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Research Interests

I’m broadly interested in stochastic modeling, machine learning, and computational statistics. In particular, much of my work focuses on contributing inferential tools in dependent, constrained, and missing data settings.

Currently, I am developing latent variable approaches for inference in dynamic stochastic models, especially discrete valued Markov processes in continuous time such as branching processes and mechanistic compartmental models. Recently I am considering their extensions over spatial domains and networks, incorporating more flexible rules of behavior and interaction. We are also interested in casting these and related problems from an optimization viewpoint via EM and its generalization MM (majorization-minimization). These approaches admit fast, intuitive algorithms for tasks including clustering and estimation under general set-based constraints. I am particularly interested in likelihood-based formulations and exploring connections between Frequentist and Bayesian approaches derived from these perspectives.

I work primarily on developing theory and methods, often driven by applications to systems biology and epidemiology. On the applications side, much of my work focuses on enabling rigorous inference for noisy or partially observed epidemic data. I am also interested in stochastic modeling and inference for hematopoiesis, the process of blood cell production, and related systems, especially using emergent data types from single-cell lineage tracking and sequencing.

A list of publications along with software is available here.

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