Archan Ray

Research Interests

Broadly, I am interested in developing computationally efficient algorithms for large-scale data analysis, numerical linear algebra, and machine learning. Much of my work focuses on randomized and sublinear algorithms for matrix problems, including sketching, sampling, spectral approximation, low-rank approximation, and fast solvers.

More recently, I have worked on efficient algorithms for modern AI systems, including randomized sketch-and-precondition methods, quantum-inspired algorithms, parameter-efficient finetuning, data subsampling, and watermarking. A recurring theme in my work is to design algorithms that are theoretically principled, computationally scalable, and useful in practice.

Publications

• Provably Faster Randomized and Quantum Algorithms for k-means Clustering via Uniform Sampling

  Tyler Chen\( ^\dagger \), Archan Ray\( ^\dagger \), Akshay Seshadri\( ^\dagger \), Dylan Herman, Bao Bach, Pranav Deshpande, Abhishek Som, Niraj Kumar, Marco Pistoia

  in submission

• Adaptive and Robust Watermark for Generative Tabular Data

  Dung Daniel Ngo\( ^\dagger \), Archan Ray\( ^\dagger \), Akshay Seshadri\( ^\dagger \), Daniel Scott, Saheed Obitayo, Niraj Kumar, Vamsi K. Potluru, Marco Pistoia, Manuela Veloso

  Conference on Uncertainty in Artificial Intelligence (UAI) 2026.

• MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning

  Javier Lopez-Piqueres, Pranav Deshpande, Archan Ray, Mattia J. Villani, Marco Pistoia, Niraj Kumar

  Transactions on Machine Learning Research (TMLR) 2026

• GPU-Parallelizable Randomized Sketch-and-Precondition for Linear Regression using Sparse Sign Sketches\( ^\star \)

  Tyler Chen, Pradeep Niroula, Archan Ray, Pragna Subrahmanya, Marco Pistoia, Niraj Kumar

  International Workshop on Parallel and Distributed Scientific and Engineering Computing 2026 (PDSEC 2026). Best Paper Award

• A Simple Analysis of a Quantum-Inspired Algorithm for Solving Low-Rank Linear Systems

  Tyler Chen\( ^\dagger \), Junhyung Lyle Kim\( ^\dagger \), Archan Ray\( ^\dagger \), Shouvanik Chakrabarti, Dylan Herman, Niraj Kumar

  ArXiv

• A Unified Framework for Provably Efficient Algorithms to Estimate Shapley Values

  Tyler Chen\( ^\dagger \), Akshay Seshadri\( ^\dagger \), Mattia J. Villani\( ^\dagger \), Pradeep Niroula, Shouvanik Chakrabarti, Archan Ray, Pranav Deshpande, Romina Yalovetzky, Marco Pistoia, Niraj Kumar

  Annual Conference on Neural Information Processing Systems (NeurIPS) 2025.

• Improved Spectral Density Estimation via Explicit and Implicit Deflation\( ^\star \)

  Rajarshi Bhattacharjee, Rajesh Jayaram, Cameron Musco, Christopher Musco, Archan Ray

  ACM-SIAM Symposium on Discrete Algorithms (SODA) 2025.

• Universal Matrix Sparsifiers and Fast Deterministic Algorithms for Linear Algebra\( ^\star \)

  Rajarshi Bhattacharjee, Gregory Dexter, Cameron Musco, Archan Ray, Sushant Sachdeva, David P. Woodruff

  Innovations in Theoretical Computer Science (ITCS) 2024.

• Sublinear Time Eigenvalue Approximation via Random Sampling\( ^\star \)

  Rajarshi Bhattacharjee, Gregory Dexter, Petros Drineas, Cameron Musco, Archan Ray

  extended abstract in International Colloquium on Automata, Languages, and Programming (ICALP) 2023, full paper in Algorithmica 2024.

• Sublinear Time Approximation of Text Similarity Matrices

  Archan Ray, Nicholas Monath, Andrew McCallum, Cameron Musco

  AAAI Conference on Artificial Intelligence (AAAI) 2022.

• Tight Coupling of Character, Word, and Place Recognition for End-to-End Text Recognition in Maps

  Archan Ray, Aruni Roy Chowdhury, Yi Fung, Jerod Weinman, Erik Learned-Miller

  Technical Report, College of Information and Computer Sciences, University of Massachusetts, Amherst, MA, 2019.

• Historical Map Annotations for Text Detection and Recognition

  Archan Ray, Ziwen Chen, Ben Gafford, Nathan Gifford, Jagath Jai Kumar, Abyaya Lamsal, Liam Niehus-Staab, Jerod Weinman, Erik Learned-Miller

  Grinnell College, Technical Report, 2018.

• U-PC: Unsupervised Planogram Compliance

  Archan Ray, Nishant Kumar, Avishek Shaw, Dipti Prasad Mukherjee

  European Conference on Computer Vision (ECCV) 2018.

Theses/Dissertations

• Sublinear Algorithms for Matrices: Theory and Applications

  Archan Ray

  PhD Dissertation, UMass Amherst, 2024.

• Estimation of image features representing facial emotions for emotion synthesis

  Archan Ray

  M. Tech Dissertation Series, Indian Statistical Institute, Kolkata, 2015.

Patents

• System and method for object recognition based estimation of planogram compliance

  Pranoy Hari, Shilpa Yadukumar Rao, Rajashree Ramakrishnan, Avishek Kumar Shaw, Archan Ray, Nishant Kumar, and Dipti Prasad Mukherjee

  U.S. Patent 10,748,030, 2020.