Pratiksha Trust, Bangalore, founded by Infosys co-founder Senapathy “Kris” Gopalakrishnan and his wife Mrs Sudha Gopalakrishnan, has set up three Distinguished Chair Professorships at the Indian Institute of Science, Bangalore. The purpose of these Chair Professorships is to bring top-notch researchers in the areas of neuromorphic computing, computational neuroscience, machine learning, and data science foster intense research collaboration between world renowned researchers in these areas and the research community at several departments and centres of the Indian Institute of Science.
The first of these three chairs, “K. Vaidyanathan Distinguished Chair,”  was launched on June 22, 2015. Professor Shihab Shamma, Institute of Systems Research, University of Maryland, College Park, USA is the first recipient of the Shri. K. Vaidyanathan Chaired Professorship.
The second of the chair professorships is named after Mrs. Sudha Murthy, Chairperson of the Infosys Foundation, and will be called the “Mrs. Sudha Murthy Distinguished Chair.” It was launched on October 12, 2016 in the the Faculty Hall of IISc. Professor Vasant Honavar, who is currently the Edward Frymoyer Endowed Professor of Information Sciences and Technology and Professor of Computer Science at the Pennsylvania State University, has been chosen for the first Ms. Sudha Murthy Chair Professorship. Prof. Honavar is the Director of the Center for Big Data Analytics and Discovery Informatics and the Director of the Artificial Intelligence Laboratory where he also serves on the faculties of graduate programs in Bioinformatics and Genomics and Neuroscience and co-directs an interdisciplinary PhD program in Biomedical Data Sciences. Prof. Honavar’s expertise is in Artificial Intelligence (especially Machine Learning, Knowledge Representation, and Causal Inference), Bioinformatics, Data Science, Health Informatics, Neural Computing, and Neuroinformatics. Prof. Honavar is known for his work on algorithms for building predictive models from large, distributed, semantically disparate, richly structured (tabular, multi-relational, sequence, text, image, and network) data; logics for selective knowledge sharing; methods for representing and reasoning with qualitative preferences; methods for causal inference; and applications in bioinformatics (especially characterization and prediction of protein-protein and protein-RNA interactions, interfaces and complexes).
