Swetava Ganguli

Swetava is a Staff Machine Learning Research Scientist at Apple. His research mainly focuses on developing novel algorithms and techniques to analyze, understand, and draw inferences from large, multimodal (combination of heterogenous data modalities), and spatiotemporal datasets which are sparsely labeled or unlabeled. Topics of major emphasis in his research include self-supervised learning, semi-supervised and weakly-supervised learning, and deep generative modeling. He is strongly interested in translating his research in machine learning and AI to build technologies and products for diverse applications that solve real-world problems.

He holds two M. S. degrees (2014, 2019) and a PhD (2018) focused on computer science and computational engineering from Stanford University and a B. Tech. (2012) from Indian Institute of Technology (IIT), Gandhinagar where he graduated as the President’s Gold Medalist.