Alankrita Bhatt

Alankrita Bhatt is a Postdoctoral Scholar Research Associate at the Department of Computing and Mathematical Sciences (CMS) at the California Institute of Technology since January 2023. Prior to this, she was a research fellow at the Simons Institute for the Theory of Computing, UC Berkeley, in Fall 2022. She holds a Ph.D. in Electrical Engineering from UC San Diego (2022), advised by Prof. Young-Han Kim, along with an M.S. in Statistics from UC San Diego (2020) and a B.Tech in Electrical Engineering from IIT Kanpur (2016).

• Education
  • Ph.D. in Electrical Engineering, UC San Diego (2016–2022)
  • M.S. in Statistics, UC San Diego (2020)
  • B.Tech in Electrical Engineering, Indian Institute of Technology Kanpur (2012–2016)

Her research lies at the intersection of Information Theory and Statistics, focusing on Sequential Decision Making, Machine Learning, and Probability. Her recent work explores universal prediction, confidence sequences, and online learning frameworks with side information.

Alankrita’s publications (2017–2024) span journals like IEEE Transactions on Information Theory and conferences including ISIT, NeurIPS, and COLT. Key themes include universal graph compression, sequential probability assignment, and stochastic block models, reflecting her expertise in integrating statistical and information-theoretic approaches.