Yehya Farhat

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I am a third year PhD student in the Computer Science department at Rice University, advised by Anastasios Kyrillidis. I am also part of the AI OWLS Ken Kennedy institute research cluster.

My overarching research goal is to develop scalable, rigorous, and efficient methods for training and deploying large-scale AI systems. My work spans several interconnected areas: convex and non-convex optimization, distributed training, model compression, Mixture-of-Experts, and continual learning. I am also keenly interested in leveraging AI agents in real-world settings to solve critical bottlenecks in scientific and industrial domains.

I interned at IBM Research this past summer. Prior to that, I was a Research Scholar at Syracuse University, where I worked with Prof. Venkata Gandikota. I also earned my M.Sc. in Computer Science from Syracuse University, completing my thesis under the supervision of Prof. Ferdinando Fioretto. Before that, I received my B.Sc. in Computer Science, with a minor in Mathematics, from the American University of Beirut (AUB).

In my free time, I enjoy playing chess, staying active by playing football, and hitting the gym. I am also an avid writer passionate about sharing intriguing scientific topics I encounter. To stay updated, you can find and follow me on Medium.

News

Nov 07, 2025 Announcement_1 neurips2025
Nov 07, 2025 Check out our new preprint TwIST: Rigging the Lottery in Transformers with Independent Subnetwork Training! We propose a novel distributed training algorithm that trains subnetworks in parallel to uncover high-performing sparse models that need no fine-tuning.
Sep 19, 2025 Excited to announce that our paper Learning to Specialize: Joint Gating-Expert Training for Adaptive MoEs in Decentralized Settings has been accepted at NeurIPS2025