Parsimonious Learning in Hierarchy of Compute Contexts

ABSTRACT

The adoption of machine learning (ML) by domains like computational science and cyber-physical systems has brought immense opportunity to accelerate research progress in these disciplines. However, idiosyncratic domain properties like high data generation costs, data corruption or compute restrictions often lead to novel failure modes of otherwise successful ML pipelines. This talk proposes a new design paradigm called 'parsimonious machine learning' as a viable solution to overcome common ML failure modes due to data and compute paucity in scientific and cyber-physical contexts. Specifically, the talk will demonstrate novel techniques that develop generalizable ML pipelines, alleviating the adverse effects of data paucity and compute paucity by leveraging a 'hierarchy-of-compute' design.

BIOGRAPHY

Nikhil Muralidhar is an assistant professor in the Department of Computer Science at Stevens Institute of Technology. At Stevens, Nikhil leads the Scientific AI (ScAI) lab with a focus on developing novel machine learning techniques for application in scientific, cyber-physical and epidemiological contexts. Nikhil has published over 25 articles in top-tier conferences like ICLR, IJCAI, AAAI, IEEE ICDM, SIAM SDM and journals like ACM TIST and Physics of Fluids.

Nikhil also serves as a PC member for ICLR, NeurIPS, ICML, SIAM SDM, IEEE Big Data. He has also contributed a book chapter to the book titled: Science Guided Machine Learning: Emerging Trends in Combining Scientific Knowledge with Data-driven Methods.

Prior to joining Stevens, Nikhil completed his Ph.D at Virginia Tech and was nominated for College of Engineering Outstanding Doctoral Student award and the Outstanding Doctoral Dissertation award by the Computer Science Department. Nikhil was awarded the Outstanding Academic Achievement award during his M.S. at George Mason University.