Speakers

SPW 2015 will feature several plenary presentations from renown experts in their fields:

 

Yonina Eldar

Xampling and Sparse Signal Processing

Dr. Yonina Eldar,

Professor of Electrical Engineering, Technion

The Edwards Chair in Engineering

Yonina C. Eldar received the B.Sc. degree in Physics in 1995 and the B.Sc. degree in Electrical Engineering in 1996 both from Tel-Aviv University (TAU), Tel-Aviv, Israel, and the Ph.D. degree in Electrical Engineering and Computer Science in 2002 from the Massachusetts Institute of Technology (MIT), Cambridge. She is currently a Professor in the Department of Electrical Engineering at the Technion – Israel Institute of Technology, Haifa, Israel, where she holds the Edwards Chair in Engineering. Dr. Eldar has received numerous 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 received several best paper awards and best demo awards together with her research students and colleagues including the SIAM outstanding Paper Prize and the IET Circuits, Devices and Systems Premium Award, and was selected as one of the 50 most influential women in Israel. She is a member of the Young Israel Academy of Science and Humanities and the Israel Committee for Higher Education, and an IEEE Fellow. She is author of the book “Sampling Theory: Beyond Bandlimited Systems” and co-author of the books “Compressed Sensing” and “Convex Optimization Methods in Signal Processing and Communications”, all published by Cambridge University Press.

 

Yonina Eldar

A Presentation on Machine Learning

Alfred Hero, the R. Jamison and Betty Williams Professor of Engineering, University of Michigan EECS

Alfred O. Hero III received the B.S. (summa cum laude) from Boston University (1980) and the Ph.D from Princeton University (1984), both in Electrical Engineering. Since 1984 he has been with the University of Michigan, Ann Arbor, where he is the R. Jamison and Betty Williams Professor of Engineering. His primary appointment is in the Department of Electrical Engineering and Computer Science and he also has appointments, by courtesy, in the Department of Biomedical Engineering and the Department of Statistics. He has held other visiting positions at a variety of notable institutions. Alfred Hero is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). He received the University of Michigan Distinguished Faculty Achievement Award (2011). He has been plenary and keynote speaker at several workshops and conferences. He has received several best paper awards including: an IEEE Signal Processing Society Best Paper Award (1998), a Best Original Paper Award from the Journal of Flow Cytometry (2008), a Best Magazine Paper Award from the IEEE Signal Processing Society (2010), a SPIE Best Student Paper Award (2011), an IEEE ICASSP Best Student Paper Award (2011), an AISTATS Notable Paper Award (2013), and an IEEE ICIP Best Paper Award (2013). He received an IEEE Signal Processing Society Meritorious Service Award (1998), an IEEE Third Millenium Medal (2000), an IEEE Signal Processing Society Distinguished Lecturership (2002), and an IEEE Signal Processing Society Technical Achievement Award (2014). He was President of the IEEE Signal Processing Society (2006-2007). Alfred Hero is currently a member of the Big Data Special Interest Group (SIG) of the IEEE Signal Processing Society. Since 2011 he has been a member of the Committee on Applied and Theoretical Statistics (CATS) of the US National Academies of Science. His research has been focussing on the following research themes in modeling, computation, and inference for large scale time varying data arising from networks and graphs. These themes are being developed for data whose provenance includes: materials science, biomolecular assays, health surveillance, communications/surveillance networks, and dynamic social media.

 

 

Andrew Lo

A Presentation on Finance and Signal Processing

Andrew Lo, MIT Charles E. and Susan T. Harris Professor, Professor of Finance

Slides (pdf)

Andrew W. Lo is the Charles E. and Susan T. Harris Professor, a Professor of Finance, and the Director of the Laboratory for Financial Engineering at the MIT Sloan School of Management. His research interests include the empirical validation and implementation of financial asset pricing models; the pricing of options and other derivative securities; financial engineering and risk management; trading technology and market microstructure; statistics, econometrics, and stochastic processes; computer algorithms and numerical methods; financial visualization; nonlinear models of stock and bond returns; hedge-fund risk and return dynamics and risk transparency; and, most recently, evolutionary and neurobiological models of individual risk preferences and financial markets. His awards include the Alfred P. Sloan Foundation Fellowship, the Paul A. Samuelson Award, the American Association for Individual Investors Award, the Graham and Dodd Award, the 2001 IAFE-SunGard Financial Engineer of the Year Award, a Guggenheim Fellowship, the CFA Institute’s James R. Vertin Award, and awards for teaching excellence from both the Wharton School of the University of Pennsylvania and MIT Sloan. Lo holds a BA in economics from Yale University as well as an AM and a PhD in economics from Harvard University.

 

 

Ryan Thomas

Speech Research at Amazon

Ryan Thomas, Principal Research Scientist, Amazon Speech Research

Ryan Thomas studied mathematics and electrical engineering at Utah State University. Since then he has made significant contributions to speech processing. He worked as a language modeling research scientist and HPC engineering at Nuance Communications, a senior engineer at Coraid. He was a research scientist at Yap where he worked on automated voice mail transcription. He is presently a principal research scientist at Amazon Research (AtZ Development center), where he contributed to the development of the Amazon Echo.

 

Chris Dick

Using FPGAs for Signal Processing

Chris Dick, Distinguished Engineer and DSP Chief Architect, Xilinx

Chris Dick is the Chief DSP Architect at Xilinx and heads the Communications Signal Processing Group working in the area of advanced wireless systems for 3G LTE, MIMO-OFDM and high-speed wired FEC technologies. His research interests are in the areas of software defined radio (SDR), forward error correction, turbo equalizers, modem design, VLSI architectures for signal processing, FPGA-based signal processing, wireless systems, multicarrier modulation, MIMO, hardware architectures for real-time signal processing, fast algorithms, multirate filters, narrowband systems, DSP design flows and programming models for reconfigurable hardware. In addition to his experience in the wireless domain Chris has performed signal processing work in the area of speech and imaging radar processing. Chris has been an advisor to the National Science Foundation in the area of Application-Specific Hardware/Software, and FPGA-based and Reconfigurable Systems and has advised the US Defense Sciences Research Agency – an advisory arm of DARPA. He holds the position of adjunct professor at both Rice University and Santa Clara University where he teaches courses on signal processing and hardware implementation of real-time signal processing systems. Chris has in excess of 100 publications in refereed conferences and journals and has been an invited speaker at many industry events. He holds 20 patents (issued and pending).

 

V. John Mathews

Signal Processing for Neural Prostheses

V. John Mathews, Professor of Electrical and Computer Engineering at the University of Utah

V. John Mathews is a Professor of Electrical and Computer Engineering at the University of Utah . He received his Ph.D. and M.S. degrees in Electrical and Computer Engineering from the University of Iowa, Iowa City, Iowa in 1984 and 1981, respectively, and the B. E. (Hons.) degree in Electronics and Communication Engineering from the University of Madras, India in 1980. He joined the department of Electrical Engineering at the University of Utah in 1985, where he is engaged in teaching signal processing classes and conducting research in signal processing algorithms. He served as the Chairman of the department from 1999 to 2003. His current research interests are in nonlinear and adaptive signal processing and application of signal processing techniques in audio and communication systems, biomedical engineering, and structural health management. He has also contributed in the areas of perceptually-tuned image compression and spectrum estimation techniques. He is the author of the book Polynomial Signal Processing, published by Wiley, and co-authored with Professor G. L. Sicuranza, University of Trieste, Italy. He has published approximately 150 technical papers, and is the inventor on seven patents.

Dr. Mathews is a Fellow of IEEE and has served as a member of the Signal Processing Theory and Methods Technical Committee, the Education Committee and the Conference Board of the IEEE Signal Processing Society . He was the Vice President (Finance) of the IEEE Signal Processing Society during 2003-2005, and the Vice President (Conferences) of the Society during 2009-2011. He is a past associate editor of the IEEE Transactions on Signal Processing and the IEEE Signal Processing Letters and has served on the editorial boards of the IEEE Journal of Selected Topics in Signal Processing and the IEEE Signal Processing Magazine. He has served on the organization committees of several international technical conferences including as General Chairman of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2001. He was a Distinguished Lecturer of the IEEE Signal Processing Society for 2013 and 2014, and is the recipient of the 2014 IEEE Signal Processing Society Meritorious Service Award.

 

C. Sidney Burrus

Open Educational Resources, MOOCs, and Machine Learning: Using Technology to Enhance Education

C. Sidney Burrus, Senior Strategist for Connexions, the Maxfield and Oshman Professor Emeritus of Electrical and Computer Engineering, and Dean Emeritus of the George R. Brown School of Engineering, Rice University

C. Sidney Burrus received the PhD degree from Stanford University in 1965 after which he joined the faculty at Rice University where he is now Research Professor in ECE, Senior Strategist for Connexions, the Maxfield and Oshman Professor Emeritus of Electrical and Computer Engineering, and Dean Emeritus of the George R. Brown School of Engineering. From 1984 to 1992 he was chairman of the ECE Department at Rice. From 1998 to 2005 he was dean of Engineering. He has been part of the Connexions Project since 1999. In 1975-76 and again in 1979-80 he was a Guest Professor at the University of Erlangen in Germany, and during the academic year 1989-90 he was a Visiting Professor in the Electrical Engineering and Computer Science Department at MIT. Dr. Burrus was elected Fellow of the IEEE in 1981, the IEEE S-ASSP Technical Achievement Award in 1986, and was a Distinguished Lecturer for the Signal Processing Society and for the Circuits and Systems Society from 1989 through 1992. He was awarded the IEEE S-SP Society Award in 1994, the Millenium Medal in 2000, and the SPIE Wavelet Pioneer Award in 2006. He served on the IEEE Signal Processing Society ADCOM and has coauthored five books and over 200 papers on digital signal processing.

 

Bob Stewart

Teaching Signal Processing and Communications with MATLAB, Simulink, and RTL-SDR Radio

Bob Stewart, MathWorks Professor of Signal Processing, University of Strathclyde, Scotland, UK

Abstract: Low-cost hardware provides many opportunities for hands-on learning where students can reinforce theory with practical results and experimentation. For students of signal processing and communications, for less than $20 students they now can acquire the RTL-SDR USB receiver. (For the last few years, FPGA enabled software defined radio (SDR) hardware has been available of course, but it has always been too expensive for students to acquire.) The RTL-SDR device can now provide a hardware platform that will allow I/Q sampling in channels of a few MHz bandwidth over the radio frequency (RF) range from 50MHz to 1.7GHz and digitize directly onto a computing platform (Windows, Linux or Mac). And by connecting the RTL-SDR to a computer running MATLAB and Simulink alongside appropriate drivers and toolboxes we can give students an instant laboratory for exploring RF signal properties, mastering the fundamentals of DSP enabled radio design, and building real-time software-defined radio receivers directly on their own computers. Therefore direct from their desktop, students can perform off-the-air RF signal viewing in the time and frequency domains, design full DSP enabled SDR receivers for FM and AM signals, view mobile, ISM, satellite and frequency bands. As their mastery of DSP and digital communications progresses, then later they can implement complete first principle designs of QPSK type receivers with PHY layers, synchronization, tuning, control layers and so on; the full spectrum of digital communications!

We have demonstrated the effectiveness of this hands-on approach in undergraduate laboratories and some recent tutorial workshops at Strathclyde. More recently we have integrated our teaching materials into a free 650 page downloadable book with 120 structured examples. This contains all of the material needed to learn the rudiments of MATLAB, Simulink, and DSP enabled radio design with the RTL-SDR. In this talk we will present findings from this experience, outline some of the examples and pedagogy of our DSP enabled SDR courses and our insights for future curriculum development.

Robert Stewart is the MathWorks Professor of Signal Processing at the University of Strathclyde. Since August 2014 he has been Chair and Head of the Department of Electronic and Electrical Engineering, with more than 55 academic staff and almost 300 researchers. His interests in teaching, knowledge exchange and research over the last 20 years has focused around signal processing and communications. In recent years he has been working on digital communications and software defined radio, with specific interest in radio standards and most recently on wireless white space radio utilising TV spectrum frequencies. From 2006-2012 he was the Xilinx Professor of DSP and Digital Logic at Strathclyde, and from since 1997 he has been a visiting Professor at the University of California, Los Angeles (UCLA) Extension School. In 2004 he founded of the technology company Steepest Ascent Ltd, based in Glasgow with an office in Los Angeles. The company was acquired by MathWorks in 2013.