Mini-Symposium: Introduction to photonic reservoir computing
The Mini-Symposium is organised by POST-DIGITAL together with other H2020 ITN projects coordinated by Aston Institute of Photonic Technologies (AiPT): FONTE, MOCCA
24 February 2021 virtually and online (due to the COVID-19 pandemic).
All times are CET, Paris, Berlin, Rome time.
A participation link will be sent.
|Chair: Peter Bienstman, IMEC, Belgium|
Professor at the Department of Information Technology, UGent Belgium
|10:10-11:25|| Herbert Jaeger
Professor of Computing in Cognitive Materials, head of the MINDS group
University of Groningen, The Netherlands
Title: An Introduction to Echo State Networks
|11:35- 12:15|| Lorenzo De Marinis
Institute of Communication, lnformation and Perception Technologies (TeCIP)
Sant’Anna School of Advanced Studies- Pisa, Italy
Title: Photonic Neuromorphic Computing: an overview of optics for AI
|12:15-1:00|| Dr Danijela Markovic
Permanent Researcher at
The French National Centre for Scientific Research, France
Title: Reservoir computing with spin-torque nano-oscillators
Professor at Université de Sherbrooke, Canada
Title: Integrating Sensing and Computing in MEMS
Herbert Jaeger is full Professor for Computing in Cognitive Materials at the Rijksuniversiteit Groningen (RUG) and head of the MINDS Group ”Modeling Intelligent Dynamical Systems”. He studied mathematics and psychology at the University of Freiburg and obtained his PhD in Computer Science (Artificial Intelligence) at the University of Bielefeld in 1994. After a 5-year postdoctoral fellowship at the German National Research Center for Computer Science (Sankt Augustin, Germany) he headed the “Intelligent Dynamical Systems” group at the Fraunhofer Institute for Autonomous Intelligent Systems AIS (Sankt Augustin, Germany). In 2003 he was appointed as Associate Professor for Computational Science at Jacobs University Bremen, where he stayed until he moved to RUG in 2019.
Herbert Jaeger is one of several lucky independent co-discoverers of the “reservoir computing” principle for training recurrent neural networks.
Lorenzo de Marinis received his B.S. degree and M.S. degree magna cum laude in electronic engineering from the University of Pisa, respectively in 2017 and 2019. He was a research scholar at Scuola Superiore Sant’Anna, Pisa from May to September 2019. He is currently a Ph.D. student at Scuola Superiore Sant’Anna working on photonic integrated circuit design for neuromorphic computing with a focus on electronic-photonic codesign and analog computing.
Danijela Markovic has obtained her PhD in quantum physics in 2017 from Ecole Normale Supérieure on the subject of quantum information with superconducting circuits.
For her post-doc, Danijela has joined the group of Julie Grollier at Unité Mixte de Physique CNRS/Thales, where she has worked on neuromorphic computing with spin-torque oscillators and in particular on reservoir computing. Since 2020, Danijela is a permanent researcher in CNRS. She is particularly interested in implementing neuromorphic computing on quantum systems such as superconducting circuits.
Julien Sylvestre received the B.Sc. degree in physics from McGill University (Montréal, Canada) in 1998, and the Ph.D. degree in physics from the Massachusetts Institute of Technology (Cambridge, USA) in 2002, for a thesis in gravitational waves astronomy under the supervision of Rai Weiss (2017 Nobel prize). He was a Post-Doctoral Scholar at the California Institute of Technology and at NASA’s Jet Propulsion Laboratory (Pasadena, USA) until 2004, when he joined the IBM Corporation (Bromont, Canada), where he worked in microelectronics R&D as principal technical professional in the Systems and Technology Group. He joined the University of Sherbrooke in 2014, where he is now a Professor of Mechanical Engineering and chairholder of the NSERC/IBM Canada Industrial Research Chair. He is the founder and CTO of Dekko Technologies. His research interests include many aspects of thermal and mechanical phenomena in microsystems, including numerical simulation methods, cooling, reliability, MEMS and the integration of advanced functionalities, such as photonics and machine learning.