We start with Shannon´s problem of transmission of messages over noisy channels. Shannon’s famous solution of this problem was one of the starting points of our information society. Shannon used deterministic encoding and decoding of messages. It is clear that randomized encoding and decoding cannot increase the Shannon capacity for message transmission over noisy channels. Even if we use common randomness between the transmitter and receiver, it is not possible to increase the Shannon capacity for message transmission. As a next step, we consider the problem of secure message transmission of wiretap channels. In this case we discuss that it is already necessary to use local randomness at the transmitter to achieve the capacity for secure message transmission. We further discuss the problem of message transmission and secure message transmission of noisy channels with jammers. For these type of channels, local randomness at the transmitter is a very important resource that increases the capacity and stabilizes the communication from the transmitter to the receiver. In the second part of the talk we will introduce the communication task of identification. In the identification task, the receiver is interested in testing whether a specific message has been transmitted. The transmitter has no idea which message is interesting to the receiver. The identification task is very important for new applications, e.g. car to car, car to infrastructure, sensor networks, and for the tactile internet. If we only use deterministic encoding and decoding, then the capacity for identification over noisy channels is equal to the Shannon capacity for message transmission. So the number of messages that the receiver can identify grows exponentially with the block length. The situation changes dramatically if we can use local randomness at the transmitter. In this case we will show that the number of messages that the receiver can correctly identify grows doubly exponentially. We will show that the same is true for the secure identification task. We will extend this to the identification of noisy channels with a jammer. Here additional gains can be achieved by using a common randomness transmitter and receiver. We will further discuss storage of data and secure storage of private data on public databases. At the end of the talk we will discuss applications for big data.

This is joint work with Christian Deppe from TU Munich-LNT.

Holger Boche received the Dipl.-Ing. and Dr.-Ing. degrees in electrical engineering from the Technische Universität Dresden, Dresden, Germany, in 1990 and 1994, respectively. He graduated in mathematics from the Technische Universität Dresden in 1992. From 1994 to 1997, he did Postgraduate studies in mathematics at the Friedrich-Schiller Universität Jena, Jena, Germany. He received his Dr. rer. nat. degree in pure mathematics from the Technische Universität Berlin, Berlin, Germany, in 1998. In 1997, he joined the Heinrich-Hertz-Institut (HHI) für Nachrichtentechnik Berlin, Berlin, Germany.

Starting in 2002, he was a Full Professor for mobile communication networks with the Institute for Communications Systems, Technische Universität Berlin. In 2003, he became Director of the Fraunhofer German-Sino Lab for Mobile Communications, Berlin, Germany, and in 2004 he became the Director of the Fraunhofer Institute for Telecommunications (HHI), Berlin, Germany. Since October 2010 he has been with the Institute of Theoretical Information Technology and Full Professor at the Technische Universität München, Munich, Germany. Since 2014 he has been a member and honorary fellow of the TUM Institute for Advanced Study, Munich, Germany. He was a Visiting Professor with the ETH Zurich, Zurich, Switzerland, during the 2004 and 2006 Winter terms, and with KTH Stockholm, Stockholm, Sweden, during the 2005 Summer term.

Prof. Boche is a Member of IEEE Signal Processing Society SPCOM and SPTM Technical Committee. He was elected a Member of the German Academy of Sciences (Leopoldina) in 2008 and of the Berlin Brandenburg Academy of Sciences and Humanities in 2009. He received the Research Award ”Technische Kommunikation” from the Alcatel SEL Foundation in October 2003, the ”Innovation Award” from the Vodafone Foundation in June 2006, and the Gottfried Wilhelm Leibniz Prize from the Deutsche Forschungsgemeinschaft (German Research Foundation) in 2008. He was co-recipient of the 2006 IEEE Signal Processing Society Best Paper Award and recipient of the 2007 IEEE Signal Processing Society Best Paper Award. He was the General Chair of the Symposium on Information Theoretic Approaches to Security and Privacy at IEEE GlobalSIP 2016. Among his publications is the recent book Information Theoretic Security and Privacy of Information Systems (Cambridge University Press).

He has received many prizes for his work on mathematics and computing, and is a founder of Interactive Supercomputing, Inc. and Julia Computing, Inc.

He received the B.S. & M.S. degrees in mathematics from Yale in 1984, and the Ph.D. in applied mathematics from MIT in 1989 under the direction of Lloyd N. Trefethen. Edelman’s research interests include Julia, high-performance computing, numerical computation, linear algebra and random matrix theory. He has consulted for Akamai, IBM, Pixar, and NKK Japan among other corporations.

  Humans can be viewed as social sensors that input information to a social network. The interaction of social sensors present several challenges from a statistical signal processing viewpoint: sensors interact with and influence other social sensors resulting in herding behaviour. Second, due to privacy concerns, social sensors  reveal quantized decisions (ratings, recommendations).  Third, social sensors are risk averse decision makers with anticipatory emotions. This talk describes mathematical models for how social sensors interact over a social network, how social sensor decision-making can result in herding behaviour, and how herding can be mitigated by providing incentives to individual sensors. We will also discuss novel methods to poll social networks based on expectation polling and the friendship paradox. The seminar draws from ideas in statistical signal processing and behavioral economics.

From 2002-2016 he was the Canada Research Chair professor in statistical signal processing at the University of British Columbia, Canada. His current research interests include statistical signal processing and stochastic control with applications in social networks. Dr Krishnamurthy has served as Distinguished lecture for the IEEE signal processing society and Editor in Chief of IEEE Journal Selected Topics in Signal Processing. He received a honorary doctorate from KTH (Royal Institute of Technology), Sweden in 2013.

This lecture will move towards this world by beginning from well relatively understood maximum entropy modelization. We first show that non-Gaussian and non-Markovian stationary processes requires to separate scales and measure scale interactions, which can be done with a deep neural network. Applications to turbulence models in physics and cosmology will be shown.

We shall review deep Generative networks such as GAN and Variational Encoders, which can synthesize realizations of non-stationary processes or highly complex processes such as speech or music. We show that they can be considerably simplified by defining the estimation as an inverse problem. This will build a bridge with  maximum entropy estimation. Applications will be shown on images, speech and music generation.

Stéphane Mallat was Professor at the Courant Institute of Mathematical Sciences from 1988 to 1994. In 1995, he became Professor in Applied Mathematics at Ecole Polytechnique, Paris and Department Chair in 2001. From 2001 to 2007 he was co-founder and CEO of a semiconductor start-up company. From 2012 to 2017 he was Professor in the Computer Science Department of Ecole Normale Supérieure, in Paris. Since 2017, he holds the “Data Sciences” chair at the Collège de France.

Stéphane Mallat’s research interests include machine learning, signal processing, and harmonic analysis. He is a member of the French Academy of sciences, a foreign member of the US National Academy of Engineering, an IEEE Fellow and a EUSIPCO Fellow. In 1997, he received the Outstanding Achievement Award from the SPIE Society and was a plenary lecturer at the International Congress of Mathematicians in 1998. He also received the 2004 European IST Grand prize, the 2004 INIST-CNRS prize for most cited French researcher in engineering and computer science, the 2007 EADS grand prize of the French Academy of Sciences, the 2013 Innovation medal of the CNRS, and the 2015 IEEE Signal Processing best sustaining paper award.

The underlying connections between financial engineering and signal processing as well as optimization are too strong to be ignored. At the core, engineers try to model the system they deal with, be it a wireless communication channel or the price fluctuations in the financial markets. With a model of the reality in hand, one can then start making forecasts and design strategies for the future. In a wireless link, one may want to optimize the statistics of the signal to be transmitted by the antennas, whereas in a financial market one may attempt to optimize the investment strategies. This talk will provide a glimpse of financial engineering from a signal processing and optimization perspective, including topics on robust estimation, Kalman filtering, and discrete-state HMM, while exploring connections to other engineering disciplines.

Daniel P. Palomar received the Electrical Engineering and Ph.D. degrees from the Technical University of Catalonia (UPC), Barcelona, Spain, in 1998 and 2003, respectively.

He is a Professor in the Department of Electronic and Computer Engineering at the Hong Kong University of Science andTechnology (HKUST), Hong Kong, which he joined in 2006. Since 2013 he is a Fellow of the Institute for Advance Study (IAS) at HKUST. He had previously held several research appointments, namely, at King’s College London (KCL), London, UK; Stanford University, Stanford, CA; Telecommunications Technological Center of Catalonia (CTTC), Barcelona, Spain; Royal Institute of Technology (KTH), Stockholm, Sweden; University of Rome “La Sapienza”, Rome, Italy; and Princeton University, Princeton, NJ. His current research interests include applications of convex optimization theory, game theory, and variational inequality theory to financial systems, big data systems, and communication systems.

Dr. Palomar is an IEEE Fellow, a recipient of a 2004/06 Fulbright Research Fellowship, the 2004 and 2015 (co-author) Young Author Best Paper Awards by the IEEE Signal Processing Society, the 2015-16 HKUST Excellence Research Award, the 2002/03 best Ph.D. prize in Information Technologies and Communications by the Technical University of Catalonia (UPC), the 2002/03 Rosina Ribalta first prize for the Best Doctoral Thesis in Information Technologies and Communications by the Epson Foundation, and the 2004 prize for the best Doctoral Thesis in Advanced Mobile Communications by the Vodafone Foundation and COIT.

He has been a Guest Editor of the IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2016 Special Issue on “Financial Signal Processing and Machine Learning for Electronic Trading”, an Associate Editor of IEEE TRANSACTIONS ON INFORMATION THEORY and of IEEE TRANSACTIONS ON SIGNAL PROCESSING, a Guest Editor of the IEEE SIGNAL PROCESSING MAGAZINE 2010 Special Issue on “Convex Optimization for Signal Processing,” the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2008 Special Issue on “Game Theory in Communication Systems,” and the IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2007 Special Issue on “Optimization of MIMO Transceivers for Realistic Communication Networks.”

Because of these nonlinearities, conventional estimators based on empirical risk minimization generally involve solving a non-convex optimization program. We propose a method (called “anchored regression”) that is based on convex programming and amounts to maximizing a linear functional (perhaps augmented by a regularizer) over a convex set. 

The proposed convex program is formulated in the natural space of the problem, and avoids the introduction of auxiliary variables, making it computationally favorable. Working in the native space also provides us with the flexibility to incorporate structural priors (e.g., sparsity) on the solution.

For our analysis, we model the equations as being drawn from a fixed set according to a probability law.  Our main results provide guarantees on the accuracy of the estimator in terms of the number of equations we are solving, the amount of noise present, a measure of statistical complexity of the random equations, and the geometry of the regularizer at the true solution. We also provide recipes for constructing the anchor vector (that determines the linear functional to maximize) directly from the observed data.

We will discuss applications of this technique to nonlinear problems including phase retrieval, blind deconvolution, and inverting the action of a neural network.

This is joint work with Sohail Bahmani.

Dr. Romberg received the B.S.E.E. (1997), M.S. (1999) and Ph.D. (2004) degrees from Rice University in Houston, Texas; in 2010, he was named a Rice University Outstanding Young Engineering Alumnus. From Fall 2003 until Fall 2006, he was a Postdoctoral Scholar in Applied and Computational Mathematics at the California Institute of Technology. Justin Romberg has been on the faculty at the Georgia Institute of Technology since 2006 where he is the Schlumberger Professor in the School of Electrical and Computer Engineering. He is currently on the editorial board for the SIAM Journal on Imaging Science.