Newsworthy

Max Planck Institute for Mathematics in the Sciences in Leipzig, Germany features my profile as a co-organizer of the Summer School on Randomness and Learning in Non-linear Algebra in July 2019.

I taught two short courses in summer schools in 2018.

The Center for Interdisciplinary Scientific Computation (CISC) awarded the 2018 seed grant to Lulu Kang (applied math), Mahima Saxena (psychology), and me.

I was the recipient of the IIT College of Science Junior Research Excellence Award in December 2015.

As of 2015, I am an Elected Member of the ISI.

Summary

My research focuses on the interplay between commutative algebra and statistics, where the interaction goes two ways. First, I build statistical models for discrete relational data that capture more complex behavior than traditional models, study them through the algebrogeometric lens, prove their interpretability in practice, and develop scalable model/data fit testing methodologies using a blend of combinatorial, algebraic, probabilistic, and Bayesian algorithms. In the other direction, I study randomized algorithm approaches to computational algebra problems whose expected runtimes are much lower then the well-known worst-case complexity bounds, develop probabilistic models to study average and extreme behavior of algebraic objects, and use machine learning to predict and improve behavior of algebraic computations.

Algebraic statistics for network models

    • Non-asymptotic goodness-of-fit tests based on Markov bases;
    • Existence and complexity of MLE;
    • Dynamic combinatorially-inspired data-oriented algorithms for model fitting;
    • Application to (large) sparse network data.
    Sponsors:
    DARPA FA9550-12-1-0392 (2012-2013), AFOSR FA9550-14-1-0141 (2014-2017).

    Statistical models in psychology

    • Computational challenges in occupational health psychology:
      using statistics to model dynamic worker well-being.
    Sponsors:
    2018 CISC seed grant with Mahima Saxena, IIT Psychology, and Lulu Kang, IIT Applied Math.

    Randomness and learning for non-linear algebra

      • Stochastic non-linear algebra;
      • Solving systems of multivariate equations;
      • Fast randomized algorithms for large structured systems;
      • Randomized structures in algebra with applicaitons.
      Sponsors:
      NSF DMS-1522662 (2015-2019).

      Here are my Google Scholar and ResearchGate profiles, though the latter seems outdated.

      I actively mentor and involve students in my research. If you are interested, check out these research summary slides from April 2017.