The annual NVIDIA Scholarship has been introduced.
For 25 years, the NVIDIA Graduate Fellowship Program has been offering assist for excellent work associated to NVIDIA applied sciences for graduate college students.
Today, this system introduced the ten doctoral college students who received the 2026 fellowship. Each of them will obtain a grant of as much as $60,000 to assist their analysis in all areas of computational innovation.
Their analysis focuses on the frontiers of accelerated computing, together with autonomous programs, laptop structure, laptop graphics, deep studying, programming programs, robotics, and safety.
This yr, 8 out of the ten winners are Chinese. Last yr, 7 Chinese doctoral college students have been chosen, together with alumni from Shanghai Jiao Tong University, University of Science and Technology of China, and Zhejiang University.
Next, let’s study in regards to the data of this yr’s winners.
Jiageng Mao
University of Southern California. Reason for successful: Solve complicated bodily synthetic intelligence issues by leveraging numerous prior data from Internet-scale knowledge, thereby attaining strong and generalizable intelligence for embodied brokers in the actual world.
Data reveals that Jiageng Mao is a doctoral scholar on the University of Southern California. His analysis route is bodily synthetic intelligence, with the objective of making use of synthetic intelligence to the actual world by creating algorithms in fields resembling robotics, laptop imaginative and prescient, and pure language processing. It is known that he’s significantly excited by intuitive physics, massive imaginative and prescient – language (- motion) fashions, and world modeling.
Liwen Wu
University of California, San Diego. Reason for successful: Use neural supplies and neural rendering to enhance the realism and rendering effectivity of bodily based mostly rendering.
Liwen Wu is a doctoral scholar within the Department of Computer Science and Engineering on the University of California, San Diego. Previously, he obtained a grasp’s and a bachelor’s diploma in laptop science from the University of Illinois at Urbana – Champaign. His analysis areas are laptop graphics and 3D imaginative and prescient, and he’s significantly excited by neural rendering, inverse rendering, (neural) look modeling, and 3D reconstruction.
Sizhe Chen
University of California, Berkeley. Reason for successful: Committed to making sure the security of AI in actual – world purposes. Currently, the main focus is on defending AI brokers from immediate injection assaults by means of common and sensible protection measures with out compromising the performance of the AI brokers.
Data reveals that Sizhe Chen’s present principal analysis route is the safety problems with AI in sensible purposes. He beforehand obtained a grasp’s and a bachelor’s diploma in engineering from Shanghai Jiao Tong University. In his view, immediate injection assaults are the first menace to AI brokers, which have prompted precise harm to a number of synthetic intelligence programs of firms resembling Google, OpenAI, and Anthropic. To promote the broader software of LLMs in AI brokers, he has developed a principled, common, and sensible protection mechanism towards immediate injection.
Yunfan Jiang
Stanford University. Reason for successful: Develop scalable strategies to construct common – function robots for each day duties by means of a hybrid knowledge supply that covers actual – world complete – physique operations, massive – scale simulations, and Internet – scale multimodal supervision.
Data reveals that Yunfan Jiang is a 3rd – yr doctoral scholar within the Department of Computer Science at Stanford University. He is supervised by Professor Fei – Fei Li and belongs to the Stanford Vision and Learning Laboratory. His analysis route is the intersection of machine studying and robotics. Previously, he obtained a grasp’s diploma from Stanford University and has additionally served as a analysis intern at NVIDIA GEAR and the Boston Dynamics AI Institute.
Yijia Shao
Stanford University. Reason for successful: Research human – machine collaboration, develop AI brokers that may talk and coordinate with people throughout job execution, and design new human – machine interplay interfaces.
Data reveals that Yijia Shao is a doctoral scholar in pure language processing at Stanford University. She graduated from the Yuanpei College of Peking University with a serious in knowledge science. She began researching machine studying and pure language processing at the moment and has interned at establishments resembling Microsoft Research Asia and the University of California, Los Angeles.
Currently, her analysis pursuits lie in machine studying and pure language processing, and he or she is dedicated to integrating pure language processing fashions (resembling LLMs) into bigger programs.
Shangbin Feng
University of Washington. Reason for successful: Advance mannequin collaboration, enabling a number of machine studying fashions educated by totally different individuals on totally different knowledge to collaborate, mix, and complement one another to attain an open, decentralized, and collaborative future AI.
He entered the University of Washington to pursue a doctoral diploma in 2022. His analysis instructions embody mannequin collaboration, social pure language processing (NLP), networks, and buildings. He graduated from Xi’an Jiaotong University with a bachelor’s diploma in laptop science and expertise and from the University of Washington with a grasp’s diploma in laptop science and engineering.
Irene Wang
Georgia Institute of Technology. Reason for successful: Develop an built-in co – design framework that integrates accelerator structure, community topology, and runtime scheduling to attain massive – scale, vitality – environment friendly, and sustainable AI coaching.
She is at the moment a 3rd – yr doctoral scholar on the Georgia Institute of Technology, supervised by Professor Divya Mahajan. Previously, she obtained a bachelor’s diploma in laptop engineering from the University of British Columbia.
Currently, her analysis pursuits cowl a variety of machine studying programs and laptop structure, with a give attention to optimizing distributed deep – studying infrastructure.
Chen Geng
Stanford University. Reason for successful: Use scalable knowledge – pushed algorithms and physics – impressed ideas to mannequin the 4D bodily world, thereby selling the event of physics – based mostly 3D and 4D world fashions in robotics and scientific purposes.
He is at the moment a doctoral scholar in laptop science at Stanford University, supervised by the properly – recognized scholar Jiajun Wu. In 2023, he obtained an educational diploma in laptop science from Zhejiang University.
His analysis focuses on the intersection of 4D laptop imaginative and prescient, graphics, and machine studying, with a large concern for knowledge – pushed modeling of the bodily world and the appliance of such fashions. He is at the moment smitten by creating a neural – symbolic graphics engine for (inverse) modeling of macro – mechanical programs.
Shvetank Prakash
Harvard University. Reason for successful: Build AI brokers utilizing new algorithms, rigorously chosen datasets, and agent – first infrastructure, and advance {hardware} structure and system design.
He graduated from the Fu Foundation School of Engineering and Applied Science at Columbia University. He entered Harvard University to pursue a doctoral diploma in laptop science in 2021. His analysis pursuits embody extremely – low – energy machine studying programs, laptop structure, and machine studying within the system area.
Manya Bansal
MIT. Reason for successful: Design programming languages for contemporary accelerators, enabling builders to jot down modular and reusable code with out sacrificing the low – degree management required to attain peak efficiency.
She is at the moment pursuing a doctoral diploma in laptop science at MIT. She graduated from Stanford University with a bachelor’s diploma. Her analysis pursuits embody instruments for designing scalable and environment friendly languages for heterogeneous programs.
In addition, there are 5 finalists for the 2026 NVIDIA Scholarship. They are:
- Zizheng Guo, Peking University
- Peter Holderrieth, MIT
- Xianghui Xie, Max Planck Institute for Informatics
- Alexander Root, Stanford University
- Daniel Palenicek, Technische Universität Darmstadt
Official web site hyperlink:
https://blogs.nvidia.com/weblog/graduate-fellowship-recipients-2026-2027/
This article is from the WeChat official account “Almost Human” (ID: almosthuman2014). Author: Someone involved about AI. It is printed by 36Kr with authorization.