Kai Yi is a research scientist focused on model compression and inference acceleration, currently at Meta AI in Sunnyvale with nine years of research and engineering experience across academia and industry. His PhD work at KAUST produced practical LLM compression and communication-efficient federated learning methods (including NeurIPS and ICLR oral/poster papers), and he has a track record of translating research into deployable systems through internships at SonyAI, Tencent, CMU, SenseTime, and others. Kai combines deep expertise in multimodal learning, zero-shot methods, and efficient on-device models with hands-on experience optimizing and porting networks for embedded and production environments. He is particularly skilled at bridging federated learning theory and large-model deployment, often delivering both algorithmic novelty and pragmatic speedups.
9 years of coding experience
6 years of employment as a software developer
PhD Candidate of Computer Science, Machine Learning Optimization, PhD Candidate of Computer Science, Machine Learning Optimization at KAUST (King Abdullah University of Science and Technology)
Bachelor's degree, Computer Software Engineering, GPA: 85.49/100, Bachelor's degree, Computer Software Engineering, GPA: 85.49/100 at Xi'an Jiaotong University
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