UVA School of Engineering: Capital One Fellows 2026-2027

In October 2025, we announced our deepening partnership with the University of Virginia (UVA) via a new $4.5 million initiative with the UVA School of Engineering and Applied Science. This includes a $2 million investment in the Capital One AI Research Neighborhood, matched by UVA for a total of $4 million, plus $500,000 for a new Ph.D. Fellowship Awards program. Through these investments in AI research infrastructure and individual scholarship, we are focused on advancing specialized scientific knowledge, fostering industry-academia collaboration and developing impactful research co-publications, ultimately allowing us to meaningfully advance science.

We recently concluded the selection process for our fellowship awards. We are excited to share our PhD Fellows for the 2026-2027 academic year:

Zhepei Wei is a Ph.D. candidate in the Computer Science department at the University of Virginia, advised by Professor Yu Meng. Zhepei has held research positions at Microsoft Research, Meta and Amazon, working on large language models (LLMs). His first-authored research papers have been published in top-tier venues in the fields of machine learning and artificial intelligence (e.g., NeurIPS, ICML, ICLR) with over 1,400 citations on Google Scholar. He is also a recipient of the UVA Copenhaver Charitable Trust Bicentennial Fellowship and the John A. Stankovic Outstanding Graduate Research Award.

Zhepei's core research interest lies in the learning foundations of LLMs and their applications to practical problems (e.g., question answering, logical reasoning, agentic workflows), with a focus on efficiency, trustworthiness and generalizability. His work enables fast, verifiable and autonomous LLM-powered systems that can be deployed at financial scale, including efficient fraud scoring and credit analysis, grounded compliance review with traceable citations and robust automated workflows for financial operations.

Zhenyu Lei is a Ph.D. student in electrical and computer engineering at the University of Virginia, advised by Professor Jundong Li. Before joining UVA, he earned his B.S. in physics (Honors Graduate) from Xi'an Jiaotong University. 

His research centers on making LLM reasoning more efficient and reliable. Specifically, he works on reasoning distillation, which compresses powerful models into smaller ones that retain strong reasoning ability, and on reasoning editing, which corrects failures in model behavior and enables LLMs to reason reliably over their lifetime. Building on this foundation, he is excited to extend his work to the financial domain, where efficient and trustworthy reasoning is critical for real-world decision-making. He has published over twenty papers at leading venues including ICLR, AAAI, and ACL, with three oral presentations.

Congratulations to our award recipients!

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