Leading short video AI algorithm innovation at TikTok. Yale PhD specializing in LLMs, NLP and Multimodal AI.
I am leading the US Content Ecology AI team, where we build and deploy large-scale, production-ready AI systems for short-video understanding and distribution. Our work spans large language models (LLMs), natural language processing (NLP), computer vision (CV), multimodal learning, agentic AI and recommendation systems. Focusing on short video as a high-dimensional, multimodal, and creator-driven data format, my team systematically tackles core algorithmic challenges across content understanding, creator understanding, and inspiration-driven distribution. We provide a unified and reusable algorithmic foundation that supports multiple core TikTok products.
Lead US content ecology AI Team at TikTok. We develop large-scale AI algorithms across multimodal content understanding and LLM applications across TikTok short video apps. Lead the build-out of an AI algorithm team at TikTok Short Video, driving cross-team initiatives across content understanding, multimodal intelligence, and large-scale recommendation systems.
Dissertation: Multi-modal Reinforcement Learning. Advised by Kumpati S. Narendra. Research focused on reinforcement learning, neural networks, NLP and AI.
My research spans GenAI, LLMs, NLP, Agentic AI, and Multimodal Content Understanding.
For a complete list of publications, please visit my Google Scholar profile .
I am always interested in discussing collaborations, speaking opportunities, and industry–academia partnerships. I am also actively looking for top AI talent to join my group at TikTok, both full-time and as interns. Please feel free to reach out via email if you are interested.