I am an assistant professor in the College of Computer and Information Science at Northeastern. I work on probabilistic programming frameworks that provide building blocks for model development in data science, machine learning, and artificial intelligence. I am one of the creators of the Anglican, a probabilistic programming system that is closely integrated with Clojure. I am currently developing of Probabilistic Torch, a library for deep generative models that extends PyTorch.
OCT 2018 ∙ A draft of our book An Introduction to Probabilistic Programming is now publicly available [arXiv]. This book is intended as a graduate-level introduction to probabilistic programming languages and methods for inference in probabilistic programs.
OCT 2018 ∙ Thanks to all speakers and attendees for making PROBPROG 2018 a success!
AUG 2018 ∙ I am teaching DS 5230 Unsupervised Machine Learning and Data Mining this Fall [website].
DEC 2017 ∙ Our extended abstract “Inference Trees: Adaptive Inference with Exploration” was accepted at the NIPS workshop on Advances in Approximate Bayesian Inference [website].
SEP 2017 ∙ Our paper “Learning Disentangled Representations with Semi-Supervised Deep Generative Models” has been accepted for publication at NIPS [arxiv].