Damien Querlioz

Short Bio

Damien Querlioz is a CNRS Research Director at the Centre de Nanosciences et de Nanotechnologies of Université Paris-Saclay and CNRS. He was first trained at Ecole Normale Supérieure Paris as a physicist and at Ecole Supérieure d'Electricité as a microelectronics engineer. He received the Ph.D. degree from Université Paris-Sud in 2009. He was then a Postdoctoral Scholar at Stanford University and at the Commissariat a l'Energie Atomique. 

Damien Querlioz develops new concepts in nanoelectronics relying on bio-inspiration. He believes that nanoelectronics can allow inventing highly energy-efficient forms of memory-centric computing. He investigates stochastic approaches,  the use of spintronics devices for bioinspired systems, and  the connection between bioinspired memories and Bayesian inference. His research interests have also included the physics of advanced nanodevices. He has developed the Wigner Monte Carlo approach to simulate and understand quantum transport in nanometer-scale devices. 

In 2016, Damien Querlioz received the Habilitation degree (HDR). In 2017, he cofounded the interdisciplinary INTEGNANO research group,  with amazing colleagues working around an integrative and multidisciplinary approach to the development of novel charge and spin-based devices, which he led until in 2023. He works with fantastic students and postdocs.  

Damien Querlioz has coauthored 9 book chapters, more than 150 journal articles and conference proceedings and given more than 100 invited talks at national and international workshops and conferences.  He has also coauthored the book "The Wigner Monte-Carlo Method for Nanoelectronic Devices" (London: ISTE; Hoboken: Wiley, 2010) with Philippe Dollfus.  In 2017, he received a CNRS Bronze medal.  He has also been a co-recipient of the 2017 IEEE Guillemin-Cauer Best Paper Award and of the 2018 IEEE Biomedical Circuits and Systems Best Paper Award.

The research of Damien Querlioz has been funded by the European Research Council, the Seventh Framework Programme of the European Union, Horizon 2020, Horizon Europe, Agence Nationale de la Recherche, Région Ile-de-France/DIM NANO-K and Ministère de l'écologie, du développement durable et de l'énergie. Starting 2022, he has been leading the BioElectronPhoton project of PEPR Electronique. 

Research

Artificial Intelligence is transforming computing, but its remarkable progress comes with growing demands for energy and memory. A major part of this challenge comes not just from computation itself, but from storing, moving and updating information.

My research explores how emerging nanotechnologies can make AI more energy-efficient by fundamentally rethinking the relationship between memory, computation and learning.

The brain provides a fascinating source of inspiration. Unlike conventional computers, it tightly integrates memory and computation, and exploits the complex, sometimes stochastic behavior of its basic components. My research builds on two ideas inspired by these observations.

 1.  Bringing memory and computation together

Emerging nanotechnologies offer exciting opportunities in this direction. Devices such as memristors, ferroelectric memories, and magnetic memories can store information in extremely compact structures, and sometimes participate directly in computation.

Together with my students and collaborators, I investigate how to combine these technologies with conventional electronics to build new computing architectures. Our work ranges from neural networks to Bayesian inference, including experimental demonstrations of hybrid memories that support both learning and inference.

Today, this challenge extends beyond storing neural network weights: modern generative AI also needs to manage information that changes continuously as it processes new inputs. This opens fascinating opportunities for new types of memory-centric computing.

2. Exploiting the physics of nanodevices

Conventional computers are designed to perform highly accurate, deterministic operations. To achieve this, they carefully suppress much of the complexity of their underlying devices. But what if the physical properties that make nanodevices imperfect for conventional computing could become useful for AI?

Emerging memories exhibit rich behaviors, including stochastic switching, nonlinear dynamics, and varying degrees of information retention. Instead of eliminating these effects, we explore how to exploit them for computation.

This approach has led us to develop Bayesian machines that compute with probabilities and uncertainty, Ising machines that exploit physical randomness for optimization, and learning algorithms that adapt continuously without forgetting everything they previously learned.

A recurring theme is Bayesian electronics: combining probabilistic algorithms with emerging devices to build AI systems that can evaluate their uncertainty and learn from limited data. See our perspective in Nature Reviews Electrical Engineering.

All this work is highly interdisciplinary and can only happen within a context of strong collaboration, and I am happy to work with an ensemble of fantastic collaborators!

Recent external collaborations: Julie Grollier at the CNRS/Thales lab (Palaiseau), Fabien Alibart at IEMN (Lille), Jacques Droulez and Pierre Bessière at ISIR (Paris), Emmanuel Mazer at LIG (Grenoble), Raphael Laurent at La Poste/ProBAYES (Grenoble), Elisa Vianello and Etienne Nowak at LETI (Grenoble), Jean-Michel Portal and Marc Bocquet at IM2NP (Marseille), Sandip Tiwari at Cornell University (USA), Manan Suri at IIT Delhi (India), Omid Kavehei at the University of Sydney (Australia), Jayasimha Atulasimha at Virginia Commonwealth University (USA). 

Contact Information

Email: damien.querlioz@c2n.upsaclay.fr 

Office Phone: (+33)170270403

Postal Address: Damien Querlioz, Centre de Nanosciences et de Nanotechnologies,  10 Boulevard Thomas Gobert, 91120 Palaiseau, France