I work on recommendation systems at Meta as a Software Engineer, building machine-learning systems for ranking, retrieval, and user modeling at very large scale. My research interests span representation learning, cognitive science, and the study of how intelligent systems learn and adapt.
I received my Ph.D. in Computer Science from Carnegie Mellon University, advised by Dr. Leila Wehbe. My doctoral research used computational models to study how visual information is represented in the human brain—and what comparisons between neural networks and brains can genuinely explain.
During my Ph.D., I also worked at Meta Reality Labs on self-supervised representation learning for neuromuscular signals and downstream adaptation.
Before CMU, I studied Automation at Tsinghua University and worked with Dr. Jiwen Lu on computer vision and multimodal learning.
Pinned perspectives
A three-part arc: boundary → adaptive organization → user dynamics.
A research perspective on language, brains, and large language models organized around one question: at what scale does the explanatory loop close?
A research perspective on NeuroAI through Norbert Wiener: from representation to feedback, learning, agency, and the question of who chooses the goal.
A recommender does not merely learn a user from their past; it becomes part of the environment through which the user's future is formed.
Questions, observations, and unfinished ideas.
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As AI rapidly colonizes academia, what does it mean when knowledge production no longer serves humans?
We are facing the deepest epistemological shift since the Enlightenment: the institutional gamification of science (machines serving metrics) and the decoupling of human cognition (machines serving machines). 🧵👇