For more than two decades, NVIDIA has supported graduate students doing outstanding work relevant to NVIDIA technologies through the NVIDIA Graduate Fellowship Program. Today we’re announcing the latest awards of up to $50,000 each to five Ph.D. students involved in research that spans all areas of computing innovation.
Selected from a highly competitive applicant pool, the awardees will participate in a summer internship preceding the fellowship year. The work they’re doing puts them at the forefront of accelerated computing, with fellows tackling projects in deep learning, robotics, computer vision, computer graphics, circuits, autonomous vehicles and programming systems.
“Our fellowship recipients are among the most talented graduate students in the world,” said NVIDIA Chief Scientist Bill Dally. “They’re working on some of the most important problems in computer science, and we’re delighted to support their research.”
The NVIDIA Graduate Fellowship Program is open to applicants worldwide.
Our 2023-2024 fellowship recipients are:
- Eric Chan, Stanford University — Designing generative models for 3D objects and scenes. The work enables AI that can synthesize objects and scenes for content creation, or reconstruct real-world environments for 3D perception. Applications include photorealistic 3D avatars, text-to-object generation and computer vision for robotics.
- Katie Luo, Cornell University — Focusing on perception for self-driving vehicles, with a goal of bringing self driving to a diverse set of end users — and exploring additional channels of information to adapt to diverse, real-world scenarios in a data-efficient manner.
- Rohan Yadav, Stanford University — Building parallel programming languages and systems to accelerate software development for distributed and accelerated computing platforms, with a focus on systems for dense and sparse tensor computations on distributed machines.
- Yicheng Zhu, University of California, Berkeley — Exploring extreme-performance hybrid switched-capacitor voltage regulation modules for ultra-high-power GPUs, which enables highly efficient and ultra-compact vertical power delivery with fast transient response.
- Zongyi Li, California Institute of Technology — Working on developing deep learning methods (neural operators) for partial differential equations and scientific applications.
We also acknowledge the 2023-2024 fellowship finalists:
- Bobbi Yogatama, University of Wisconsin
- Charles Gouert, University of Delaware
- Chen Wang, Stanford University
- Cheng Chi, Columbia University
- Ethan Tseng, Princeton University
- Hong-Xing (Koven) Yu, Stanford University
- Jiarui Xu, University of California, San Diego
- Kaidi Cao, Stanford University
- Shikhar Bahl, Carnegie Mellon University
- Songwei Ge, University of Maryland