Shiva Kaul, Ph.D, M.S
Research Focus
Shiva Kaul is a Research Fellow in the Department of Population Medicine. He earned his Ph.D in Computer Science at Carnegie Mellon University under Geoffrey Gordon. His thesis work encompassed multiple different areas of machine learning, from developing faster primitives for sequence-to-sequence models, to developing conformal prediction algorithms for meta-analyses. Earlier, he earned his B.S. and M.S. at Carnegie Mellon, conducting research under Mahadev Satyanarayanan, and also spent time at Microsoft Research under Dengyong Zhou. He has a track record of first-author publications at top machine learning venues (e.g. NeurIPS, ICML, UAI), which have been recognized with spotlight and oral presentations, as well as a doctoral consortium award.
His current research involves AI evaluation, causal inference, and algorithm development --- both theoretical and applied. His goal is to develop a system for rigorously answering healthcare questions that unifies artificial intelligence with evidence-based medicine. His perspective is that healthcare is not just an "application area" for existing methods; the hard-earned lessons in this field can help guide AI's future as it is applied in areas of more uncertainty and more consequence.