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:
Key considerations
- Investor positioning can change fast
- Volatility remains possible near catalysts
- Macro rates and liquidity can dominate flows
Reference reading
- https://blogs.nvidia.com/blog/graduate-fellowship-recipients-2026-2027/#content
- https://www.nvidia.com/en-us/
- https://blogs.nvidia.com/?s=
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