Allan Jabri was born in Sydney, Australia to parents from China and Lebanon. Raised in the United States, Allan’s experience living between cultures drove him to construct theories of the world, to learn to deconstruct stigma, and to revel in our cognitive ability to distill simplicity from complexity. This practice catalyzed his curiosity in science and bred the compassionate humanist he identifies as today.
As an undergraduate at Princeton University, Allan developed his interest in computer science and the study of methods for automating problem solving through abstraction in artificial intelligence (AI) and machine learning. His undergraduate thesis work on probabilistic methods for egocentric scene understanding inspired him to pursue a career in research. After graduation, Allan worked as a research engineer at Facebook AI Research in New York, where he was fortunate to learn from colleagues from around the world, to mature as a researcher and engineer, and to deepen his curiosities across machine learning, cognitive science, and AI.
Allan is a member of Berkeley AI Research. He is interested in problems related to self-supervised learning, continual learning, intrinsic motivation, and embodied cognition. His long-term goal is to build learning algorithms that allow machines to autonomously acquire visual and sensorimotor common sense. During his PhD, he also hopes to mentor students, contribute to open source code projects, and develop a more interdisciplinary perspective on AI.