IMPORTANT NOTE: In view of the COVID-19 uncertainty, and in particular the rising number of Omicron variant infections worldwide, FICC 2022 has been moved to a fully virtual conference.

Keynote Speakers

Bill Inmon

Bill Inmon

Forest Rim Technology, United States

Bill Inmon – the “father of data warehouse” – has written 62 books published in nine languages. He received his Bachelor of Science degree in mathematics from Yale University in 1967, and his Master of Science degree in computer science from New Mexico State University. Bill owns and operates Forest Rim Technology, a company that applies and implements data warehousing solutions executed through textual disambiguation and TextualETL. Bill’s latest adventure is the building of technology known as textual disambiguation (textual ETL) – technology that reads raw text in a narrative format and allows the text to be placed in a conventional data base so that it can be analyzed by standard analytical technology, thereby creating unique business value for Big Data/unstructured data. Bill was named by ComputerWorld as one of the ten most influential people in the history of the computer profession. Bill lives in Denver, Colorado. For more information about textual disambiguation (textual ETL) refer to www.forestrimtech.com. Three of Bill’s latest books are DATA ARCHITECTURE: SECOND EDITION, Elsevier press, HEARING THE VOICE OF THE CUSTOMER, Technics Publications, and TURNING TEXT INTO GOLD, Technics Publications. Bill’s most recent book is BUILDING THE DATALAKEHOUSE, Technics Publications.

Keynote Title: MEDICAL RESEARCH - TAMING THE DRAGON

Abstract: Medical records are made for the most part in the form of text. And this presents an obstacle to the organization needing to do medical research. But now it is easy, simple and inexpensive to take the text found in those medical records and turn them into a data base. Once the data base is created, it is then easy to do analysis against them. Come hear more.



Emil Björnson

Emil Björnson

KTH Royal Institute of Technology, Sweden

Emil Björnson is a Professor of Wireless Communication at the KTH Royal Institute of Technology, Stockholm, Sweden. He is an IEEE Fellow, Digital Futures Fellow, and Wallenberg Academy Fellow. He has a podcast and YouTube channel called Wireless Future. His research focuses on multi-antenna communications and radio resource management, using methods from communication theory, signal processing, and machine learning. He has authored three textbooks and has published a large amount of simulation code. He has received the 2018 IEEE Marconi Prize Paper Award in Wireless Communications, the 2019 EURASIP Early Career Award, the 2019 IEEE Communications Society Fred W. Ellersick Prize, the 2019 IEEE Signal Processing Magazine Best Column Award, the 2020 Pierre-Simon Laplace Early Career Technical Achievement Award, the 2020 CTTC Early Achievement Award, and the 2021 IEEE ComSoc RCC Early Achievement Award. He also received six Best Paper Awards at the conferences.

Keynote Title: How much more bandwidth do we need in wireless?

Abstract: The world is becoming increasingly digitalized and connected, and mobile broadband connectivity is the backbone of this development. The demand for capacity and expectations on service quality are constantly increasing, which calls for new wireless technology solutions. Much of the capacity improvements in past decades have been achieved by increasing the spectral bandwidth. 4G utilizes wireless channels with tens of MHz of sub-6 GHz spectrum, while 5G is envisioned to exploit hundreds of MHz in the mmWave bands. If we extrapolate this development into the next decade, it seems logical that 6G will require several GHz of bandwidth and operate in the sub-THz bands. But how practically viable is such a development and will it answer to the actual needs of society? Do we truly need more bandwidth or are there alternative solutions? These are the questions that will be discussed and answered in this keynote.



Yonina Eldar

Yonina Eldar

Weizmann institute of Science, Israel

Yonina Eldar is a Professor in the Department of Mathematics and Computer Science, Weizmann Institute of Science, Rehovot, Israel where she heads the center for Biomedical Engineering and Signal Processing and holds the Dorothy and Patrick Gorman Professorial Chair. She is also a Visiting Professor at MIT, a Visiting Scientist at the Broad Institute, and an Adjunct Professor at Duke University and was a Visiting Professor at Stanford. She is a member of the Israel Academy of Sciences and Humanities, an IEEE Fellow and a EURASIP Fellow. She received the B.Sc. degree in physics and the B.Sc. degree in electrical engineering from Tel-Aviv University, and the Ph.D. degree in electrical engineering and computer science from MIT, in 2002. She has received many awards for excellence in research and teaching, including the IEEE Signal Processing Society Technical Achievement Award (2013), the IEEE/AESS Fred Nathanson Memorial Radar Award (2014) and the IEEE Kiyo Tomiyasu Award (2016). She was a Horev Fellow of the Leaders in Science and Technology program at the Technion and an Alon Fellow. She received the Michael Bruno Memorial Award from the Rothschild Foundation, the Weizmann Prize for Exact Sciences, the Wolf Foundation Krill Prize for Excellence in Scientific Research, the Henry Taub Prize for Excellence in Research (twice), the Hershel Rich Innovation Award (three times), and the Award for Women with Distinguished Contributions. She received several best paper awards and best demo awards together with her research students and colleagues, was selected as one of the 50 most influential women in Israel, and was a member of the Israel Committee for Higher Education. She is the Editor in Chief of Foundations and Trends in Signal Processing, a member of several IEEE Technical Committees and Award Committees, and heads the Committee for Promoting Gender Fairness in Higher Education Institutions in Israel.

Keynote Title: PACE Tech: Physics and Algorithms Coupled to Enhance Technology

Abstract: We live in a digital world where data processing and storage is performed using computers. The digital revolution affects all aspects of our life from communications through radar, and medical imaging systems. This revolution is based on sensing the physical signals around us, and representing the acquired signals by digital bits that can be processed by a computer. However, clearly information is lost in this process: digitization rates are limited by the mathematical bounds of Nyquist and Shannon, and acquisition devices are limited by physical bounds such as the diffraction limit. Therefore, we cannot obtain infinite precision in time, space and frequency. In this talk we consider how the interplay between science, physics and algorithms can pave the way to enhanced technology that is not limited by the bounds above. We suggest treating sensing and processing in an end-to-end fashion, and exploiting physical principles to allow interesting new ways to transmit data in a coded fashion, which cannot be viewed, processed or heard directly. Instead the information is embedded indirectly in ways that are more efficient on the one hand, but can be properly decoded by appropriate mathematical algorithms on the other. This coupling of physics and algorithms paves the way to devices that can transmit, sample and process at rates and resolutions exceeding known bounds. We will illustrate applications of these ideas to a variety of problems in ultrasound and optical imaging, radar systems, communication channels and more, and show several demos of real-time prototypes overcoming standard bounds including a wireless ultrasound probe, super resolution ultrasound, and sub-Nyquist automotive radar.



Kwang-Cheng Chen

Kwang-Cheng Chen

University of South Florida, United States

Kwang-Cheng Chen has been a Professor at the Department of Electrical Engineering, University of South Florida, since 2016. From 1987 to 2016, Dr. Chen worked with SSE, Communications Satellite Corp., IBM Thomas J. Watson Research Center, National Tsing Hua University, HP Labs., and National Taiwan University in mobile communications and networks. He visited TU Delft (1998), Aalborg University (2008), Sungkyunkwan University (2013), and Massachusetts Institute of Technology (2012-2013, 2015-2016). He founded a wireless IC design company in 2001, which was acquired by MediaTek Inc. in 2004. He has been actively involving in the organization of various IEEE conferences and serving editorships with several IEEE journals, together with various IEEE volunteer services to the IEEE, Communications Society, Vehicular Technology Society, and Signal Processing Society, such as founding the Technical Committee on Social Networks in the IEEE Communications Society. He also serves in the editorial board of Nature Scientific Reports. Dr. Chen also has contributed essential technology to various international standards, namely IEEE 802 wireless LANs, Bluetooth, LTE and LTE-A, 5G-NR, and ITU-T FG ML5G. He has authored and co-authored over 300 IEEE papers (including 7 Highly Cited Papers in past 10 years), 4 books published by Wiley and River (most recently, Artificial Intelligence in Wireless Robotics, 2020), and 26 granted US patents. Dr. Chen is an IEEE Fellow and has received several prestigious awards including 2011 IEEE COMSOC WTC Recognition Award, 2014 IEEE Jack Neubauer Memorial Award, 2014 IEEE COMSOC AP Outstanding Paper Award. Dr. Chen’s current research interests include wireless networks, quantum computations and communications, artificial intelligence and multi-agent systems, blockchain, and post-quantum cryptography.

Keynote Title: Wireless Networked Artificial Intelligence in a Smart Factory

Abstract: Smart factory emerges as one of the most influential technologies for Internet of Things, which holistically integrates wireless networking, AI computing, and automatic control to accomplish flexible production. A novel view considers a smart factory composed of two kinds of multi-robot systems, production robots and transportation robots. Focusing on production robots, real-time algorithm of multi-robot task assignment is first proposed to enable flexible re-configuration of a smart factory according to dynamic demands. To facilitate resilient operation of production multi-robot system that can be treated as a cyber-physical system, social learning is uniquely employed to maintain the high yield of precision production against point failures including errors due to wireless communications. This series of technology innovations present computationally efficient facilitation of a smart factory.