Dr. Honavar's research contributions span Artificial Intelligence, Machine Learning, Causal Inference and biomedical informatics. Vasant is a Fellow of the American Association for the Advancement of Science (AAAS).

Scientific progress is increasingly enabled by our ability to examine natural phenomena through the computational lens, and our ability to acquire, share, integrate and analyze disparate types of data. Some have gone so far as to suggest that the emergence of big data has made the scientific method obsolete, that we can simply let statistical machine learning algorithms find correlations and patterns, and science can advance without coherent models, unified theories, or any mechanistic explanations at all. This talk will argue that big data, on its own, in Rutherford’s terminology, amounts to little more than massive "stamp collections”; and that making the leap from "stamp collecting to physics”, requires advances on many fronts beyond data management and machine learning. Specifically, realizing the promise and potential of big data to advance science requires fundamental advances in several facets of Artificial Intelligence, including: (i) algorithmic abstractions of the domain of scientific discourse, scientific artifacts (e.g., data, assumptions, knowledge, hypotheses, models, explanations, arguments, etc.) and elements of the scientific method (e.g., experimentation, induction, deduction, abduction, analysis, synthesis; (ii) cognitive tools that leverage the abstractions to augment and extend human intellect and abilities, and infrastructure to support human-machine, human-human, and machine-machine collaboration.