Siyuan Guo one of Forbes` 30 Under 30 in Science

Tübingen – Forbes has released its eleventh annual 30 Under 30 Europe list in the Science and Healthcare category, recognizing some of Europe’s most promising young innovators, entrepreneurs, and changemakers who are shaping the future of science and technology.

Among this year’s honorees is Siyuan Guo, a former Ph.D. student in the Empirical Inference Department at the Max Planck Institute for Intelligent Systems (MPI-IS). She participated in the Cambridge – Tübingen Ph.D. Fellowship program, conducting research at the intersection of machine learning, physics, and causality.

Forbes selected Guo for her groundbreaking work on advancing the theoretical foundations of artificial intelligence. While modern AI systems often achieve impressive results, they frequently operate as “black boxes,” relying heavily on trial and error, because their principles aren’t completely understood. Guo’s research addresses this limitation by developing a theoretical framework to better understand how these systems work. “Her Ph.D. research showed how basic physics principles help describe how complex models work,” it says in the press statement. 

During her doctoral studies, Guo focused on building probabilistic foundations of causality. She was supervised by Bernhard Schölkopf, Director of the Empirical Inference Department at MPI-IS and Director of the ELLIS Institute Tübingen, as well as Ferenc Huszár, Professor in Machine Learning at the University of Cambridge.
Siyuan Guo seeks to understand learning and intelligence from first principles. She finds intriguing answers at the intersection of physics and machine learning. Siyuan hypothesises that, similar to light travelling and Newtonian mechanics, learning also adheres to the laws of physics – specifically, the principle of least action.

Originally from Hangzhou, China, Guo developed an early interest in STEM disciplines, particularly mathematics, physics, and computer science. She earned both her bachelor’s and master’s degrees in mathematics from the University of Cambridge, as well as a Master of Science in machine learning from University College London. She began her Ph.D. in computer science at Cambridge in October 2021 and joined MPI-IS the following year.

Reflecting on her path, Guo says:
“My desire to become a scientist formed during my Ph.D. I was really fascinated by exploring the unknown and felt incredibly fortunate to do so alongside equally passionate people. To me, science is a deeply rewarding profession driven by human curiosity.”

She adds:
“The work recognized by Forbes, which I call the ‘Physics of Learning’, emerged from a desire to move beyond trial-and-error approaches in machine learning. I wanted to understand the fundamental nature of intelligence. This curiosity directly motivated my research into the ‘Physics of Learning’.”

Siyuan Guo has lectured widely on her results, including invited talks at the Flatiron Institute, Lawrence Berkeley National Laboratory, ELLIS DeepMind Seminar, the University of Oxford, and UCL. In 2025, she was awarded the MIT EECS Rising Star award. She was also in New York City for an internship with Meta’s FAIR team. She has received funding and support from the G-Research PhD prize by the University of Cambridge, she was awarded the MPI-IS Outstanding Female Doctoral Student prize, and won the Premium Research Studentship.

Looking ahead, Guo is continuing her work as an AI Scientist at Prior Labs, where she aims to further advance the understanding of intelligence and develop next-generation AI systems.