Siddarth Asokan
Research SDE II
About
Siddarth Asokan is currently a Research Software Development Engineer (RSDE II) at the Microsoft Research Lab (MSRI) in Bangalore, India, and is working as part of Dr. Manik Varma’s (opens in new tab) group, exploring the world of textual generative models in the context of large-scale information retrieval and eXtreme Classification (opens in new tab). Prior to joining Microsoft, Prior to joining MSR, he was an interdisciplinary Ph.D. scholar at the Robert Bosch Center for Cyber-Physical Systems(RBCCPS) (opens in new tab) at the Indian Institute of Science (IISc) (opens in new tab), Bangalore, graduating in 2023 (and earning a Masters by Research degree along the way), and a Bachelors in Engineering (B.E.) degree in Electronics and Communications from the M.S. Ramaiah Institute of Technology(MSRIT) (opens in new tab), Bangalore in 2017. During his Ph.D., he worked at the Spectrum Lab (opens in new tab) in the Department of Electrical Engineering at IISc, under the supervision of Dr. Chandra Sekhar Seelamantula, (opens in new tab) while working on generative machine learning for images and developed strong theoretical foundations for the widely popular generative adversarial networks (GANs) and diffusion model frameworks with significant contributions to high-dimensional interpolation, Fourier approximations, and partial differential equations in very large dimensional space.
He has received various accolades in the past, including the IEI Young Engineers’ Award 2024-25, the Qualcomm Innovation Fellowship in 2019, 2021, 2022, and 2023, the Robert Bosch Center for Cyber Physical Systems Fellowship in 2020 and 2021, and the Microsoft Research Fellowship in 2018. His doctoral research was awarded the IUPRAI Doctoral Dissertation Award 2023, and the Prof. Satish Dhawan Research Award 2024.
His research interests are broadly in the area of generative machine learning, information retrieval and signal processing. Currently, at MSR, his focus is on building frontier generative information retrieval models, and on methods to make them low-latency and accurate, by leveraging world knowledge. During his Ph.D., he worked on generative adversarial networks (GANs), and on building theoretical foundations for analyzing GANs, leveraging insights from classical image processing, variational Calculus and Fourier theory, and designing network architectures motivated by those findings.
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