Vanathi Gopalakrishnan

Associate Professor
Work Phone: (412) 624-3290 Work Fax: 412-624-5310
Photo of Vanathi Gopalakrishnan

Biography

Titles:

Associate Professor of Biomedical Informatics
Associate Professor of Intelligent Systems
Associate Professor of Computational and Systems Biology
Associate Professor of Bioengineering
Associate Professor of Clinical and translational Science
Director, PRoBE Laboratory for Pattern Recognition from Biomedical Evidence
Core Faculty Member, Biomedical Informatics Training Program
Faculty Member, Intelligent Systems Program
Faculty Member, Joint CMU-Pitt Program in Computational Biology
Faculty Member, Medical Scientist Training Program
Faculty Member, Cardiovascular Bioengineering Training Program
Co-Director of Bioengineering, Biotechnology and Innovation (BBI) Area of Concentration School of Medicine
Director of the Intelligent Systems Program, School of Computing and Information

Research Interests:

Dr. Gopalakrishnan is a biomedical data scientist who is passionate about developing intelligent systems to reduce the burden of disease. Her primary research focus has been on the development of novel algorithms involving rule learning for the predictive and integrative modeling of biomedical data obtained from molecular profiling studies, radiologic imaging and clinical textual reports. She is fundamentally interested in technologies for data mining and discovery that allow incorporation of prior knowledge. Fundamental research areas of interest involve extensions to rule learning via the incorporation of (1) Bayesian Statistics, (2) prior rule models, and (3) knowledge obtained through mining of ontologies or the literature. Dr. Gopalakrishnan is generally interested in the design and development of computational methods for solving clinically relevant biological problems, such as the discovery and verification of biomarkers for disease state prediction. Her research over the past decade has focused on the development, application and evaluation of symbolic, probabilistic and hybrid machine learning methods to the modeling and analysis of high-dimensional, sparsely-populated biomedical datasets, particularly from proteomic profiling studies for early detection of disease. Her current research projects involve the study of novel variants of rule learning techniques for biomarker discovery, prediction and monitoring of diverse diseases including neurodegenerative and cardiovascular diseases, lung, breast and esophageal cancers, and parasitic infectious disease, with a focus on the analyses of data obtained from metabolomics and microbiome profiling.

Areas of Interest:

Rule Learning Hybrid Algorithms – Design and Development,
Multi-modal Biomedical Data Science – Modeling and Analysis,
Biomarker Discovery,
Predictive Modeling for Precision Medicine and Health Care

Publications:
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