2021
A Flexible Joint Longitudinal-Survival Model for Analyzing Longitudinally Sampled Biomarkers
Open Journal of Statistics · paper
Software Engineer at Meta
My work and life are guided by the same principle: experience becomes meaningful when we learn from it. In AI, I build self-improving systems that learn through interaction and turn feedback into continuous improvement. In life, I practice being present and reflecting on experience—growing with intention and gratitude for the priceless gift of being alive.
I am a Software Engineer at Meta, where I focus on self-improving AI systems. Previously, I worked on Bean Machine, an open-source universal probabilistic programming language for fast and accurate Bayesian analysis. My broader contributions at Meta span agent evaluation and observability, continual learning, AI-assisted software engineering, and adaptive model optimization. Before joining Meta, I spent nine years at two startups: Cylance (acquired; 2016–2019) and MIND Research Institute (2010–2016). I earned my B.S. from Sharif University of Technology and my M.S. and Ph.D. from UC Irvine, where my doctoral research focused on Bayesian nonparametric modeling.

2021
Open Journal of Statistics · paper
2020
10th International Conference on Probabilistic Graphical Models (PMLR 138) · paper
2020
U.S. Patent 10,754,948 · patent
2020
2020
International Conference on Probabilistic Programming (PROBPROG) · paper
2017
Kindle edition · book