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<div class="entry-title img-bg orientation-landscape">For 25 years, the <a href="https://research.nvidia.com/graduate-fellowships" target="_blank" rel="noopener">NVIDIA Graduate Fellowship Program</a> has supported graduate students doing outstanding work relevant to NVIDIA technologies. This month, 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.</div>
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<p>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.</p>
<p>The NVIDIA Graduate Fellowship Program is open to applicants worldwide.</p>
<p>The 2026-2027 fellowship recipients are:</p>
<ul>
<li aria-level="1"><b>Jiageng Mao</b>, 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.</li>
<li aria-level="1"><b>Liwen Wu</b>, University of California San Diego — Enriching realism and efficiency in physically based rendering with neural materials and neural rendering.</li>
<li aria-level="1"><b>Manya Bansal</b>, 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.</li>
<li aria-level="1"><b>Sizhe Chen</b>, 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.</li>
<li aria-level="1"><b>Yunfan Jiang</b>, 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.</li>
<li aria-level="1"><b>Yijia Shao</b>, 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.</li>
<li aria-level="1"><b>Shangbin Feng</b>, 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.</li>
<li aria-level="1"><b>Shvetank Prakash</b>, Harvard University — Advancing hardware architecture and systems design with AI agents built on new algorithms, curated datasets and agent-first infrastructure.</li>
<li aria-level="1"><b>Irene Wang</b>, 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.</li>
<li aria-level="1"><b>Chen Geng</b>, 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.</li>
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<p>The company also acknowledges the 2026-2027 fellowship finalists:</p>
<ul>
<li><b>Zizheng Guo</b>, Peking University</li>
<li aria-level="1"><b>Peter Holderrieth</b>, Massachusetts Institute of Technology</li>
<li aria-level="1"><b>Xianghui Xie</b>, Max Planck Institute for Informatics</li>
<li aria-level="1"><b>Alexander Root</b>, Stanford University</li>
<li aria-level="1"><b>Daniel Palenicek</b>, Technical University of Darmstadt</li>
</ul>
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