Welcome to my research page! My primary interests lie in
machine learning, structured prediction, and enhancing
artificial intelligence reasoning with domain knowledge and
constraints. I have a broad range of research goals, which has
led me to work on diverse projects in machine learning and
artificial intelligence. These include inference and learning
over graphs, object navigation and detection, time-series
modeling, entity resolution, dialogue systems, explainability,
online learning, event detection in autonomous driving, and
more.
My research is inherently multidisciplinary, involving
extensive collaborations with both industry and academic
partners. Recently, I have focused on the symbiotic
integration of deep neural networks with symbolic systems and
theory, aiming to push the boundaries of what AI can achieve.
Started Ph.D. studies in Computer Science and Engineering
at the University of California, Santa Cruz, under the
guidance of my advisor, Dr. Lise Getoor.
June 2020
Internship with Google
In collaboration with Google, I developed advanced
neural-symbolic semi-supervised learning techniques for
dialog systems.
December 2022
Advancement to Candidacy
Achieved Ph.D. candidacy through research on NeuPSL: A
Scalable Approach to Neuro-Symbolic Inference and
Reasoning.