After Fei-Fei Li, a disciple of Kaiming He won another AI award, and 28 Fellows including Tsinghua alumni were on the list.

Just now, the list of selected scholars for the "AI2050" Scholarship was announced!

The goal of this initiative is to ensure the inclusive and beneficial development of AI, with a total award amount exceeding $18 million.

A total of 28 scholars have been selected this time, focusing on three major directions: Building an AI scientist system, designing more secure and trustworthy AI models, and enhancing the application of AI in biomedical research.

This scholarship is supported by the Schmidt Science Fellows, funded by the former CEO of Google.

Twenty-one early-career researchers and seven senior researchers will receive three-year funding.

This is the fourth edition of the AI2050 project. To date, the project has funded a total of 99 researchers in 42 institutions across eight countries globally.

Renowned Chinese scientists such as Fei-Fei Li and Percy Liang have been selected as senior fellows. Earlier this year, Chaowei Xiao, an alumnus of Tsinghua University, was included in the previous "AI2050" list.

See the list of the newly selected scholars at:

https://www.schmidtsciences.org/2025-ai2050-fellows-announcement/

AI2050 is divided into senior fellows and early-career fellows. Specifically, senior fellows are selected based on their existing contributions through a closed nomination process and do not require an application. Early-career fellows are required to hold postdoctoral or pre-tenure research positions.

In addition to the funding itself, the selected scholars will be invited to participate in annual academic exchange activities to share their research findings, expand their cooperation networks, and have the opportunity to receive additional collaborative research support.

Since its launch in 2022, AI2050 has also established a special fund to support significant computing needs, helping the selected researchers overcome hardware limitations and accelerate the research process.

Senior Fellows

A total of seven scholars with tenure have been selected as senior fellows this time.

Alán Aspuru-Guzik

Alán Aspuru-Guzik is a professor in the Department of Chemistry and Computer Science at the University of Toronto.

He is a Fellow of the Royal Society of Canada.

His research focuses on the intersection of quantum information, machine learning, and chemistry. He has accelerated the discovery of materials such as organic semiconductors, organic photovoltaics, and organic light-emitting diodes. He has also conducted foundational research in molecular characterization and generative models.

In the "AI2050" program, he is committed to creating a "AI Chemist" (AIchemist) – a professional AI scientist capable of collaborating with human researchers. This project combines advanced AI technologies (including large language models and agent systems) to accelerate scientific discovery in the field of chemistry.

The core of the project is to build an intelligent system called "El Agente" that can independently explore the chemical space, discover new compounds, and thus address global challenges such as climate change and pandemics, improving the efficiency of new material synthesis and testing.

Surya Ganguli

Surya Ganguli is a professor in the Department of Applied Physics at Stanford University, the deputy director of the Stanford Human-Centered Artificial Intelligence Institute (HAI), and a venture partner at General Catalyst.

He holds three degrees from the Massachusetts Institute of Technology (MIT) in physics, mathematics, and electrical engineering and computer science (EECS). He later completed his Ph.D. in string theory at the University of California, Berkeley, and conducted postdoctoral research in theoretical neuroscience at the University of California, San Francisco (UCSF).

He has served as a visiting researcher at Google and Meta AI and was also a venture partner at a16z (Andreessen Horowitz).

His research spans AI, physics, and neuroscience, with a core focus on understanding and enhancing how biological and artificial neural networks achieve complex and wonderful emergent computations through learning.

He has received numerous awards and honors, including two Outstanding Paper Awards at NeurIPS, the Sloan Research Fellowship, the NSF CAREER Award, and the Schmidt Science Polymath Award.

Ganguli's research project aims to establish a solid scientific foundation for interpretable and trustworthy AI, revealing the core mechanisms of generative models and large language models in the processes of creation, reasoning, and learning.

His team is building an analytical theoretical framework to explain the "creativity" and "reasoning ability" in diffusion models and large language models at the mechanism level.

The ultimate goal of this project is to reveal the core principles behind general intelligence and promote the construction of more interpretable, trustworthy, and human - value - aligned AI systems.

Shirley Ho

Shirley Ho is a renowned American astrophysicist and machine learning expert. She is currently the head of a research group at the Simons Foundation, a professor at New York University, and holds a visiting position at Princeton University.

Shirley Ho was the first to introduce three - dimensional convolutional neural networks into astrophysical research, promoting the scientific application of modern deep learning. In recent years, she has shifted her research focus to innovative methods in interpretable machine learning.

She was elected a Fellow of the International Astrostatistics Association in 2020.

Her AI2050 project is committed to promoting scientific artificial general intelligence (scientific AGI).

In the next three years, she will develop new methods to deeply understand the working mechanisms of various scientific AI models, explore their integration paths, and integrate them with language AI. This project is expected to give rise to the first truly "world - understanding" AI systems, taking a crucial step towards achieving general artificial intelligence with scientific cognitive abilities.

Sheila McIlraith

Sheila McIlraith is a professor in the Department of Computer Science at the University of Toronto, the CIFAR Chair in Artificial Intelligence in Canada (Vector Institute), and the deputy director and research leader of the Schwartz - Reisman Institute for Technology and Society.

She has published over 150 academic papers in the fields of knowledge representation, automated reasoning, and machine learning, with a research focus on "human - centered" artificial intelligence, especially sequential decision - making problems.

Sheila is a Fellow of ACM and AAAI, the current chair of the "AI100" (One Hundred Year Study on Artificial Intelligence) project, and a member of the research council of the Canadian AI Safety Institute.

McIlraith and her collaborators' research results have won numerous awards, including the SWSA Ten - Year Impact Award in 2011, the ICAPS Most Influential Paper Award in 2022, and the IJCAI - JAIR Best Paper Award in 2023.

In the AI2050 project, Sheila McIlraith is committed to endowing AI with the ability of "Purposeful Theory of Mind".

Mutual understanding is the foundation for building trust and cooperation, and "social cognition" ability is the key to enhancing this understanding.

This means that AI should not only be able to recognize and understand the mental states of itself and others, such as beliefs, desires, and intentions, but also actively consider how its decisions and actions will affect the well - being and autonomy of others. Based on this, it should be motivated to choose action paths that balance the interests and dignity of others while achieving its own goals.

This research aims to build an AI system with greater social awareness, moral perception, and cooperation tendency, laying the foundation for trustworthy and mutually beneficial intelligent agents.

Dawn Song

Dawn Song is a professor in the Department of Computer Science at the University of California, Berkeley.

Her research focuses on AI security and assurance, agentic AI, deep learning, security and privacy technologies, and decentralized technologies.

Dawn Song has received numerous top - level international honors, including the MacArthur Fellowship, the NSF CAREER Award, the Sloan Research Fellowship, and has won over 10 "Long - Standing Paper Awards" and Best Paper Awards at top conferences in computer security and deep learning.

She is a Fellow of ACM/IEEE and has been elected to the American Academy of Arts and Sciences.

Dawn Song holds a Ph.D. in computer science from the University of California, Berkeley. She obtained her bachelor's degree from Tsinghua University in 1996 and her master's degree from Carnegie Mellon University in 1999.

Dawn Song's AI2050 project is committed to developing "secure and verifiable" AI tools.

AI is reshaping software development - today's systems can not only generate code but also perform tasks as autonomous agents.

However, these technological breakthroughs also bring unprecedented security challenges: if there are vulnerabilities in the code generated by AI or the behavior of agents, they may be quickly exploited on a large scale, causing serious consequences.

Dawn Song's AI2050 project aims to solve this problem.

These tools can not only automatically write code but also simultaneously generate formal security specifications and mathematical proofs to ensure that the code is logically and securely sound.

By fundamentally eliminating entire classes of security vulnerabilities before deployment, this "provably secure" approach is expected to significantly improve the security and trustworthiness of AI systems, making them more suitable for critical applications with high - security requirements globally.

Philip Torr

Philip Torr is a professor at the University of Oxford.

He obtained his Ph.D. from the Robotics Research Group at the University of Oxford. After graduation, he continued to work as a researcher at Oxford for three years and maintains his status as a visiting scholar to this day.

Later, he worked as a research scientist at Microsoft Research for six years and then became a professor at Oxford Brookes University, focusing on computer vision and machine learning. In 2013, Philip returned to the University of Oxford as a full - professor and founded the Torr Vision Group (TVG).

He has made remarkable achievements in the field of computer vision. In 1