About Me

Since August 2025, I am an assistant professor (tenure-track) at the University of Minnesota Twin Cities (Department of Industrial and Systems Engineering), where I work on algorithms for continuous optimization problems in theoretical computer science and machine learning.

Prior to this, I was a postdoctoral researcher at MIT (hosted by Ali Jadbabaie and Suvrit Sra) and completed my Ph.D. in 2023 from the University of Washington, Seattle, where I was mentored by Yin Tat Lee (my dissertation). During my Ph.D., I interned with Jelena Diakonikolas at the University of Wisconsin, Madison, with Di Wang and Zhe Feng (Google Market Algorithms), and with Richard Q. Zhang and David P. Woodruff (DeepMind).

Before starting optimization research, I studied Electronics/Electrical Engineering, with research on rapid diagnostic tests for malaria, and spent some time in industry as a signal processing engineer working on pulse oximeters and bluetooth speakers.

Publications

Online Bidding Under Ros Constraints Without Knowing the Value
Sushant Vijayan*, Zhe Feng^α, Swati Padmanabhan^α, Karthikeyan Shanmugam^α, Arun Suggala^α, Di Wang^α.
TheWebConf 2025

First-Order Methods for Linearly Constrained Bilevel Optimization [arXiv]
Guy Kornowski^α, Swati Padmanabhan^α, Kai Wang^α, Zhe Zhang^α, Suvrit Sra
Neural Information Processing Systems (NeurIPS) 2024

On the Sample Complexity of Imitation Learning for Smoothed Model Predictive Control [arXiv]
Daniel Pfrommer*, Swati Padmanabhan*, Kwangjun Ahn, Jack Umenberger, Tobia Marcucci, Zakaria Mhammedi, Ali Jadbabaie
Conference on Decision and Control (CDC) 2024

Improving the Bit Complexity of Communication for Distributed Convex Optimization [arXiv]
Mehrdad Ghadiri^α, Yin Tat Lee^α, Swati Padmanabhan^α, William Swartworth^α, David P. Woodruff^α, Guanghao Ye^α
Symposium on Theory of Computing (STOC) 2024 [our talk]

Computing Approximate $\ell_p$ Sensitivities [arXiv], [paper]
Swati Padmanabhan^α, David P. Woodruff^α, Qiuyi Zhang^α
Neural Information Processing Systems (NeurIPS) 2023 [slides]

Online Bidding Algorithms for Return-on-Spend Constrained Advertisers [arXiv]
Zhe Feng^α, Swati Padmanabhan^α, Di Wang^α
TheWebConf 2023 [slides]

A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data [arXiv]
Jelena Diakonikolas^α, Chenghui Li^α, Swati Padmanabhan^α, Chaobing Song^α
Neural Information Processing Systems (NeurIPS) 2022

A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions [arXiv], [paper]
Damek Davis^α, Dmitriy Drusvyatskiy^α, Yin Tat Lee^α, Swati Padmanabhan^α, Guanghao Ye^α
Neural Information Processing Systems (NeurIPS) 2022, Selected for Oral Presentation [slides]

Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity [arXiv]
Sally Dong^α, Haotian Jiang^α, Yin Tat Lee^α, Swati Padmanabhan^α, Guanghao Ye^α
Neural Information Processing Systems (NeurIPS) 2022

Computing Lewis Weights to High Precision [arXiv]
Maryam Fazel^α, Yin Tat Lee^α, Swati Padmanabhan^α, Aaron Sidford^α
Symposium on Discrete Algorithms (SODA) 2022 [slides]

A Faster Interior-Point Method for Semidefinite Programming [arXiv]
Haotian Jiang^α, Tarun Kathuria^α, Yin Tat Lee^α, Swati Padmanabhan^α, Zhao Song^α
Foundations of Computer Science (FOCS) 2020 [talk]

An O(m/epsilon^3.5) Algorithm for Semidefinite Programs with Diagonal Constraints [arXiv]
Yin Tat Lee^α, Swati Padmanabhan^α
Conference on Learning Theory (COLT) 2020 [talk]

Manuscripts

Improved Sample Complexity of Imitation Learning for Barrier Model Predictive Control [arXiv]
Daniel Pfrommer*, Swati Padmanabhan*, Kwangjun Ahn, Jack Umenberger, Tobia Marcucci, Zakaria Mhammedi, Ali Jadbabaie

On the Hardness of Meaningful Local Guarantees in Nonsmooth Nonconvex Optimization [arXiv]
Guy Kornowski^α, Swati Padmanabhan^α, Ohad Shamir
Presented at NeurIPS Workshop on Optimization for Machine Learning 2024

Positive Semidefinite Programming: Mixed, Parallel, and Width-Independent [arXiv]
Arun Jambulapati^α, Yin Tat Lee^α, Jerry Li^α, Swati Padmanabhan^α, Kevin Tian^α
[5min talk | 20min talk]
This paper appeared in STOC 2020. However, after the publication, we found an error in the analysis (see arXiv), which we some day hope to fix.