For 25 years, the NVIDIA Graduate Fellowship Program has supported graduate students doing outstanding work relevant to NVIDIA technologies. Today, the program announced the latest awards of up to $60,000 each to 10 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. Their work puts them at the forefront of accelerated computing — tackling projects in autonomous systems, computer architecture, computer graphics, deep learning, programming systems, robotics and security.
The NVIDIA Graduate Fellowship Program is open to applicants worldwide.
The 2026-2027 fellowship recipients are:
- 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.
We also acknowledge the 2026-2027 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
