NVIDIA Awards up to $60,000 Research Fellowships to PhD Students

Source: NVIDIA AI Blog

Overview

For 25 years, the NVIDIA Graduate Fellowship Program has supported graduate students doing outstanding work relevant to NVIDIA technologies. The program announced the latest awards of up to $60,000 each to 10 Ph.D. students whose research spans all areas of computing innovation, including autonomous systems, computer architecture, computer graphics, deep learning, programming systems, robotics, and security.

Awardees will participate in a summer internship preceding the fellowship year. The NVIDIA Graduate Fellowship Program is open to applicants worldwide.

2026‑2027 Fellowship Recipients

• Jiageng Mao, University of Southern California — Solving complex physical AI problems by using diverse priors from internet‑scale data to enable robust, generalizable intelligence for embodied agents in the real world.
• Liwen Wu, University of California San Diego — Enriching realism and efficiency in physically based rendering with neural materials and neural rendering.
• Manya Bansal, Massachusetts Institute of Technology — Designing programming languages for modern accelerators that enable developers to write modular, reusable code without sacrificing the low‑level control required for peak performance.
• Sizhe Chen, University of California, Berkeley — Securing AI in real‑world applications, currently securing AI agents against prompt injection attacks with general and practical defenses that preserve the agent’s utility.
• Yunfan Jiang, Stanford University — Developing scalable approaches to build generalist robots for everyday tasks through hybrid data sources spanning real‑world whole‑body manipulation, large‑scale simulation and internet‑scale multimodal supervision.
• Yijia Shao, Stanford University — Researching human‑agent collaboration by developing AI agents that can communicate and coordinate with humans during task execution, and designing new human‑agent interaction interfaces.
• Shangbin Feng, University of Washington — Advancing model collaboration: multiple machine learning models, trained on different data and by different people, collaborate, compose and complement each other for an open, decentralized and collaborative AI future.
• Shvetank Prakash, Harvard University — Advancing hardware architecture and systems design with AI agents built on new algorithms, curated datasets and agent‑first infrastructure.
• Irene Wang, Georgia Institute of Technology — Developing a holistic codesign framework that integrates accelerator architecture, network topology and runtime scheduling to enable energy‑efficient and sustainable AI training at scale.
• Chen Geng, Stanford University — Modeling 4D physical worlds with scalable data‑driven algorithms and physics‑inspired principles, advancing physically grounded 3D and 4D world models for robotics and scientific applications.

Fellowship Finalists

• Zizheng Guo, Peking University
• Peter Holderrieth, Massachusetts Institute of Technology
• Xianghui Xie, Max Planck Institute for Informatics
• Alexander Root, Stanford University
• Daniel Palenicek, Technical University of Darmstadt