Wireless Industry Trends

Robert Padovani, Qualcomm Inc

In this talk we discuss the state of the wireless industry, its projected growth, interesting new applications and some of the challenges for the future.

Dr. Roberto Padovani is executive vice president and Fellow at Qualcomm Incorporated. He served as the company's chief technology officer from 2002 to 2011. Dr. Padovani joined Qualcomm in 1986, after two years at M/A-COM Linkabit. Over the past 25 years, Dr. Padovani has been involved in the research and development of digital communication systems with particular emphasis on Code Division Multiple Access (CDMA) wireless technology systems. He was involved in the initial design, development, and standardization of the IS-95 CDMA system, CDMA200, and 1xEV-DO. His research and inventions in this field have led to the worldwide standardization and commercialization of CDMA technology for second- and third-generation cellular systems.

Dr. Padovani holds more than 80 patents on wireless systems. He has published numerous technical papers in the digital communications field and was the co-recipient of the 1991 IEEE Vehicular Technology Society Best Paper Award for a fundamental paper on the capacity of CDMA cellular systems. In 2009 he received the IEEE Eric. E. Sumner Award .Äfor pioneering innovations in wireless communications, particularly to the evolution of CDMA for wireless broadband data..Ä In addition, Dr. Padovani has received the Innovators in Telecommunications, 2004 award from the San Diego Telecom Council, and the Executive of the Year, 2006 from the School of Engineering at UC San Diego. He was elected to the National Academy of Engineering in 2006. Dr. Padovani received a laureate degree from the University of Padova, Italy and master of science and Ph.D. degrees from the University of Massachusetts, Amherst, all in electrical and computer engineering. He is an IEEE Fellow and an Adjunct Professor in the Electrical and Computer Engineering Department at the University of California, San Diego.

Information Theory in an Industrial Research Lab (Watch )

Marcelo Weinberger, HP Labs

While mathematical in nature, information theory addresses basic engineering problems such as data compression, coding for error correction, and constraint coding. It is therefore not surprising that an industrial research lab would maintain core expertise in technologies that were crucial in enabling the multimedia revolution. But if the target is the practical applications - the algorithms, the industry standards - why research the mathematical foundations - the mathematical models, the fundamental bounds? In this talk, I will try to address this question by presenting "a few years" in the life of the Information Theory Research group at HP Labs. From theorems in universal data compression and universal denoising to industrial standards on image compression and intellectual property in image denoising, from capacity calculations in constraint coding to DVD standards and next-generation memory technologies, we will discuss the many faces of information theory.

Marcelo J. Weinberger received the Electrical Engineer degree from the Universidad de la Repúa, Montevideo, Uruguay, in 1983, and the M.Sc. and D.Sc. degrees from Technion - Israel Institute of Technology, Haifa, Israel, in 1987 and 1991, respectively, both in electrical engineering.

From 1985 to 1992 he was with the Department of Electrical Engineering at Technion, joining the faculty for the 1991-1992 academic year. During 1992-1993 he was a Visiting Scientist at IBM Almaden Research Center, San Jose, California. Since 1993 he has been with Hewlett-Packard Laboratories, Palo Alto, California, where he is a Distinguished Scientist and manages the Information Theory Research group. The group has transferred data compression and error-correction technology to HP's imaging, storage, and computing businesses.

Dr. Weinberger's research has focused on universal statistical modeling and its applications to problems in information theory and computation, particularly data compression. He is a co-author of the algorithm at the core of the JPEG-LS lossless image compression international standard, and was an editor of the standard specification. He also contributed to the coding algorithm of the JPEG2000 image compression standard. He is a co-recipient of the 2006 IEEE Communications/Information Theory Societies Joint Paper Award for the paper "Universal Discrete Denoising: Known Channel,'' published in the IEEE Transactions on Information Theory in January 2005, which presents the DUDE denoising algorithm.

Dr. Weinberger is a Fellow of the IEEE. He served as an Associate Editor for Source Coding of the IEEE Transactions on Information Theory from 1999 to 2002, and has been in the Technical Program Committee (TPC) of multiple conferences, co-chairing the TPC of the 2006 IEEE Information Theory Workshop.

Optimizing Kinect: Audio and Acoustics

Ivan Tashev, Microsoft Research

Engineering is always a tradeoff between conflicting constraints. The key to finding the best design is often mathematical optimization. Properly defined requirements and optimization criteria convert this mathematical methodology to a solver of real-life challenges. The talk will cover the approaches for optimizing the acoustical design and algorithms of Kinect, the best selling electronic device in history as recorded in the Guinness Book of Records. Kinect is the first industrial product with surround sound echo cancellation, hands-free sound capture and speech recognition for distances up to 3.5 meters, and the first open microphone device that doesn't need push-to-talk button to start the speech recognition. It wouldn't be successful without pushing the acoustical design and algorithms to their limits using optimization methods.

Ivan Tashev got his Diploma Engineer in Electronics and PhD in Computer Science from the Technical University of Sofia, Bulgaria, in 1984 and 1990 respectively. He was Assistant Professor in the same university and joined Microsoft in 1998. Currently he is a Principal Architect in Speech Technology Group in Microsoft Research. Dr. Tashev contributed with algorithms and designs to microphone array support in Windows, RoundTable device, the audio pipeline in Microsoft Auto platform, and the audio pipeline in Kinect. He is inventor or co-inventor of 40 US patent submissions, from which 18 are granted. Ivan Tashev is a senior member of IEEE and member of its Audio and Acoustic Signal Processing Technical Committee. He is also member of the Audio Engineering Society and its Pacific Northwest Committee, and the Acoustical Society of America. Dr. Tashev is reviewer for most of the scientific journals in his research area, member of the organizing or technical committees of ICASSP, IWAENC, WASPAA, HSCMA and other scientific conferences in his area. He authored or coauthored four books and more than 70 scientific papers. His latest book, Sound Capture and Processing, was published in 2009 by John Wiley & Sons Ltd.
How does applied math become applicable? (Watch )

Emina Soljanin, Bell Laboratories

It has been widely acknowledged that research in mathematical sciences has been crucial in making communications devices, systems, and networks possible. But which research exactly? The one considered the most applicable at the time it was being conducted, the one cast in the best power point presentation, the one proving the most fundamental theorems, the one with the most dedicated owners, the one with the most charismatic proponents, or none, or all of the above? In this talk, I will tell you what, after almost two decades of working at Bell Labs, I think was hard, what was easy, and why I believe the toughest uphill battles for applying math, information and coding theory are still in front of us.

Emina Soljanin received the M.S. and Ph.D. degrees from Texas A&M University, College Station, in 1989 and 1994, and the European Diploma degree from the University of Sarajevo, Bosnia, in 1986, all in Electrical Engineering. From 1986 to 1988, she worked in the Energoinvest Company, Bosnia, developing optimization algorithms and software for power systems' control. After graduating from Texas A&M in 1994, she joined Bell Laboratories, Murray Hill, NJ, where she is now a Distinguished Member of Technical Staff in the Mathematics of Networks and Communications Research Department. She spent a year as a visiting researcher at Ecole Polytechnique Federale de Lausanne, in Switzerland in 2008.

Dr. Soljanin's research interests are in the broad area of communications, information and coding theory, and their applications. In the course of her eighteen-year employment with Bell Labs, she has participated in a very wide range of research and business projects. These projects include designing the first distance enhancing codes to be implemented in commercial magnetic storage devices, the first forward error correction for Lucent's optical transmission devices, color space quantization and color image processing, quantum computation, link error prediction methods for the third generation hybrid ARQ wireless network standards. Her most recent activities are in the area of network and rateless coding. She is a co-author of numerous papers, patents, book chapters, and two monographs on network coding.

Dr. Soljanin served as the Associate Editor for Coding Techniques, 1997-2000, for the IEEE Transactions on Information Theory, and on the Information Theory Society Board of Governors, 2009-2011. She served in various roles on other journal editorial boards and conference program committees, in particular as a TPC co-chair of the 2008 IEEE Information Theory Workshop and 2012 International Symposium on Network Coding She is a co-organizer of the DIMACS 2001-2005 Special Focus on Computational Information Theory and Coding and 2011-2015 Special Focus on Cybersecurity. More

Content recommendation on Yahoo! sites

Deepak Agarwal, Principal Research Scientist, Yahoo! Research

Algorithmically matching articles to users in a given context is essential for the success and profitability of large scale content recommendation systems. The objective is to maximize some utility (e.g. total revenue, total engagement) of interest over a long time horizon. This is a bandit problem since there is positive utility in displaying items that may have low mean but high variance. A key challenge in such bandit problems is the curse of dimensionality. Bandit problems are also difficult to work with for responses that are observed with considerable delay (e.g. return visits, confirmation of a buy). One approach is to optimize multiple competing objectives in the short-term to achieve the best long-term performance. For instance, in serving content to users on a website, one may want to optimize some combination of clicks and downstream advertising revenue in the short-term to maximize revenue in the long-run. In this talk, I will discuss some of the technical challenges by focusing on a concrete application - content optimization on the Yahoo! front page.

Deepak Agarwal is a statistician at Yahoo! who is interested in developing statistical and machine learning methods to enhance the performance of large scale recommender systems. Deepak and his collaborators significantly improved article recommendation on several Yahoo! websites, most notably on the Yahoo! front page (a 200+% improvement in click-rates). He also works closely with teams in computational advertising to deploy elaborate statistical models on the RightMedia Ad Exchange, yet another large scale recommender system. He currently serves as associate editor for the Journal of American Statistical Association (JASA) and IEEE Transaction on Knowledge discovery and Data Engineering (TKDE).

A Trillion Photos (Watch )

Steve Seitz, University of Washington and Google

Collectively, we take upwards of a trillion photos each year. These images together comprise a nearly complete visual record of the world's people, places, things and events. However, this record is massively disorganized, unlabeled, and untapped. This talk explores ways of transforming this massive, unorganized photo collection into reconstructions and visualizations of the world's sites, cities, and people. After a brief recap of our work on Photosynth and reconstructing Rome in a day, I will present new work on modeling places and people from large photo collections, and our work on MapsGL, Google's latest mapping product

Bio Steve Seitz is a Professor in the Department of Computer Science and Engineering at the University of Washington. He also directs an imaging group at Google's Seattle office. He received his B.A. in computer science and mathematics at the University of California, Berkeley in 1991 and his Ph.D. in computer sciences at the University of Wisconsin in 1997. Following his doctoral work, he spent one year visiting the Vision Technology Group at Microsoft Research and the subsequent two years as an Assistant Professor in the Robotics Institute at Carnegie Mellon University. He joined the faculty at the University of Washington in July 2000. He was twice awarded the David Marr Prize for the best paper at the International Conference of Computer Vision, and he has received an NSF Career Award, and ONR Young Investigator Award, and an Alfred P. Sloan Fellowship, and is an IEEE Fellow. His work on Photo Tourism (joint with Noah Snavely and Rick Szeliski) formed the basis of Microsoft's Photosynth technology. Professor Seitz is interested in problems in computer vision and computer graphics. His current research focuses on 3D modeling and visualization from large photo collections.