Keynote Speakers

Georgios Giannakis

Georgios B. Giannakis (Fellow’97) received his Diploma in Electrical Engr. from the Ntl. Tech. Univ. of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. He was with the U. of Virginia from 1987 to 1998, and since 1999 he has been a professor with the U. of Minnesota, where he holds a Chair in Wireless Communications, a University of Minnesota McKnight Presidential Chair in ECE, and serves as director of the Digital Technology Center.  His general interests span the areas of communications, networking and statistical signal processing – subjects on which he has published more than 430 journal papers, 720 conference papers, 25 book chapters, two edited books and two research monographs (h-index 132). Current research focuses on data science and network science with applications to social, brain, and power networks with renewables. He is the (co-) inventor of 32 patents issued, and the (co-) recipient of 9 best journal paper awards from the IEEE Signal Processing (SP) and Communications Societies. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), and the inaugural IEEE Fourier Tech. Field Award (2015). He is a Fellow of EURASIP, and has served the IEEE in various posts including that of a Distinguished Lecturer.

Title of The Talk: Sparsity and Low Rank for Inference of Cognitive Communication Network States

Abstract.: Viewed through a statistical inference lens, many challenges facing communication network analytics boil down to (non-) parametric regression and classification, dimensionality reduction, or clustering. Adopting such a vantage point, this keynote presentation will put forth novel learning approaches for comprehensive situation awareness of cognitive radio (CR) communication networks that include spatio-temporal sensing via RF spectrum and channel gain cartography, flagging of network anomalies, prediction of network processes, and dynamic topology inference. Key emphasis will be placed on parsimonious models leveraging sparsity, low-rank or low-dimensional manifolds, attributes that are instrumental for complexity reduction.


Sarah Harris

Sarah Harris is an Associate Professor at the University of Nevada, Las Vegas. She completed her B.S. at B.Y.U. and her M.S. and Ph.D. at Stanford University. She has worked at Hewlett Packard, Nvidia, and various other places. She worked at Harvey Mudd College as an assistant and then associate professor from 2004-2014 and joined UNLV in 2014. She also spent a year as a visiting professor at the Technische University of Darmstadt in Germany. Her research interests include embedded systems, biomedical engineering, and robotics. Outside of work, she enjoys playing music and spending with her kids.

Title of the Talk: Control algorithms for smooth prosthetic gait

Abstract: An estimated 23.6 million Americans are affected by Type II diabetes and 37,000 undergo lower limb amputation each year [CDC]. About half of these patients are prescribed a foot-ankle prosthesis, yet mobility is often severely restricted by pain and impaired walking dynamics — in turn, leading to cardiovascular disease and other comorbidities. In order to affect better health outcomes for persons with amputation, we have addressed the link between residual limb pathology and mechanical dysfunction of the prosthesis with what we call a Dynamic Intelligence System. This system allows the dynamics of a human wearer of prosthetics to enter the control loop of that prosthetic limb for the first time. This Dynamic Intelligence System mitigates the extreme environment at the prosthesis-residual limb interface by reducing the harsh impacts and oscillatory loads that cause compressive, shear, and bending stresses at the residual limb.


C.-C. Jay Kuo

Dr. C.-C. Jay Kuo received his Ph.D. degree from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as Director of the Media Communications Laboratory and Distinguished Professor of Electrical Engineering and Computer Science. His research interests are in the areas of media processing, compression and understanding. Dr. Kuo was the Editor-in-Chief for the IEEE Trans. on Information Forensics and Security in 2012-2014. He was the Editor-in-Chief for the Journal of Visual Communication and Image Representation in 1997-2011, and served as Editor for 10 other international journals. Dr. Kuo received the 1992 National Science Foundation Young Investigator (NYI) Award, the 1993 National Science Foundation Presidential Faculty Fellow (PFF) Award, the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2011 Pan Wen-Yuan Outstanding Research Award, the 2014 USC Northrop Grumman Excellence in Teaching Award, the 2016 USC Associates Award for Excellence in Teaching, the 2016 IEEE
Computer Society Taylor L. Booth Education Award, the 2016 IEEE Circuits and Systems Society John Choma Education Award, the 2016 IS&T Raymond C. Bowman Award, and the 2017 IEEE Leon K. Kirchmayer Graduate Teaching Award. Dr. Kuo is a Fellow of AAAS, IEEE and SPIE. He has guided 145 students to their Ph.D. degrees and supervised 27 postdoctoral research fellows. Dr. Kuo is a co-author of 260 journal papers, 900 conference papers and 14 books.

Title of the Talk: Interpretable Convolutional Neural Networks via Feedforward Design

Abstract:The superior performance of Convolutional Neural Networks (CNNs) has been demonstrated in
many applications such as image classification, detection and processing. Yet, CNN’s working principle remains a mystery. We offer an interpretable design for simple CNNs through a feedforward construction without backpropagation. A CNN is simple if it is a cascade of two networks; namely, the Conv net and the FC net. The Conv net consists of convolutional layers while the FC net contains fully connected layers. To design the Conv net, we develop a new signal transform, called the Saab (Subspace approximation with adjusted bias transform. The bias term in filter weights is chosen to annihilate nonlinearity of the activation function, which simplifies our design significantly. For the FC net design, we propose a label-guided linear least squared regression (L3SR) method. To shed light on the behavior of the FC net, we measure the cross-entropy at nodes of FC layers. The properties and performance of the traditional backpropagation design and the proposed feedforward design are compared and analyzed.


Christopher Geiger

Christopher Geiger is the engineering director and chief engineer of the Lockheed Martin Enterprise Sustainment Solutions (ESS) market segment. His research interests include aerospace and defense sustainment information systems, automated test systems, and the
resilience and cyber security of ad hoc networks. He received a master's in business administration and bachelor’s degrees in electrical engineering and chemistry from the University of Florida. He is a licensed Professional Engineering (PE) in Florida and
Texas and a UK Chartered Engineer (CEng). Mr. Geiger is also on the board of directors of MidFlorida Credit Union (in the top 100 in the US), a local elected official, and chair of the Florida Professional Engineers in Industry. In addition, he is a senior member of the American Institute of Aeronautics and Astronautics (AIAA) and IEEE as well as a member of the Royal Aeronautical Society (RAeS). Mr. Geiger resides in Orlando, FL with his wife Lisa and three children where you can often find him canoeing and hiking in the wilds of Central Florida.

Title of the Talk: What do Aerospace and Defense Sustainment Information Systems have to do with the Latest Trends in Technology?

Abstract: There is an ebb and flow to the transfer of technology between defense and commercial industries. Many current hot topics such as machine learning, cloud computing, mobile devices, and autonomous vehicles receive significantly more commercial R&D funding than from defense. However, aerospace and defense sustainment information systems have had a head start over commercial Internet of Things (IoT) and the related topics above. This talk outlines surprising commonalities, shared lessons, and some potential futures from aerospace and defense sustainment as we all hurtle toward an ever more interconnected world.

 


Jacques Christophe Rudell

Jacques Christophe Rudell joined the Electrical Engineering Department as an assistant professor in January 2009. He has a BS in electrical engineering from the University of Michigan and MS and Ph.D. degrees in electrical engineering from the University of California, Berkeley. From 1989 to 1991, Rudell was an IC Designer and Project Manager with Delco Electronics (now Delphi), where he focused on bipolar analog circuits for automotive applications. From 2000 to 2001, he was a postdoctoral Researcher at the University of California, Berkeley, in addition to holding consulting positions in several Silicon Valley firms. In early 2002, he joined Berkana Wireless (now Qualcomm), San Jose, Calif., as an Analog/RF IC Design Engineer and later became the Design Manager of the Advanced IC Development Group. From 2005 to 2008, he worked in the Advanced Radio Technology Group at Intel, where his work focused mainly on RF transceiver circuits and systems, in advanced silicon processes.

Rudell received the National Science Foundation CAREER award for his work related to mmWave CMOS IC design. He has served on the IEEE International Solid-State Circuits Conference technical program committee (2003-2010), and on the RFIC steering committee (2002-2013) where he was the 2013 General Chair. He was also an Associate Editor for the IEEE Journal of Solid-State Circuits (2009-2015). Rudell is currently a member of the IEEE European Solid-State Circuit Conference’s technical program committee.


Russel Jacob (Jake) Baker

Russel Jacob (Jake) Baker received the B.S. and M.S. degrees in electrical engineering from the University of Nevada, Las Vegas, in 1986 and 1988. He received the Ph.D. degree in electrical engineering from the University of Nevada, Reno in 1993.

From 1981 to 1987 he served in the United States Marine Corps Reserves (Fox Company, 2nd Battalion, 23rd Marines, 4th Marine Division). From 1985 to 1993 he worked for E. G. & G. Energy Measurements and the Lawrence Livermore National Laboratory designing nuclear diagnostic instrumentation for underground nuclear weapons tests at the Nevada test site. During this time he designed, and oversaw the fabrication and manufacture of, over 30 electronic and electro-optic instruments including high-speed cable and fiber-optic receiver/transmitters, PLLs, frame- and bit-syncs, data converters, streak-camera sweep circuits, Pockels cell drivers, micro-channel plate gating circuits, and analog oscilloscope electronics. In 1991-1992 he was an adjunct faculty member in the department of electrical engineering at the University of Nevada, Las Vegas (UNLV). From 1993 to 2000 he served on the faculty in the department of electrical engineering at the University of Idaho (UI). In 2000 he joined a new electrical and computer engineering program at Boise State University (BSU) where he served as department chair from 2004 to 2007. At BSU he helped establish graduate programs in electrical and computer engineering including, in 2006, the university’s second PhD degree. In 2012 he re-joined the faculty at UNLV. During his tenure at the UI, BSU, and UNLV he has been the major professor to more than 85 graduate students. In addition to this industry and academic experience, he has done technical and expert witness consulting for over 100 companies and laboratories.

Over the last 30+ years his research and development interests have been, or currently are, focused on analog and digital integrated circuit design and fabrication, design of diagnostic electrical and electro-optic instrumentation for scientific research, integrated electrical/biological circuits and systems, array (memory, imagers, and displays) fabrication and design, CAD tool development and online tutorials, low-power interconnect and packaging techniques, design of wired/wireless communication and interface circuits, circuit design for the use and storage of renewable energy, power electronics, and the delivery of online engineering education.

Professor Baker is the named inventor on 149 US patents. He is a member of the honor societies Eta Kappa Nu and Tau Beta Pi, a licensed Professional Engineer, a popular lecturer that has delivered over 50 invited talks around the world, an IEEE Fellow, and the author of the books CMOS Circuit Design, Layout, and Simulation, CMOS Mixed-Signal Circuit Design, and a coauthor of DRAM Circuit Design: Fundamental and High-Speed Topics. He received the 2000 Best Paper Award from the IEEE Power Electronics Society, the 2007 Frederick Emmons Terman Award, and the 2011 IEEE Circuits and Systems Education Award.

He currently serves, or has served, on the IEEE Press Editorial Board (1999-2004), as editor for the Wiley-IEEE Press Book Series on Microelectronic Systems (2010-2018), as the Technical Program Chair of the 2015 IEEE 58th International Midwest Symposium on Circuits and Systems (MWSCAS 2015), as advisor for the student branch of the IEEE at UNLV (2013-present), on the IEEE Solid-State Circuits Society (SSCS) Administrative Committee (2011-2016), as a Distinguished Lecturer for the SSCS (2012-2015), and as the Technology Editor (2012-2014) and Editor-in-Chief (2015-2020) for the IEEE Solid-State Circuits Magazine.


Chiman Kwan

Chiman Kwan received his BS (honors) with major in electronics and minor in mathematics from the Chinese University of Hong Kong in 1988, and MS and Ph.D. degrees in electrical engineering from the University of Texas at Arlington in 1989 and 1993, respectively. He is the founder and Chief Technology Officer of Signal Processing, Inc. and Applied Research LLC, leading research and development effort in real-time control, chemical agent detection, biometrics, speech processing, image fusion, remote sensing, mission planning for UAVs, and fault diagnostics and prognostics.
From April 1991 to February 1994, he worked in the Beam Instrumentation Department of the SSC (Superconducting Super Collider National Laboratory) in Dallas, Texas, where he was heavily involved in the modeling, simulation and design of modern digital controllers and signal processing algorithms for the beam control and synchronization system. He later joined the Automation and Robotics Research Institute in Fort Worth, where he applied intelligent control methods such as neural networks and fuzzy logic to the control of power systems, robots, and motors. Between July 1995 and April 2006, he was the Vice President of Intelligent Automation, Inc. in Rockville, Maryland.
Over the past 27 years, Dr. Kwan has served as Principal Investigator/Program Manager for more than 115 competitively selected projects with total funding more than 36 million dollars from various government agencies and private companies such as Ford, Motorola, Boeing, Honeywell, and Stanford Telecom. He has 15 issued and pending patents, 50+ invention disclosures, 310+ journal and conference papers, and 450+ proprietary technical reports. He received numerous awards and recognitions from NASA, US Navy, US
Air Force, and IEEE.

Title of the Talk: Enhancing Safety of UAVs in National Airspace

Abstract: Unmanned Air Vehicles (UAV), also known as Unmanned Air Systems (UAS), are gaining more attention
in recent years. Some potential commercial applications may include cargo transfer between major cities, package and food delivery to individual households, etc. However, it is well-known that UAVs are much less reliable and have far more accidents than manned aircraft. This is probably one of the most important reasons that FAA is hesitant to open up the national airspace (NAS) and imposes tight restrictions to UAVs. Reliability of UAVs can be strengthened using durable engines and communication equipment, strong structural materials, advanced conditioned based maintenance and structural health monitoring procedures, accurate fault diagnostic algorithms, and robust and fault tolerant controllers. Despite the above measures, some equipment failures such as engine and communication equipment failures may still occur. In this talk, we present some recent research results done by our team to deal with major contingencies such as lost communications and engines failures. In particular, contingency planning software prototypes have been developed that can deal with those aforementioned major contingencies. Architectures of contingency planning software and some exemplar application scenarios will be discussed throughout the talk.


Erwin Bellers

Erwin Bellers received his M.Sc degree in Electrical Engineering with distinction from University of Twente in the Netherlands in 1993 and his PhD degree in Electrical Engineering from Delft University of Technology in 2000. Erwin joined Philips Research in Eindhoven, the Netherlands, in 1993 as a
Researcher and mainly conducted research in video enhancement, de-interlacing and frame-rate conversion. In 2000 he joined Philips Research USA in New York as a senior Scientist where he led a research-team focusing on spatial resolution enhancement.
In 2002 Erwin moved to Silicon Valley to join Philips Semiconductors as a Senior Video Architect, and continuing into NXP Semiconductors in 2006. Erwin further innovated on the algorithm and architecture of frame-rate conversion which became a successful product range of NXP Semiconductors.
With the merger of NXPs TV business and Trident, Erwin became responsible for video algorithm innovations within Trident. In 2010 the TV business of Trident was acquired by Sigma Designs and Erwin continued in several roles including director, senior principle and Fellow. In June 2018 the TV business of Sigma Designs was acquired by the startup V-Silicon. Currently, Erwin is responsible for the video innovations and leading the Video Innovation Team in his role as Fellow Engineer.
Erwin was invited as a co-author for the Proceedings of the IEEE, and he won the second price in the ICCE Outstanding Paper Awards program in 1997 and 2006, and the first price in 2009. Erwin has published over 50 papers, 1 book, 1 book chapter, and holds over 50 patents and patent applications.

Title of the Talk: Challenging Algorithm and Architecture design in the realm of Consumer Electronics.

Abstract: The Consumer Electronics (CE) market is very challenging. Bounded by tremendous price erosions, short
time-to-market cycles, high level of Silicon integration, high customer expectations, constantly changing requirements, expected level of adaptation over its lifetime, level of innovation, etc., creates an interesting landscape which stimulates creativity and innovation at all levels.
In television sets the video resolution has seen an increase from 1K to 2K to 4K and now pushing towards 8K pixels, from Standard Definition Range to High Dynamic Range (HDR), from a relative small to a large color space, from single to quad CPU cores, from simple graphics to more complex graphics. In the presentation we will address various challenges from a television System-on-Silicon (SoC) providers point of view, discuss some key video technologies from algorithmic and architectural perspective, and some opportunities and challenges ahead.


Yuhan Cai

Yuhan Cai is an Engineering Manager at LinkedIn, leading a team of scientists/engineers to build machine learning infrastructure products, facilitating the robust, efficient and straightforward application of machine learned capabilities. Before LinkedIn, he has worked at bigger companies like Apple, Microsoft and Amazon, as well as smaller startups, focusing on scalable big data platforms and AI advancement. He received a Bachelor and Master of Computer Science from the University of British Columbia in Canada, and he was the winner of ACM SIGMOD Best Paper and Best Demo Awards.

 

Title of the Talk: ReMix: Simplifying Workflows in Machine Learning Infrastructure

Abstract: Our mission at Machine Inference Infrastructure is to build easy, scalable and operable machine learning to connect the world’s professionals to the resources they need, and our vision is to leverage Artificial Intelligence at massive scale to bring real economic opportunity to the world. In this talk, I’m going to present one of our machine learning infrastructure products, ReMix, which is a generic workflow orchestration solution for building mid-tier services. ReMix was originally conceived as the next generation federation solution unifying LinkedIn federation infrastructure. The lessons learned from the federation use cases applied generally to many Relevance verticals, thus ReMix has been built as a more generalized solution for building Relevance mid-tier services. It provides a declarative Java API for composing workflows from operators and from other workflows. It includes convenient support for making asynchronous calls to external REST resources, batching/caching data fetches needed by multiple operators in a workflow, and other performance and robustness enhancing workflow optimizations. It has reached General Availability in June 2018 and been running in production with Ranker Relevance (recommendation) Models/Endpoints and search blending since 2018.


Joel Jones

Joel Jones is the Principal Software Engineer at Cavium,USA since 5 years.Previously he was associated with Apple as VM Backend Compiler Engineer.He has completed his PhD from University of Illinois at Urbana-Champaign.


Important Deadlines

Full Paper Submission:30th November 2018
Acceptance Notification: 10th December 2018
Final Paper Submission:20th December 2018
Early Bird Registration: 20th December 2018
Presentation Submission: 31st December 2018
Conference: 7th-9th January 2019

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Announcements

• Conference Proceedings will be submitted for publication at IEEE Xplore Digital Library
• Extended Version of accepted and presented paper will be submitted for publication at special issue in Sensor Journal
• Best Paper Award will be given for each tracks
• There will be two workshops on-
i. Data Analysis and ii. IoT on Jan 9, 2019
• There will be Corporate Exhibitions and Product Display on 7th and 8th January of 2019.
• Conference Record No-