Aditya Bhaskara
Algorithms · Machine Learning · Foundations of AI

About Me

I am an Associate Professor at the Kahlert School of Computing at the University of Utah. I am also part of the Theory Group and the Utah Center for Data Science.

Prior to Utah, I spent two years as a postdoc at the Algorithms Group in Google Research NYC and at EPFL in Switzerland. Going further back, I was a PhD student at Princeton University and an undergraduate at IIT Bombay.

Research Interests

  • Algorithms & Optimization
  • Robust and Efficient Machine Learning
  • Foundations of AI
  • Theoretical Computer Science

As part of collaborations, I also work on problems in wireless networks and security.

(Selected) Publications

For a full list of publications, please see my DBLP page.

  • Efficient algorithms for linear regression with heteroskedastic errors S. Chaudhary, A. Bhaskara, S. Lattanzi, S. Vassilvitskii Neural Information Processing Systems (NeurIPS), 2026
  • Adaptive Weighted Averaging A. Bhaskara, A. Cutkosky, R. Kumar, M. Purohit Conference on Learning Theory (COLT), 2026
  • On the Optimization Trajectory of DeepWalk Embeddings C. Harker, A. Bhaskara International Conference on Machine Learning (ICML), 2026
  • Descent with Misaligned Gradients and Applications to Hidden Convexity A. Bhaskara, A. Cutkosky, R. Kumar, M. Purohit International Conference on Learning Representations (ICLR), 2025
  • New Tools for Smoothed Analysis: Least Singular Value Bounds for Random Matrices with Dependent Entries A. Bhaskara, E. Evert, V. Srinivas, A. Vijayaraghavan ACM Symposium on Theory of Computing (STOC), 2024
  • Online Learning and Bandits with Queried Hints A. Bhaskara, S. Gollapudi, S. Im, K. Kollias, K. Munagala Innovations in Theoretical Computer Science (ITCS), 2023
  • Additive Error Guarantees for Weighted Low Rank Approximation A. Bhaskara, A. K. Ruwanpathirana, M. Wijewardena International Conference on Machine Learning (ICML), 2021

Teaching

  • Fall 2026 · CS 3190: Foundations of Data Analysis Canvas link
  • Spring 2026 · CS 3130: Probability and Statistics for Engineers Canvas link
  • Fall 2025 · CS 4150: Algorithms Canvas Link
  • Spring 2025 · CS 6960: Theory of Machine Learning Course Webpage. Intended for early graduate students and mathematically inclined undergraduates.
  • Spring 2024 · CS 6966: Algorithms, Geometry, and Optimization Course Webpage. Topics include probabilistic analysis, LPs and SDPs, spectral graph theory, and online algorithms.