Mini-Symposium and Tutorial: ‘Numerical implementation of Bayesian filtering for signal equalisation and demodulation’
The aim of this mini-symposia is to facilitate high-level discussions on research challenges of Bayesian filtering implemented in telecomms applications.
28th October 2020 virtually and online (due to the COVID-19 pandemic).
All times are CET, Paris, Berlin, Rome time.
A participation link has been emailed. Use this same link for both sessions.
|Session 1||Chair: Vladislav Neskorniuk (FONTE ESR; Aston University; UK)|
|09:20-09:30||Sergei K. Turitsyn
Director of the Aston Institute of Photonic Technologies (AiPT)
and Coordinator of Project FONTE
Aston University, UK
Associate Professor in Sensor Informatics and Medical Technology;
Department of Electrical Engineering and Automation;
Aalto University, Finland
Title: Introduction to Bayesian Filtering
Associate Professor in Machine Learning in Photonic Systems;
Department of Photonics Engineering;
Technical University of Denmark (DTU)
Title: Application of Bayesian filtering for laser and frequency comb noise characterization
Prof. Dr.-Ing. in the Communications Engineering Lab (CEL)
Karlsruher Institut für Technologie, Germany
Title: Bayes’ Theorem and the BCJR Algorithm – Swiss Army Knife for Communication Engineers
|Session 2||Chair: Abtin Shahkarami (FONTE ESR; Telecom Paris; France)|
Postdoctoral Research Fellow
Department of Physics
Technical University of Denmark (DTU)
Title: Bayesian filtering for quantum communication
|15:15-16:15||Zhe (Sage) Chen
Associate Professor of Psychiatry, Neuroscience and Physiology;
School of Medicine;
New York University, USA
Title: Bayesian filtering: history, new tools and applications
Darko Zibar is Associate Professor at the Department of Photonics Engineering, Technical University of Denmark and the group leader of Machine Learning in Photonics Systems (M-LiPS) group. He received 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 has been on several occasions (2006, 2008 and 2019) visiting researcher with the Optoelectronic Research Group led by Prof. John E. Bowers at the University of California, Santa Barbara, (UCSB). At UCSB, he has been working on topics ranging from analog and digital demodulation techniques for microwave photonics links and machine learning enabled ultra-sensitive laser phase noise measurements techniques. In 2009, he was a visiting researcher with Nokia-Siemens Networks, working on clock recovery techniques for 112 Gb/s polarization multiplexed optical communication systems. In 2018, he was visiting Professor with Optical Communication (Prof. Andrea Carena, OptCom) group, Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino working on the topic of machine learning based Raman amplifier design. His resrearch efforts are currently focused on the application of machine learning technqiues to advance classical and quantum optical communication and measurement systems. Some of his major scientific contributions include: record capacity hybrid optical-wireless link (2011), record sensitive optical phase noise measurement technique that approaches the quantum limit (2019) and design of ultrawide band arbitrary gain Raman amplifier (2019). He is a recipient of Best Student paper award at Microwave Photonics Conference (2006), Villum Young Investigator Programme (2012), Young Researcher Award by University of Erlangen-Nurnberg (2016) and European Research Council (ERC) Consolidator Grant (2017). Finally, he was a part of the team that won the HORIZON 2020 prize for breaking the optical transmission barriers (2016).
Simo Särkkä is Associate Professor in Sensor informatics and medical technology at Department of Electrical Engineering and Automation (EEA), Aalto University
Simo received his Master of Science (Tech.) degree in engineering physics and mathematics, and Doctor of Science (Tech.) degree in electrical and communications engineering from Helsinki University of Technology, Espoo, Finland, in 2000 and 2006, respectively. Currently, he is an Associate Professor in Aalto University, Technical Advisor of IndoorAtlas Ltd., and an Adjunct Professor with Tampere University and LUT University. He is also a Fellow of European Laboratory for Learning and Intelligent Systems (ELLIS), and he is the leader of AI Across Fields (AIX) program and AI for Health SIG in Finnish Center for Artificial Intelligence (FCAI). From 2000 to 2010 he worked with Nokia Ltd., Indagon Ltd., and Nalco Company in various industrial positions related to telecommunications, positioning systems, and industrial process control. From 2010 to 2013 he worked as a Senior Researcher with the Department of Biomedical Engineering and Computational Science (BECS) at Aalto University, Finland, and also held the position of Academy Research Fellow for 2013-2018.
His and his group’s research interests are in multi-sensor data processing systems with applications in location sensing, health and medical technology, machine learning, inverse problems, and brain imaging. He has authored or coauthored around 150 peer-reviewed scientific articles and his books “Bayesian Filtering and Smoothing” and “Applied Stochastic Differential Equations” along with the Chinese translation of the former were recently published via the Cambridge University Press. He is a Senior Member of IEEE, member of IEEE Machine Learning for Signal Processing committee, and serving as a Senior Area Editor of IEEE Signal Processing Letters.
Prof. Dr.-Ing. Laurent Schmalen
Laurent Schmalen is a full professor at Karlsruhe Institute of Technology (KIT) in Karlsruhe, Germany, where he heads the Communications Engineering Lab (CEL). From 2011 to 2019, he was a member of technical staff and department head at Nokia Bell Labs in Stuttgart, Germany. He joined Bell Labs after receiving his Ph.D. from RWTH Aachen University in Aachen, Germany. His research topics include forward error correction algorithms and digital coded modulation schemes for high-speed optical communications. He received multiple awards for his research work, including the 2016 Journal of Lightwave Technology Best Paper Award, has more than 120 publications in journal and conference papers, has co-authored 4 book chapters and holds several patents.
Home page: http://www.cel.kit.edu/team_schmalen.php
Hou-Man is a Postdoctoral Research Fellow working on Machine Learning aided DSP for CV-QKD, system performance ,simulation and verification.
Previously an Early Stage Researcher in the Marie Curie ICONE Initial Training Network Fellow Hou-Man worked on the design of next generation optical networks at Orange Polska as a part of the ITN, investigating provisioning strategies to enable greater network capacity throughput. He also has an interest in digital signal processing for compensation of signal impairments due to optical fibre and component properties.
Hou-Man’s work was been in the area of optical networks, specifically the design and implementation of optical networks incorporating cognitive flexible transceivers. Most recently he is investigating the provisioning of optical links with respect to link deviations from the ideal design in anticipation of increased network dynamics due to technologies such as flexgrid, elastic optical networking and software defined networking being a key component of next generation optical networks.
Hou-Man has extensive laboratory experience from the implementation of 10 Gbit/s OOK and BPSK channels to more advanced coherent DP-QPSK and DP-16QAM up to 35 Gbaud using both lab equipment and commercially available products. He designed the experimental test setups used at UCL and implemented the automation required to perform measurements of very large data sets and visited Ciena Corporation in Ottawa to avail of their comprehensive laboratory facilities to perform DWDM measurements using the latest available commercial modems as a continuation of his work in UCL.
Hou-Man’s specialties are: Digital Signal Processing; Optical measurements and lab work; Experimental design; Measurement automation; Troubleshooting and debugging of complex optical transmission systems.
Zhe (Sage) CHEN is a principal investigator at New York University School of Medicine (NYUSOM), where Ihe direct the Computational Neuroscience, Neuroengineering and Neuropsychiatry (CN3) Laboratory. He is also a faculty member of the Training Program of Computational Neuroscience at NYU and an affiliated faculty member of the Neuroscience Institute at NYUSOM.
Zhe is a computational neuroscientist and an electrical engineer. His work has focused on using statistical/biophysical modeling, computational statistics, and machine learning methods to help understand representations of neuroscience data, to decipher important neural circuit mechanisms of targeted brain circuits, such as the hippocampus, prefrontal cortex, and motor cortex of rodents and non-human primates. In collaboration with experimental neuroscientists, histeam has developed real-time brain-machine interfaces to conduct closed-loop neuroscience experiments, such as detecting acute pain signals for acute pain signals for pain neuromodulation, and decoding animal’s trajectory paths during off-line memory reactivation. Finally, Zhe has been gradually building his research portfolio along the direction of computational psychiatry and translational neuroscience, aiming to bridge the gap between animal and human research.
Zhe’s current and future research directions focus on the intersection of computational and systems neuroscience, neural engineering, and data science.