Shiva Kasiviswanathan

Shiva Kasiviswanathan

I am a Principal Applied Scientist at Amazon, working on the theory and practice of machine learning.

Shiva Kasiviswanathan speaking at the Agentic AI Summit
Principal Applied Scientist Amazon, Bay Area, California

Research interests

  • LLM post-training and alignment Reliable reasoning, evaluation, and adaptation for large language models.
  • Machine Learning Theory Foundations of learning, optimization, and reliable statistical methods.
  • Differential privacy Rigorous privacy guarantees for statistical and machine learning systems.

Selected publications

All research
  1. ICLR 2026

    Training Large Language Models to Reason in Parallel with Global Forking Tokens

    With S. Jia and X. Wang.

    ICLR 2026.

  2. ICML 2023

    Thompson Sampling with Diffusion Generative Prior

    With Y. Hsieh, B. Kveton, and P. Bloebaum.

    ICML 2023.

  3. AISTATS 2019

    Subsampled Renyi Differential Privacy and Analytical Moments Accountant

    With Y. Wang and B. Balle.

    AISTATS 2019.

    Notable Paper Award.

  4. COLT 2018

    Restricted Eigenvalue from Stable Rank with Applications to Sparse Linear Regression

    With M. Rudelson.

    COLT 2018.

  5. SICOMP 2011

    What Can We Learn Privately?

    With H. K. Lee, K. Nissim, S. Raskhodnikova, and A. Smith.

    SIAM Journal on Computing 2011; FOCS 2008.

  6. STOC 2010

    The Price of Privately Releasing Contingency Tables and the Spectra of Random Matrices with Correlated Rows

    With M. Rudelson, A. Smith, and J. Ullman.

    STOC 2010.