Stéphane Mallat: A Visionary at the Intersection of Mathematics and Compute

Published Date : 12/09/2025 

Stéphane Mallat, a pioneer in the field of data science, has been awarded the 2025 CNRS Gold Medal for his groundbreaking work in artificial intelligence and signal processing. His interdisciplinary approach has bridged mathematics, applied mathematics, and computer science, opening new avenues of research and innovation. 

Stéphane Mallat, the founder of the Data Science Chair at the Collège de France, is renowned for his innovative research that bridges mathematics, applied mathematics, and computer science. Throughout his career, Mallat has combined scientific rigor, technological impact, and interdisciplinary vision, significantly influencing the fields of signal and image processing and modern artificial intelligence.

The CNRS Chairman & CEO Antoine Petit highlighted Mallat’s achievements: “From wavelet theory to generative AI, Stéphane Mallat’s innovative research has profoundly transformed signal and image processing, in addition to modern AI. In awarding him the 2025 Gold Medal, the CNRS recognizes his exceptional career at the intersection of mathematics and computer science, that of a visionary scientist of international renown. This distinction is a reminder that all of today’s developments in artificial intelligence stem from basic research. It highlights the young discipline of data science, which relies on algorithms and is based on the French tradition of mathematical excellence.”

Mallat’s career path exemplifies the transition from theoretical research to industrial application. He has developed his work into practical technologies, filing 10 international patents. His fascination with the concrete applications of his research has led him to maintain active dialogue with the world of innovation. In 2001, he founded the Let It Wave start-up, which he led until 2007, focusing on the development of super-resolution chips for high-definition video. His career, marked by transitions from academic research to entrepreneurship, underscores his commitment to linking basic science and technological impact.

Born in 1962 in Suresnes, Mallat began his academic journey at École Polytechnique in 1981. He completed his PhD at the University of Pennsylvania in 1988 and defended his authorisation to supervise research thesis in mathematics at the Université Paris-Dauphine in 1992. From 1995 to 1998, he served as Professor of Mathematics and Computer Science at New York University’s Courant Institute. After spending years in the United States, Mallat returned to France, heading the Applied Mathematics Department at Polytechnique from 1998 to 2001. He continued as a professor there until 2012, when he joined the Computer Science Department of the École Normale Supérieure. In 2017, he was appointed Professor at the Collège de France, where he holds the Data Science Chair.

Mallat’s current work focuses on the mathematical modeling of neural networks, aiming to explain the foundations of artificial intelligence. He is also a member of the French Academy of Science, the French Academy of Technologies, and the National Academy of Engineering in the United States. As an outstanding teacher, Mallat has trained multiple generations of students in engineering schools and universities, both in France and abroad, and has supervised over 20 doctoral theses.

Mallat is deeply committed to the transmission and sharing of knowledge at the interface between artificial intelligence, mathematics, and computer science. He has initiated multiple structuring projects, including Challenge Data, an online platform offering competitions in data processing for students and scientists. Since 2021, as part of his Chair at the Collège de France, Mallat has developed this initiative into a unique platform where challenges are proposed by public organizations, companies, and research laboratories based on concrete issues in climate science, biology, and other fields. He also launched MathAData, an initiative targeting secondary school students to motivate and teach them mathematics through concrete data analysis problems in artificial intelligence.

Throughout his career, Mallat has received numerous prestigious awards, including the EADS Grand Prize from the French Academy of Science in 1997, the 2004 CNRS prize for the most widely-cited French researcher in engineering science, and the Milner Award from the Royal Society in 2023. He was the winner of the 2013 CNRS Innovation Medal and was made an Officer of the Legion of Honour in 2024. In the same year, he won the Sustained Impact Award over 20 years from the Institute of Electrical and Electronics Engineers (IEEE). 

Frequently Asked Questions (FAQS): 

Q: What is the CNRS Gold Medal?

A: The CNRS Gold Medal is the highest scientific distinction awarded by the French National Centre for Scientific Research (CNRS). It recognizes exceptional contributions to scientific research and innovation.

Q: What are some of Stéphane Mallat's notable achievements?

A: Stéphane Mallat is known for his work in wavelet theory, generative AI, and the mathematical modeling of neural networks. He has received numerous awards, including the EADS Grand Prize, the CNRS prize for the most widely-cited French researcher, and the Milner Award.

Q: What is the Data Science Chair at the Collège de France?

A: The Data Science Chair at the Collège de France is a position held by Stéphane Mallat, focusing on the interdisciplinary study of data science, combining mathematics, computer science, and applied mathematics.

Q: What is the Challenge Data platform?

A: Challenge Data is an online platform initiated by Stéphane Mallat, offering competitions in data processing for students and scientists. It proposes challenges based on real-world issues in various fields such as climate science and biology.

Q: What is MathAData?

A: MathAData is an initiative launched by Stéphane Mallat to motivate and teach secondary school students mathematics through concrete data analysis problems in artificial intelligence. 

More Related Topics :