Prof. Hui Cao Yale UniversityCharacterizing ultrashort optical pulses has always been a critical but difficult task, which has a broad range of applications. We propose and demonstrate a self-referenced method of characterizing ultrafast pulses with a multimode fiber. The linear and nonlinear speckle patterns formed at the distal end of a multimode fiber are used to recover the spectral amplitude and phase of an unknown pulse. We deploy a deep learning algorithm for phase recovery. The diversity of spatial and spectral modes in a multimode fiber removes any ambiguity in the sign of the recovered spectral phase. Our technique allows for single-shot pulse characterization in a simple experimental setup. This work reveals the potential of multimode fibers as a versatile and multi-functional platform for optical sensing.Hui Cao is the John C. Malone Professor of Applied Physics and of Physics, and a professor of Electrical Engineering at Yale University. She received her Ph.D. degree in Applied Physics from Stanford University in 1997. Prior to joining the Yale faculty in 2008, she was on the faculty of Northwestern University from 1997 to 2007. Her technical interests and activities are in the areas of mesoscopic physics, complex photonic materials and devices, nanophotonics, and biophotonics. She authored or co-authored one monograph, twelve book-chapters, seven review articles and 250 journal papers. She is a Fellow of the APS, OSA, AAAS and IEEE.Registration is closed.

Prof. Darko Zibar Technical University of DenmarkAccording to the recent data traffic predictions, current optical communication systems, operating in the C--band only, will not be able to satisfy future data rate demands. A viable and long--term solution would be to employ systems operating in multiple bands (O+E+S+C+L) and make usage of the spatial division multiplexing (SDM) (multi--core and multi--mode). Designing optimal signaling, amplification and detection schemes, for such systems, will be challenging due to the high system complexity. Finally, performing system optimization in terms of channel power and bandwidth allocation, as well as modulation format selection, will become difficult using standard tools that rely on analytical or semi--analytical models. What will complicate the matter even further is the focus on providing a secure way of transmitting information using quantum communication. This will require a coexistence and management of classical and quantum channels in the same optical network. As quantum signals have in general significantly, lower powers compared to the signals in classical communication, the reception of quantum signals is more challenging, making a strong case for having intelligent optical receivers that can receive and even distinguish between classical and quantum signals. The field of machine learning (ML) can provide useful tools to address the aforementioned challenges. This is because ML techniques excel at: 1) learning highly--complex input--output mappings which allows for system optimization, 2) learning signaling and detection schemes for complex channels or for channels where analytical models are not available and 3) performing ultra--sensitive signal detection. In this talk, it will be shown how machine learning can enable design of ultrawide-band optical amplifiers, perform constellation shaping over the nonlinear fibre optic channel and enable ultra-sensitive measurements of optical phase that approach the quantum limit.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).Registration is closed.

Prof. Hualiang Zhang University of Massachusetts LowellMetasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods to obtain target electromagnetic (EM) responses, an approach that demands significant efforts to investigate the enormous number of possible meta-atom structures. In this talk, I discuss our research activities in deep learning-based metasurface design, showing how deep learning approaches can be applied for objective-driven meta-atom and meta-device design, opening venues to engineer and realize multifunctional meta-devices and meta-optics. First, I will discuss our deep learning modeling approach for predicting the performance of freeform metasurface structures. Our neural network approach overcomes two key challenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch and accurate EM-wave phase prediction. Second, to demonstrate the capability of deep learning techniques for complex and non-intuitive metasurface design, I will present a novel conditional generative network that can achieve meta-atom/metasurface designs based on different performance requirements. Applications of these deep learning networks will also be discussed.Dr. Hualiang Zhang is an Associate Professor at the Electrical and Computer Engineering Department, University of Massachusetts Lowell. He received his B.S. degree in Electrical Engineering from the University of Science and Technology of China in 2003. He received his Ph.D. degree in Electrical and Computer Engineering from the Hong Kong University of Science and Technology in 2007. From 2007 to 2009 he was a postdoctoral research associate in the Department of Electrical and Computer Engineering at the University of Arizona. From 2009 to Jan. 2016, he was a faculty member at the University of North Texas. His research interests are on applied electromagnetics and high frequency electronics, enabled by advanced computational techniques, new materials, and innovative manufacturing technologies. He is the co-author of over 220 journal and conference papers. He is an associate editor of Wiley’s International Journal of Numerical Modelling – Electronic Networks, Devices and Fields. Dr. Zhang is a senior member of IEEE.Registration is closed.