International workshop: Machine Learning Meets Photonics

Professor J.Nathan Kutz, Washington University
Deep Learning Architectures for Science and Engineering Discovery and Design

Bio:
J. Nathan Kutz is the Robert Bolles and Yasuko Endo Professor within the Department of Applied Mathematics at the University of Washington in Seattle. His main research interests involve non-linear waves and coherent structures (especially in fibre lasers), as well as dimensionality reduction and data-analysis techniques for complex systems.
He graduated from the University of Washington with a B.S. in 1990 and obtained his PhD in 1994 at Northwestern University, supervised by William L. Kath.
He is the author of the book Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data (Oxford Univ. Press, 2013). He also delivers on a regular basis Scientific Computing and Computational Methods for Data Analysis as Massive Open Online Courses (MOOCs) on the Coursera platform.
He was elected as a Fellow of the Society for Industrial and Applied Mathematics, in the 2022 Class of SIAM Fellows, “for contributions to applied dynamical systems, machine learning, and nonlinear optics.

Professor Claudio Conti, Sapienza University of Rome
Classical High-dimensional Computing

Bio:
CLAUDIO CONTI is the author of over 250 articles published in top-level journals (including 36 Physical Review Letters, 1 Nature, 1 Science, 2 Nature Physics, 5 Nature Communications, and 4 Nature Photonics). Among his recent interests is the merging of concepts in the science of complexity, machine learning, photonics, and nonlinear optics towards applications such as Ising machines and fundamental tests of quantum mechanics. He is head of the Nonlinear Optics Laboratory and Professor of “Experimental Optics” and “Nonlinear Waves and Solitons” at the Department of Physics of the University Sapienza in Rome (IT). CC is also the Scientific Director of the Laboratory of Photonic Technologies at the Research Center Enrico Fermi.

Dr Francesca Parmigiani, Microsoft Research Cambridge, UK
Accelerating ML models and NP-hard optimisation problems using light

Bio:
Dr Francesca Parmigiani is a member of the Optics for the Cloud at Microsoft Research Cambridge, UK, and a visiting staff at the Optoelectronics Research Centre (ORC), University of Southampton, UK. Her current research focuses on the development of new optical technologies in the rapidly growing field of networking and computing for the cloud. She is Topical Editor for Optics Letters, OSA, since January 2019 and is/has been Technical Programme Committee member for various prestigious optical communications technology conferences. Before joining Microsoft in July 2018, she was Principal Research Fellow at the ORC, which she had joined to carry out her PhD.  She was author/co-author of more than 200 publications in the highest prestigious optical communications technology journals and conferences and of 5 chapters in various different published books (e.g. Elsevier and Wiley). She has been working in several research projects such as EU PHASORS, EU TRIUMPH, EU FP7 ICT-BONE and EPSRC First Grant SPEED (EP/P026575/1).

Assistant Professor Christian Haeger, Chalmers University of Technology
Machine learning opportunities for integrated polarization sensing and fiber-optic communication

Bio:
Christian Häger is an assistant professor in the Communication Systems research group. He received the Dipl.-Ing. degree (M.Sc. equivalent) in electrical engineering from Ulm University, Germany, in 2011 and his Ph.D. degree in communication theory from Chalmers University of Technology, Sweden, in 2016. From 2016 until 2019, he was a postdoctoral researcher at the Department of Electrical and Computer Engineering at Duke University, USA. Since 2017, he is a postdoctoral researcher at the Department of Electrical Engineering at Chalmers University of Technology. His research interests include modern coding theory, fiber-optic communications, and machine learning. He received the Marie Sklodowska-Curie Global Fellowship from the European Commission in 2017.

Dr Mario Krenn, Max Planck Institute for the Science of Light
De-novo design of physics experiments with artificial intelligence

Bio:
Mario Krenn is a research group leader of the Artificial Scientist Lab at the Max Planck Institute for the Science of Light (Theory Division). He is excited about the potential of artificial intelligence-inspired and -augmented science, and how we can use algorithms in a more “creative” way. To make progress, he believes it will be important to learn what humans mean by crucial scientific concepts such as surprising, creativity, understanding, or interest. He has created AIs for designing quantum experiments and hardware (several actually built in labs) and inspiring novel ideas for quantum technologies. M. Krenn also builds autonomously semantic networks from scientific publications, and uses machine learning to predict and suggest personalised future research questions and ideas. In that sense, he uses the machine as a source of inspiration to accelerate scientific progress. Ultimately, he wants to create algorithms that help us to uncover the secrets of the Universe.

Professor Peter Bienstman, Photonics Researcg Group, Ghent University – imec
Neuro-inspired computing with silicon integrated photonics

Bio:
Peter Bienstman was born in Ghent, Belgium, in 1974. He received a degree in electrical engineering from Ghent University, Belgium, in 1997 and a Ph.D. from the same university in 2001, at the Department of Information Technology (INTEC), where he is currently a full professor. His research interests include several applications of nanophotonics (biosensors, photonic information processing, …) as well as nanophotonics modelling. He has published over 110 papers and holds several patents. He has been awarded a ERC starting grant for the Naresco-project: Novel paradigms for massively parallel nanophotonic information processing.

Professor Serge Massar, Université libre de Bruxelles (ULB)
Photonic Neuromorphic Computing in the Frequency Domain

Bio:
Serge Massar is a Belgian physicist and professor at the Université libre de Bruxelles (ULB), where he leads the Laboratoire d’Information Quantique. His research focuses on quantum information theory, quantum measurement, entanglement, and quantum non-locality. He is a pioneer in “device-independent” quantum cryptographic tasks, enhancing the security of quantum key distribution and random number generation. Massar has also made significant contributions to theoretical computer science, solving longstanding problems such as the Travelling Salesman Problem, and to experimental physics, including advancements in quantum optics, non-linear optics, and the discovery of novel properties in silicon waveguides. His recent work explores artificial intelligence and machine learning, with a focus on ultra-fast computation using optical systems.
Amongst other recognitions, he was awarded the Alcatel-Bell prize of the FNRS for his research on experimental quantum information processing, the 2010 La Recherche prize for his work on certified quantum random number generation, and was awarded the best paper award at the highly competitive Symposium on Theory of Computing (STOC) in 2012 for his work on the Travelling Salesman Problem. He has co-authored over 130 publications in peer reviewed scientific journals and over 70 conference proceedings.

Professor Darko Zibar, Technical University of Denmark – DTU
End-to-end learning for fiber optic communication systems

Bio:
Darko Zibar received the M.Sc. degree in telecommunication and the Ph.D. degree in optical communications from the Technical University of Denmark, in 2004 and 2007, respectively. He was a Visiting Researcher with the Optoelectronic Research Group (Prof. John E. Bowers), University of California, Santa Barbara, CA, USA, in 2006 and 2008, where he worked on coherent receivers for analog optical links. In 2009, he was a Visiting Researcher with Nokia-Siemens Networks, where he worked on clock recovery techniques for polarization multiplexed systems. He is currently group leader of Machine Learning in Photonic Systems (MLiPS) and Associate Professor at DTU Fotonik, Technical University of Denmark. His research efforts are currently focused on the application of machine learning methods to optical communication, ultra-sensitive amplitude and phase detection, quantum meatrology and optical fibre sensing systems. He is a recipient of Young Researcher Award by University of Erlangen-Nurnberg, in 2016, for his contributions to applications of machine learning techniques to optical technologies. He was a part of the team that won the HORIZON 2020 prize for breaking the optical transmission barriers. In 2017, he was granted European Research Council (ERC) Consolidator Grant where the focus is on the demonstration of nonlinear-distortion free optical communication systems using machine learning techniques.

Associate Professor Miguel C. Soriano, Institute for Cross-Disciplinary Physics and Complex Systems (IFISC)
Unconventional Computing with Nonlinear Photonic Systems

Bio:
Miguel C. Soriano is a tenured scientist at the Spanish National Research Council (CSIC), currently based at the Institute for Cross-Disciplinary Physics and Complex Systems (IFISC) in Mallorca, Spain. His research focuses on nonlinear dynamics and information processing, particularly through reservoir computing. He also investigates the experimental and theoretical aspects of semiconductor lasers subjected to delayed optical feedback and explores the synchronization properties of semiconductor lasers operating in chaotic regimes. Dr. Soriano earned his PhD with greatest distinction from the Department of Applied Physics and Photonics at the Vrije Universiteit Brussel (VUB), Belgium, in 2006.
At IFISC, he leads the Nonlinear Physics Laboratory and contributes to the Nonlinear Photonics Lab. His experimental work with nonlinear electronic systems and semiconductor laser diodes targets applications in information processing and communications. He edited several notable publications, including the book “Photonic Reservoir Computing: Optical Recurrent Neural Networks” (July 2019) and special issues such as “Trends in Reservoir Computing” (2021), “Ordinal and Pattern-Based Quantifiers for Nonlinear Time Series Analysis” (2022), and “Neural Network Learning with Photonics and for Photonic Circuit Design” (2023).

Professor Daniel Brunner, CNRS researcher, FEMTO-ST, Optics department, Besancon
Model-Free Training of Fully Hardware Implemented Laser Neural Networks

Bio:
Daniel Brunner is a CNRS researcher with the FEMTO-ST, France. His interests include novel computing using quantum or nonlinear substrates with a focuses on photonic neural networks. He has received several University and the IOP’s 2010 Roys prize, the IOP Journal Of Physics: Photonics emerging leader 2021 prize as well as the CNRS Bronze medal in 2022. He edited one Book and two special issues, has presented his results 50+ times upon invitation, has published 70+ scientific articles, has been awarded a prestigious ERC Consolidator grant and is a pilot of the BEP-PEPR Electronique project of the France 2030 initiative.

Dr Alfredo De Rossi, Thales
All optical signal processing with nonlinear integrated photonics

Bio:
Alfredo De Rossi received the Ph.D. degree from Università Roma Tre, Roma, Italy, in 2002, the M.S. degree in electrical engineering from Università La Sapienza Roma, Italy, in 1997. He has been with Thales Research Center, Palaiseau, since 2000. His expertise is in nonlinear and integrated optics, with particular emphasis on photonic crystals and other subwavelength photonic structures.
Main interests: nonlinear optics, nanophotonics, dynamical systems. Carried out a research activity aiming at the miniaturisation of nonlinear optics and the realisation of functional on-chip nonlinear devices (e.g. soliton on-chip compression, ultra-fast optical gates, wavelength conversion, …) with a special focus on energy efficiency. He coordinated the ICT-FET project COPERNICUS.

Professor Harish Bhaskaran FREng, Oxford University
Photonic device concepts for next-generation computational hardware

Bio:
Professor Harish Bhaskaran, FREng, CEng, FIMechE is the Professor of Applied Nanomaterials at the University of Oxford, where he directs the  Oxford Fab, and serves as the Associate Head for Research of the Mathematical, Physical and Life Sciences Division. He is distinguished for his research achievements in engineering nanoscale devices including breakthrough photonic or neuromorphic computing and displays. He has won global recognition for his multidisciplinary work and leadership in successfully managing large programmes and in commercialising inventions. His research led to the commercialisation of conductive AFM tips (IBM/Nanosensors). He also co-founded three companies to commercialise non-emissive displays, decorative reconfigurable films and recently, Salience Labs in the field of photonic hardware for AI. He is a prolific inventor, including that of the photonic tensor core and pioneer in the fields of photonic computing, NEMS and sustainable nanomanufacturing.

Dr. Folkert Horts, IBM Research Zurich
Photonic Circuits for Neural Network Inference and Training

Bio:
To be updated

Dr. Mirko Goldmann, Akhetonics
From Analog Devices to Digital Systems: Optical Computing for Edge AI

Bio:
Mirko Goldmann received his B.Sc. from the Technical University of Ilmenau and his M.Sc. in Physics from the Technical University of Berlin in 2020. Following his studies, he completed his Ph.D. on the topic “Computing with Dynamical Systems” at the Institute for Cross-Disciplinary Physics and Complex Systems (IFISC, UIB-CSIC), Spain. As a member of the European Training Network “Post-Digital,” his research focused on the development and implementation of unconventional computing paradigms in photonic hardware. Currently, he works as a Photonic Design Engineer at Akhetonics applying his expertise in photonic computing, design automation and optimization of physical systems.

Dr Brandon Redding, U.S. Naval Research Laboratory
Frequency Multiplexed Photonic Next-Generation Reservoir Computing

Bio:
Brandon Redding received the B.S. degree in physics and computer science from Allegheny College, Meadville, PA, USA, in 2005, and the Ph.D. degree in electrical engineering from the University of Delaware, Newark, DE, USA, in 2010.,From 2010 to 2015, he worked as a Post-Doctoral Associate with Yale University. From 2014 to 2015, he worked as a Senior Research Scientist with the Army Research Laboratory. Since 2015, he worked as a Research Physicist with the Naval Research Laboratory. His research interests include light generation and sensing in complex and disordered systems.

Assistant  Prof Bhavin Shastri, Queen’s University, Canada
Photonic and neuromorphic computing: overview and emerging trends

Bio:
Dr. Bhavin J. Shastri is an Assistant Professor of Engineering Physics at Queen’s University, Canada, and a Faculty Affiliate at the Vector Institute for Artificial Intelligence, Canada. He was an Associate Research Scholar (2016-2018) and Banting/NSERC Postdoctoral Fellow (2012-2016) at Princeton University. He received a Ph.D. from McGill University in 2012. Dr. Shastri’s research is on the design and experimental demonstration of integrated photonic devices and systems for neuromorphic computing, artificial intelligence (AI), quantum machine learning, and quantum state tomography. A central theme is optical physics for information processing by unifying nanophotonics and complex systems (e.g. neural networks) on emerging substrates (e.g. 2D materials with silicon photonics). He has published more than 80 journal articles and 100 conference proceedings, seven book chapters, and given over 65 invited talks and lectures, five keynotes, and four tutorials. He is a co-author of the book (CRC Press, 2017) Neuromorphic Photonics, a term he helped coin. Dr. Shastri is the recipient of the 2022 SPIE Early Career Achievement Award and the 2020 IUPAP Young Scientist Prize in Optics “for his pioneering contributions to neuromorphic photonics” from the ICO. He is a Senior Member of Optica and IEEE

Dr Wolfger Peelaers, Hewlett Packard Enterprise
Automated design of photonics accelerators for machine learning

Bio:
Wolfger Peelaers is a senior research scientist at Hewlett Packard Labs, the research division of Hewlett Packard Enterprise. He holds a PhD in theoretical high-energy physics from Stony Brook University (USA). His current research focuses on leveraging machine learning methodologies to design analog devices, circuits, and systems that can serve as accelerators for optimization or machine learning workloads.

Prof Yang Hao, Queen Mary University of London
Material Discovery via Machine Learning and Research Lab Automation

Bio:
Professor Yang Hao (FREng, FIEEE, FIET) is a leading expert in wireless communications, metamaterials, and antennas. He is the QinetiQ/Royal Academy of Engineering Research Chair at Queen Mary University of London and has led £60M+ in research projects. His work in metamaterials and AI-driven materials research has shaped modern electromagnetic technologies.He co-founded Isotropic Systems (All.Space), a satellite communications company, and has supervised 70+ PhD students and postdocs. Elected Fellow of the Royal Academy of Engineering (2020), he has received multiple prestigious awards, including the 2024 European Antenna Award.

Prof David Saad, Aston University 
In vitro human neurons for computing

Bio:
Professor David Saad obtained a BA in Physics and a BSc in Electrical Engineering from the Technion, Haifa, Israel, followed by an MSc in Physics (Relativistic Field Theory) and a PhD in Electrical Engineering (Neural Networks) from Tel Aviv University. In 1992, he joined the Neural Networks Group in the Physics Department at the University of Edinburgh, initially as a postdoctoral researcher and later as a lecturer, focusing on theoretical aspects of neural networks. In 1995, he moved to Aston University, joining the Neural Computing Research Group as a lecturer, before being promoted to Reader in 1997 and Professor in 1999. He served as Head of the Mathematics Group at Aston University from 2006 to 2012 and again from 2015 to 2019.

Professor Antonio Hurtado, UKRI Turing AI Fellow, University of Strathclyde
Photonic Spiking Neurons and Spiking Neural Networks for Light-Enabled Neuromorphic Computing and Sensing

Bio:
Professor Antonio Hurtado is a Reader and Turing AI Fellow at the Institute of Photonics, University of Strathclyde, UK. He earned his PhD from Universidad Politécnica de Madrid (UPM), Spain, and has over 15 years of international research experience in photonics, working in the UK, USA, and Spain. He was awarded two Marie Curie Fellowships and a Chancellor’s Fellowship, leading to his appointment as Lecturer at Strathclyde in 2014. He established and leads the Neuromorphic Photonics Research Group and has secured major funding from UKRI, EPSRC, ONRG, and the EU.
In 2020, he received a Turing AI Acceleration Fellowship to develop a five-year programme on ‘Photonics for Ultrafast AI’.

Professor Laureen Schmalen, Karlsruhe Institute of Technology
Recent advances on machine learning-aided DSP for short-reach and long-haul optical communications

Bio:
Laurent Schmalen is a Full Professor at Karlsruhe Institute of Technology (KIT), Germany, where he co-heads the Communications Engineering Laboratory. He received his Dr.-Ing. degree from RWTH Aachen University in 2011. He previously worked as a research engineer at Alcatel-Lucent Bell Labs (2011-2016) and later led the “Coding for Optical Communications” department at Nokia Bell Labs (2016-2019). His research focuses on channel coding, machine learning, modulation formats, and optical communications. Prof Schmalen has received multiple awards, including the ERC Consolidator Grant and the IEEE Fellowship in 2023. He serves as Associate Editor for IEEE Transactions on Communications and is the TPC co-chair of the 2025 International Symposium on Topics in Coding (ISTC).

Professor John Sous, Yale University
Advancing AI for Reasoning on Physics

Bio:
Professor John Sous is an Assistant Professor in Applied Physics and the Energy Sciences Institute at Yale University. He is also affiliated with the Yale Quantum Institute and the Institute for Foundations of Data Science. Previously, he was an Assistant Professor at UC San Diego, a Gordon & Betty Moore Postdoctoral Fellow at Stanford, and a Postdoctoral Fellow at Columbia University. Prof Sous earned his Ph.D. in Physics from the University of British Columbia in 2019. His research focuses on condensed matter physics, quantum physics, and AI, exploring correlated systems to develop next-generation energy materials and efficient learning systems.

Professor Alan Pak Tao Lau, Hong Kong Polytechnic University
Machine Learning for optical communications and sensing networks

Bio:
Alan Pak Tao Lau received the B.A.Sc. degree in engineering science (electrical option) and the M.A.Sc. degree in electrical and computer engineering from the University of Toronto, Toronto, ON, Canada, in 2003 and 2004, respectively, and the Ph.D. degree in Electrical Engineering from Stanford University, Stanford, CA, USA, in 2008. He then joined The Hong Kong Polytechnic University as an Assistant and he is currently Professor and Associate Head (Promotions and Global Relations) of the Department of Electrical and Electronic Engineering(EEE), The Hong Kong Polytechnic University.  Prof Lau’s research includes analytical modelling, digital signal processing techniques and Machine Learning applications to long-haul and short-reach optical communications, optical performance monitoring, physical-aware optical networking and optical fiber sensing with multiple record-breaking transmission experiments. He collaborates extensively with Industry such as Huawei, Alibaba, Corning, NEC Labs , and helped open sourced the first comprehensive large-scale physical-layer dataset for deployed optical network for Machine Learning and related research for the optical networking community.

Dr Dimitra G. Georgiadou,University of Southampton
Machine Learning for optical communications and sensing networks

Bio:
Dr Dimitra Georgiadou is a UKRI Future Leaders Fellow, leading the Flexible Nanoelectronics Lab in the School of Electronics and Computer Science at the University of Southampton. She also serves as the Deputy Impact Champion and Advertising & Recruiting Coordinator in the UKRI Centre for Doctoral Training in Machine Intelligence for Nanoelectronic Devices and Systems (MINDS-CDT). Dimitra earned her PhD in Chemical Engineering/Organic Electronics from the National Technical University of Athens (NTUA), Greece. Before joining the University of Southampton as a Principal Researcher, she was an Industrial Fellow at the Department of Materials, Imperial College London (ICL), working with PragmatIC, a UK-SME developing flexible radiofrequency electronic devices for the Internet of Things, and Marie Skłodowska-Curie Fellow at the Department of Physics (ICL).

Dr Pedro Freire, Ofcom
Navigating the New Frontier: Opportunities and Challenges in AI Auditing

Bio:
Pedro J. Freire is a Senior Machine Learning Engineer at Ofcom, specialising in AI-driven trust and safety technologies to create a secure digital space for users. With a strong background in deep learning project development, ML auditing, generative AI, and red teaming, he is dedicated to leveraging artificial intelligence for social good. He holds a bachelor’s and a master’s degree in electronic engineering from the Federal University of Pernambuco, Brazil, with additional research experience at the State University of New York and the State University of San Francisco, USA. He later pursued a Marie Skłodowska-Curie (MSCA) Doctoral Fellowship as an Early Stage Researcher at Aston University, Birmingham, U.K., and Infinera, focusing on network monitoring, planning, and advanced digital signal processing. His master’s research introduced a metaheuristic approach using evolutionary algorithms to solve the Routing, Modulation, and Spectrum Allocation (RMLSA) problem in optical networks.

Dr Tigers Jonuzi, VLC Photonics
Reconfigurable time-wavelength integrated convolutional accelerator

Bio:
Obtained the M.Sc in Engineering Physics at the university of Politencnico di Milano (Polimi) with a thesis on the design and control of photonic integrated circuits for light beam manipulation. In the POST-DIGITAL project, attended an industrial Ph.D., hosted by VLC Photonics S.L., where his research focuses on the photonic device integration of neuromorphic computing, spanning from reservoir to convolutional neural network schemes, defining an efficient approach for harnessing and suppressing nonlinear optical processes.  He is currently the PIC Design Manager at VLC Photonics, coordinating both customer and R&D activities on photonic integrated circuits developments. He is co-authored of conference papers spanning analog photonic computing and optical beam manipulation with a great diversity of actuators as optical MEMS, graphene modulators and Phase-Change materials.

Dr Victor Brasch, Q.ANT
Photonic computing with TFLN – concepts to products

Bio:
Victor Brasch is the Head of Architecture at Q.ANT. The mission of Q.ANT is to build commercial photonic computing hardware and software in particular for AI applications. Victor Brasch obtained his PhD in physics from EPFL (Lausanne) on nonlinear integrated photonics in microresonators. After several years as a project leader at CSEM (Neuchatel) he joined Q.ANT 3 years ago. At Q.ANT he helped to develop the TFLN integrated photonics platform before he switched to his current position.