Summary
Algebra ⟶ non-linear statistics
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.Randomness ⟶ non-linear algebra
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.
Here are my Google Scholar and ResearchGate profiles, though the latter seems outdated.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.