Ainesh Bakshi
Email: ainesh (at) mit (dot) edu Office: 2-174 |

I am currently a postdoc in the Computer Science and Mathematics departments at MIT, where I collaborate closely with Ankur Moitra, Sam Hopkins and Piotr Indyk. I am broadly interested in Theoretical Computer Science and my current research interests lie in the intersection of algorithm design and learning theory. More specifically, I spend time thinking about the sum-of-squares convex programming hierarchy, randomized numerical linear algebra, algorithmic statistics, quantum information theory, and high-dimensional probability.

I recently finished my PhD at Carnegie Mellon University, where I was extremely fortunate to be co-advised by Pravesh Kothari and David Woodruff. During the summer of 2021, I interned with Madhur Tulsiani and Yury Makarychev at Toyota Technological Institute-Chicago and in summer 2020, I interned with Ken Clarkson at IBM Almaden.

Even earlier, I was an undergrad at Rutgers, New Brunswick, where I had the pleasure of working with Martin Farach-Colton
and Pranjal Awasthi.

Algorithms for Learning Latent Models: Establishing Tractability to Approaching Optimality

PhD Thesis [pdf]

Committee: Pravesh Kothari, David Woodruff, Ryan O'Donnell, Boaz Barak.

A quasi-polynomial time algorithm for Multi-Dimensional Scaling via LP Hierarchies

Ainesh Bakshi, Vincent Cohen-Addad, Samuel B. Hopkins, Rajesh Jayaram, Silvio Lattanzi

Preprint [arXiv].

Efficient Certificates of Anti-Concentration Beyond Gaussians

Ainesh Bakshi, Pravesh Kothari, Goutham Rajendran, Madhur Tulsiani, Aravindan Vijayaraghavan

FOCS 2024 [arXiv].

Structure learning of Hamiltonians from real-time evolution

Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang

FOCS 2024 [arXiv].

High-Temperature Gibbs States are Unentangled and Efficiently Preparable

Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang

FOCS 2024 [arXiv].

Learning Quantum Hamiltonians at any Temperature in Polynomial Time

Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang

QIP 2024 (Plenary Talk and Best Student Paper)

STOC 2024 (Invited to SICOMP Special Issue)

[arXiv] [Quanta].

An Improved Classical Singular Value Transform for Quantum Machine Learning

Ainesh Bakshi, Ewin Tang

SODA 2024 [arXiv].

A Near-Linear Time Algorithm for the Chamfer Distance

Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal, Erik Waingarten

NeurIPS 2023 [arXiv].

Krylov Methods are (nearly) Optimal for Low-Rank Approximation

Ainesh Bakshi, Shyam Narayanan

FOCS 2023 [arXiv].

Tensor Decompositions Meet Control Theory:

Learning General Mixtures of Linear Dynamical Systems

Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau

ICML 2023 [arXiv].

A New Approach to Learning a Linear Dynamical System

Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau

STOC 2023 [arXiv].

Sub-Quadratic Algorithms for Kernel Matrices via Kernel Density Estimation

Ainesh Bakshi, Piotr Indyk, Praneeth Kacham, Sandeep Silwal, Samson Zhou

ICLR 2023 (Spotlight Talk) [arXiv].

Low-Rank Approximation with 1/epsilon^(1/3) Matrix-Vector Products

Ainesh Bakshi, Ken Clarkson, David Woodruff

STOC 2022 [arXiv].

Robustly Learning Mixtures of k Arbitrary Gaussians

Ainesh Bakshi, Ilias Diakonikolas, He Jia, Daniel Kane, Pravesh Kothari, Santosh Vempala

STOC 2022 [arXiv].

Robust Linear Regression: Optimal Rates in Polynomial Time

Ainesh Bakshi, Adarsh Prasad

STOC 2021 [arXiv].

Learning a Latent Simplex in Input Sparsity Time

Ainesh Bakshi, Chiranjib Bhattacharyya, Ravi Kannan, David Woodruff, Samson Zhou

ICLR 2021 (Spotlight Talk) [arXiv].

List-Decodable Subspace Recovery: Dimension Independent Error in Polynomial Time

Ainesh Bakshi, Pravesh Kothari

SODA 2021 [arXiv].

Testing Positive Semi-Definiteness via Random Submatrices

Ainesh Bakshi, Nadiia Chepurko, Rajesh Jayaram

FOCS 2020 [arXiv].

Outlier-Robust Clustering of Non-spherical Mixtures

Ainesh Bakshi, Pravesh Kothari

FOCS 2020 [arXiv]

Conference version to be merged with this paper.

Robust and Sample Optimal Algorithms for PSD Low Rank Approximation

Ainesh Bakshi, Nadiia Chepurko, David Woodruff

FOCS 2020 [arXiv].

Weighted Maximum Independent Set of Geometric Objects in Turnstile Streams

Ainesh Bakshi, Nadiia Chepurko, David Woodruff

APPROX 2020 [arXiv].

Robust Communication-Optimal Distributed Clustering Algorithms

Pranjal Awasthi, Ainesh Bakshi, Nina Balcan, Colin White, David Woodruff

ICALP 2019 [arXiv].

Learning Two Layer Rectified Neural Networks in Polynomial Time

Ainesh Bakshi, Rajesh Jayaram, David Woodruff

COLT 2019 [arXiv].

Sublinear Time Low-Rank Approximation of Distance Matrices

Ainesh Bakshi, David Woodruff

NeurIPS 2018 (Spotlight Talk) [arXiv].

File Systems fated for Senescence? Nonsense, Says Science!

A. Conway, A. Bakshi, Y. Jiao, Y. Zhan, M. Bender, W. Jannen, R. Johnson, B. Kuzmaul, D. Porter, J. Yuan, M. Farach-Colton

FAST '17 [pdf].

- Program Committee: STOC 2024, COLT 2023, ALT 2023

- TA for CS 15-751 : TCS Toolkit

by Ryan O'Donnell

- TA for CS 15-451/651 : Algorithms

by Anupam Gupta and David Woodruff