Immunology & Computational & Systems Biology, Pitt

• Our research focuses on the development and use of machine-learning, high-dimensional statistical and topological network-analyses methods for biologically meaningful integration of multi-omic datasets. These analyses help us understand immune mechanisms in a wide range of contexts, encompassing both natural and vaccine-mediated immunity.

Associate Professor, Biological Sciences, Â鶹´å

• Computational molecular biology and computational genomics; especially, the evolution of genomic organization and function.

Associate Professor, Biological Sciences, Pitt

• Develop broadly applicable, innovative computer-aided drug design (CADD) techniques and apply those techniques to further infectious-disease, neurological, and cancer drug discovery.

Assistant Professor, Chemical Engineering, Chemistry, Â鶹´å

• The Gomes Group research program focuses on the development of new chemical reactions, catalysts, and materials using and developing state-of-the-art machine learning and automated synthesis.

Assistant Professor, Immunology, Pitt

• Our lab uses quantitative approaches to understand how cells process stimuli to determine the appropriate functional response.  Identifying the activating receptors, kinases and trascription factors that make up signaling pathways is necessary but not sufficient to predict how a cell will respond.

Associate Professor, Computational & Systems Biology, Pitt

• We specialize in solving problems at the interface of medicine, biology, and engineering, using computational modeling and statistical thermodynamics methods. Our research interests include (i) protein systems including known and potential drug targets and (ii) proteins with complex functional machinery, comparable to macro scaled machines we encounter in daily life.

Assistant Professor of Medicine, Division of Geriatric Medicine, Aging Institute, Pitt

• Understanding the biology of aging and age-related diseases with focuses on identifying the signaling mechanisms that drive aging in response to endogenous DNA damage. By defining these molecular mechanism(s), she hopes to identify novel therapeutic targets that can be exploited to extend healthspan.

Assistant Professor, Department of Immunology, Pitt

Assistant Professor, Department of Biological Sciences, Â鶹´å

• The Henninger Lab explores how RNA regulates gene expression at multiple scales in health and disease. We use approaches based in molecular biology, soft-matter physics, and super-resolution imaging to understand how RNA regulates its own production to direct cell fate and how RNA-mediated dysregulation contributes to human disease.

Associate Professor, Chemistry, Â鶹´å

• Theoretical and computational chemistry, machine learning, cheminformatics, drug discovery, computer-aided molecular design, materials informatics

Assistant Professor, Computational & Systems Biology, Pitt

• Synthetic morphogenesis of 3D tissues. Brain and cardiac organoids. We integrate high-throughput 3D imaging, genome engineering, and pharmacology to control cell fate and tissue morphology.

Assistant Professor, Biological Sciences, Â鶹´å
Computational Biology, Â鶹´å

• The Kaplow Lab leverages genomics experiments and develops machine learning methods to decipher transcriptional regulatory mechanisms involved in mammalian phenotype evolution.

Associate Professor, Computational & Systems Biology, Pitt

• We model epigenomic marks during differentiation and development and build methods to elucidate the role of transcriptional enhancer sequences in vertebrate left-right patterning.

Professor of Medicine, Rheumatology, Pitt

• Our laboratory effort is focused on understanding scleroderma (systemic sclerosis), and developing novel therapeutic approaches based on identifying biomarkers of the disease process and utilizing biomarkers in clinical trials.

Professor, Department of Pharmacology and Chemical Biology, Pitt

Associate Professor, Computational & Systems Biology, Pitt

• We use single cell experiments and mathematical models to understand how cells process information to make cell fate decisions.

Assistant Professor, Department of Immunology

• The Lee laboratory delves into the immunological mechanisms governing cellular interactions within the tissue microenvironment. Our focus is to understand how these intricate dynamics dictate disease progression or resolution, particularly in autoimmune diseases and cancer. Our overarching objective is to establish a causal understanding between immune responses and the spatial organization of the cellular networks within the tissue landscape.

Assistant Professor, Language Technologies Institute, Â鶹´å

Assistant Professor, Pathology, Pitt

• Our lab is interested in bioinformatics and biostatistics analysis on high-throughput genomic data, such as multi-omics Microarray and sequencing data.

Assistant Professor, Computational & Systems Biology, Pitt

• Developing embryos must orchestrate the fates and movements of their cells with precision. However, precise control is no easy feat; genetic mutations, unexpected environmental perturbations and noisy signaling all threaten to scramble communication. Despite these challenges, development is remarkably robust. Our lab tackles these challenges with a combination of optogenetic manipulation, quantitative microscopy, computational modeling and classical embryology. Over the long run, we hope to learn the mechanistic principles that enable embryos to avoid and correct errors in development.

Professor, Biomedical Informatics, Pitt

• Computational methods to identify signaling pathways underlying biological processes and diseases as well as statistical methods for acquiring knowledge from biomedical literature.

Assistant Professor, Computational Biology, Â鶹´å
Co-Director of MSAS Program, Â鶹´å

• Developing the next generation of mechanism-driven computational methods to perform learning, inference, and decision-making on biomedical and health care data to accelerate scientific knowledge discovery and generate confident and testable data-driven discoveries.
  
  

Ray and Stephanie Lane Professor of Computational Biology, Â鶹´å

• Developing novel algorithms to study genome structure and function, chromatin and nuclear genome organization, and gene regulation in mammalian genomes as well as in cancer.

Assistant Professor, Pitt

• I’m a translational systems biologist with a focus on breast and ovarian cancer biology. My lab is guided by a lens of cancer prevention, integrating functional and computational approaches to drive progress in precision prevention and precision treatment across the clinical spectrum.

Assistant Professor of Medicine, Pitt

• Cardiovascular disease in CVD is the leading cause of mortality in the developed world and contributes to 1/3rd of all-cause mortality in the United States. Cardiac fibrosis is a key pathological feature of most cardiovascular diseases. Excessive deposition of extracellular matrix proteins results in adverse cardiac remodeling, loss of compliance and cardiac function, leading to heart failure. Macrophages are the primary immune cells in the heart and play a wide variety of roles in cardiac physiology and CVD. In response to environmental stimuli, macrophages can mediate pathological inflammatory processes or reparative tissue remodeling, causing alterations in collagen turnover, cardiomyocyte stiffness and altered cardiac metabolism. My work focuses on macrophage fibroblast paracrine signaling events that mediate cardiac fibrosis in cardiovascular disease. We are also interested in taking a systems approach to understand inter organ crosstalk and inflammatory signaling in the pathology of heart failure with preserved ejection fraction (HFpEF).

Professor and Vice Chair, Pharmacology and Chemical Biology, Director of Education at Womens Cancer Research Center, Pitt

• Molecular mechanism and clinical relevance of endocrine response in breast cancer

Assistant Professor, Biomedical Informatics, Pitt

• Our research focuses on developing data-driven computational approaches to understand disease mechanisms in order to assist in the development of personalizing anticancer treatments.

Professor & Department Head Machine Learning, Â鶹´å
Professor, Language Technologies Institute, Computer Science, Computational Biology, Â鶹´å

• The long-term vision of our DELPHI research group is to make epidemiological forecasting as universally accepted and useful as weather forecasting is today.

Professor & Chair, Computational & Systems Biology, Pitt

• Our research team engages in both experimental and computational genomic technology development. We have a major focus on comprehensively measuring the functional impact of human variants and how these impacts depend on environmental and genetic context, and on training the next generation of context-dependent variant effect predictors. A second focus is on systematically mapping and analyzing protein interaction networks and network dynamics across environmental contexts.

Professor & Department Head Computational Biology, Â鶹´å
Professor, Biological Sciences, Â鶹´å

• Computational genomics, population genetics, and phylogenetics, cancer heterogeneity and progression, computational biophysics, simulation and model inference of complex reaction networks.

Director, Center for Systems Immunology, Pitt
Professor, Immunology, Pitt

• The analysis of transcription factors and gene regulatory networks that regulate the development and functioning of innate and adaptive cells of the immune system.

Assistant Professor, Computational & Systems Biology, Pitt

• Cell cycle dysregulation is a hallmark of every tumor. My lab combines experimental and computational approaches to study how the cell cycle changes during tumorigenesis, metastasis, and drug treatment. We specialize in the use of spatial proteomic imaging and quantitative image analysis to study the cancer cell cycle in its native tumor microenvironment. Ultimately, our goal is to predict disease outcomes and therapeutic success by looking directly at the phenotype driving tumor growth — the cancer cell cycle.

Professor and Vice Chair for Research, Department of Biostatistics, Pitt
Professor, Human Genetics, Computational & Systems Biology, Pitt

• We develop rigorous, timely, and useful statistical and computational methodologies to understand disease mechanisms and improve disease diagnosis and treatment.

Assistant Professor, Computational & Systems Biology, Pitt

• We study cancer systems biology of tumor microenvironments at multiple scales by integrating high-dimensional microscopy, imaging and data science, and systems and bioinformatics approaches.

Associate Professor, Pharmacuetical Sciences, Pitt

• Computational Chemistry and Biophysics, Drug Discovery and Development, Systems Pharmacology and Outcomes, specifically

Assistant Professor, Machine Learning Department, Â鶹´å

• Our research focuses on AI for equitable, data-driven decision making in high-stakes social settings, integrating methods from machine learning, optimization, and causal inference.

*Currently on leave of absence

Professor, Machine Learning, Language Technologies Institute, Computer Science, Â鶹´å

• We develop machine learning, statistical methodology, and computational systems for solving problems of learning, reasoning, and decision-making in artificial, biological, and social systems.

Professor, Computational & Systems Biology, Pitt

• The lab uses theory and data-driven computational modeling, as well as single cell techniques including live cell imaging, sequencing, and other approaches to study the fundamental questions how a cell regulate and maintain its phenotype, and how a phenotypic transition proceeds and can be controlled.

Associate Professor, Language Technologies Institute, Â鶹´å

• My recent work focuses on foundation and large language models, with particular emphasis on improving the speed–quality trade-offs in pretraining, exploring new scaling frontiers, and enabling new capabilities for next-generation GenAI applications. Current directions in my group include: Foundation Model Science, GenAI-Native Information Retrieval, New GenAI-Enabled Scenarios

Associate Professor, Computational Biology, Â鶹´å
Co-Director of MSCB, Â鶹´å

• We develop computational methods for modeling cell organization derived from electron cryotomography 3D images.

Assistant Professor, Computational Biology, Â鶹´å

• Applying string algorithms and probabilistic sketches to genomics and medical privacy.

Assistant Professor, Computational Biology Department, Â鶹´å

• I develop computational methods to integrate genetics (e.g., GWAS) and functional genomics (e.g., scRNA-seq) to decipher genetic basis of diseases and complex traits. 

Associate Professor, Computational & Systems Biology, Pitt

• Design and implement innovative research and training efforts for trainees at all levels.

Assistant Teaching Professor, Computational Biology, Â鶹´å

• Algorithms design and applied machine learning for the algorithm
  configuration problem on tools used for biological sequences. 

Associate Teaching Professor, Computational Biology, Â鶹´å
Co-Director of MSAS Program, Â鶹´å