dheeraj[at]ucsb[.]edu LinkedIn

I am a fifth year PhD student in Computer Science department at UC Santa Barbara where I am fortunate to be advised by Prof. Yu-Xiang Wang. My research primarily focuses on developing online learning algorithms that can optimally cope with non-stationarities present in the environment. Applications of my work include forecasting trends in Time Series, adaptive Total Variation denoising, non-stationary Multi Armed Bandits, Online Convex Optimization… I received my Bachelors and Masters degrees in Electrical Engineering from Indian Institue of Technology, Chennai during 2016.

Publications

• Dheeraj Baby*, Jianyu Xu* and Yu-Xiang Wang "Non-stationary Contextual Pricing with Safety Constraints", TMLR 2022 (*Equal contribution)

• Dheeraj Baby and Yu-Xiang Wang "Optimal Dynamic Regret in LQR Control", NeurIPS 2022 [pdf]

• Dheeraj Baby and Yu-Xiang Wang "Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond", AISTATS 2022 [pdf]

• Dheeraj Baby and Yu-Xiang Wang "Optimal Dynamic Regret in Exp-Concave Online Learning", COLT 2021 [pdf], Best Student Paper Award

• Dheeraj Baby, Xuandong Zhao and Yu-Xiang Wang "An Optimal Reduction of TV-Denoising to Adaptive Online Learning", AISTATS 2021 [pdf]

• Dheeraj Baby and Yu-Xiang Wang "Adaptive Online Estimation of Piecewise Polynomial Trends", NeurIPS 2020 [pdf]

• Dheeraj Baby and Yu-Xiang Wang "Online Forecasting of Total-Variation-Bounded Sequences", NeurIPS 2019 [pdf] [video]

• Jason R. Baumgartner, Robert L. Kanzelman, Pradeep Kumar Nalla, Raj Kumar Gajavelly, Dheeraj Baby "Initial-state and next-state value folding", US Patent [US10621297B1]

Industry and Academic Experience

• Reviewer at ICML19-21, JMLR21, AISTATS22. Received top 33% reviewer award at ICML20

• Intern, Google Research, Machine Learning and Optimization group, Jun 2022 - Sept 2022

• Intern, AWS AI Labs, Time Series forecasting group, Jun 2020 - Sept 2020

• Software developer, IBM Bangalore, India, 2016-2018