Moe Win
Laboratory for Information and Decision Systems (LIDS)
Massachusetts Institute of Technology

Moe Win is a Professor at the Massachusetts Institute of Technology (MIT) and the founding director of the Wireless Information and Network Sciences Laboratory. Prior to joining MIT, he was with AT&T Research Laboratories and NASA Jet Propulsion Laboratory. His research encompasses fundamental theories, algorithm design, and network experimentation for a broad range of real-world problems. His current research topics include network localization and navigation, network interference exploitation, and quantum information science.

Professor Win is a Fellow of the AAAS, the IEEE, and the IET. He has served the IEEE Communications Society as an elected Member-at-Large on the Board of Governors, as elected Chair of the Radio Communications Committee, and as an IEEE Distinguished Lecturer. He was honored with two IEEE Technical Field Awards: the IEEE Kiyo Tomiyasu Award and the IEEE Eric E. Sumner Award (jointly with Professor R. A. Scholtz). Together with students and colleagues, his papers have received several awards. Other recognitions include the IEEE Communications Society Edwin H. Armstrong Achievement Award, the International Prize for Communications Cristoforo Colombo, the Copernicus Fellowship and the Laurea Honoris Causa from the Università degli Studi di Ferrara, and the U.S. Presidential Early Career Award for Scientists and Engineers. He is an ISI Highly Cited Researcher.

Multiparty Quantum State Transmission

Abstract: Quantum information science is poised to create the next technological revolution. A key topic in quantum information science is the task of quantum state transmission using classical communication and quantum correlation. While such a task has been well studied for the one receiver setting, with exemplary protocols including teleportation and remote state preparation, little is known for multiple receivers. The difficulty in the multiple-receiver setting lies in that the spatially separated receivers need to perform distributed measurements and operations. In this presentation, we introduce new types of communication resources and put forth protocols for multiple-receiver setting in different scenarios. We advocate the use of random matrix theory to design and analyze the proposed protocols. The presented results reveal the fundamental resource tradeoff in multiparty quantum communication.



Petros T. Boufounos
Mitsubishi Electric Research Laboratories

Petros T. Boufounos (SM) is Senior Principal Research Scientist and the Computational Sensing Team Leader at Mitsubishi Electric Research Laboratories (MERL), and a visiting scholar at the Rice University Electrical and Computer Engineering Department. Dr. Boufounos completed his undergraduate and graduate studies at MIT. He received the S.B. degree in Economics in 2000, the S.B. and M.Eng. degrees in Electrical Engineering and Computer Science (EECS) in 2002, and the Sc.D. degree in EECS in 2006. Between September 2006 and December 2008, he was a postdoctoral associate with the Digital Signal Processing Group at Rice University. Dr. Boufounos joined MERL in January 2009, where he has been heading the Computational Sensing Team since 2016.
Dr. Boufounos has served as Area Editor, IEEE Signal Processing Letters (2012-2014); Senior Area Editor, IEEE Signal Processing Letters (2014-2018); Member, SigPort Editorial Board (2015-2017); and is currently Member, IEEE Signal Processing Theory and Methods Technical Committee (2016-present). He received the SPS Best Paper Award (2015) and the Geoscience and Remote Sensing Society (GRSS) Symposium Paper Award (2014).
Dr. Boufounos’ immediate research focus includes signal acquisition and processing, inverse problems, frame theory, quantization and data representations, with applications in compression, sensing, array processing, and LIDAR, among others. He is also interested into how signal acquisition interacts with other fields that use sensing extensively, such as machine learning, robotics and dynamical system theory.

The Computational Sensing Revolution in Array Processing

Abstract: Recent advances in inverse problems, including sparse signal recovery and non-convex optimization have shifted the design paradigm for sensing systems. Computational methods have become an integral part of the design toolbox, enabling the use of algorithms to address some of the hardware challenges in designing such systems. One of the most promising applications of this paradigm shift has been in array imaging systems, such as ultrasonic, radar and optical (LIDAR). The impact is also timely, as array processing is becoming increasingly important in a variety of applications, including robotics, autonomous driving, medical imaging, and virtual reality, among others. This has led to continuous improvements in sensing hardware, but also to increasing demand for theory and methods to inform the system design and improve the processing.
This talk will present a general inverse problem framework for array processing systems, which allows us to describe both the acquisition hardware and the scene being acquired. Under this framework we can exploit prior knowledge on the scene, the system, and the nature of a variety of errors that might occur, allowing for significant improvements in the reconstruction accuracy. Furthermore, we can consider the design of the system itself in the context of the inverse problem, leading to designs that are more efficient, more accurate, or less expensive, depending on the application. We will explore applications of this model to LIDAR and depth sensing, radar and distributed radar, and ultrasonic sensing. In the context of these applications, we will describe how different models can lead to improved specifications in radar and ultrasonic systems, robustness to position and timing errors in distributed array systems, and cost reduction and new capabilities in LIDAR systems.



Leonardo Lizzi
University Côte d’Azur

Leonardo Lizzi is currently an Associate Professor at the University Côte d’Azur, France. He received the master’s degree in Telecommunication Engineering and the Ph.D. degree in Information and Communication Technology from the University of Trento, Italy, in 2007 and 2011, respectively. During his Ph.D. he has been visiting researcher at the Pennsylvania State University, USA, and the University of Nagasaki, Japan. From 2011 to 2014 he was Post-Doctoral researcher at the Laboratory of Electronics, Antennas and Telecommunications (LEAT) of the University Nice – Sophia Antipolis, France. At the moment, his research focuses on reconfigurable, miniature, multi-standards antennas for Internet-of-Things applications, wearable devices and 5G terminals. He is the coordinator of the European School of Antennas (ESoA) Ph.D. course on “Antennas and Rectennas for IoT Applications”. He is co-author of more than 110 papers in international journals and conference proceedings.

Antennas for future IoT devices: challenges and perspectives

Abstract: Last years have seen the spreading of the Internet-of-Things (IoT) idea, for which any kind of object should be equipped with wireless connectivity to communicate and share information over the internet. This paradigm will be disruptive by changing the way people interact with their environment, such as at home, at work, in transportation, etc. This implies that standard telecommunication aspects must be revised to match the IoT challenges. This led for example to the development of ultra-sensitive modulation schemes thanks to lower bandwidth requirements or the definition of new protocols capable of dealing with the tremendous number of connected objects and enabling compatibility between heterogeneous devices.
In this framework, this talk will focus on the antenna design problem, which must be also completely rethought. Differently from classical approaches, the design of IoT antennas can neglect the bandwidth requirements and focus on other aspects, such as strong miniaturization. On the other hand, depending on the application at hand, aspects as antenna efficiency or environmental sensitivity become particularly important and must be carefully considered. During the talk, several examples of antennas integrated into IoT devices and developed for different industrial and research projects will be presented and discussed.