[Bnc-faculty-all-list] Webinar by Prof. Aly Al Gamal, Purdue: April 1 10AM-11AM

[Gupta, Sumeet Kumar]

Hello

It is my great pleasure to announce Prof. Aly Al Gamal's talk on April 1, 2021 from 10AM-11AM in the Seminar Series for ECE Faculty and Students. The details are below and attached. Hope to see you there.

Thanks

Sumeet

Zoom Link: https://purdue-edu.zoom.us/j/3164232249
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Title:   Wireless Communication Systems: From Information Theory to Deep Learning

Abstract: The history of wireless communications provides an ideal example for a complex and difficult engineering problem that has been successfully tackled via a principled and rigorous approach that relies on clean mathematical models. Information theory, first introduced as a mathematical theory of communication, paved the way for existence proofs in channel and source coding, where instead of searching for an exact structure of a successful code, one can carry out the easier task of finding a family of codes containing successful ones. This simple approach enabled a hierarchical design, where we can carry out research on overly complex tasks while still having open problems about the underlying building blocks. In this talk, we will provide examples for such complex tasks where progress has been made thanks to this hierarchical vision. Interestingly, with state of the art deep learning algorithms, the rationale of finding a large family of functions containing correct ones, is also the driving force. Further, with empirical data-driven research, all we can hope for is a proof-of-concept; in other words, an “existence” proof. This inspires us to propose a hierarchical framework for employing deep learning in wireless communication systems. The lowest level of the hierarchy consists of source identification tasks that facilitate identifying transmission origins for a given received signal. Given accurate source identification, analyzing and predicting peer behavior becomes feasible. Finally, spectrum/context understanding tasks like scenario classification and recognizing the behavior of peer networks can take place by building on lower-level capabilities. Our discussion is aided by concrete preliminary results, datasets, and lessons learned from Purdue’s participation in the DARPA Spectrum Collaboration Challenge (SC2). In summary, we will discuss in this talk concrete examples at the top of the hierarchical approach for using information theory for wireless communication networks, and concrete examples at the bottom of the hierarchical approach for using deep learning for the same overarching task, since the latter is the less mature area of research.

Speaker Bio: Aly El Gamal is an Assistant Professor at the Electrical and Computer Engineering Department of Purdue University. He received his Ph.D. degree in Electrical and Computer Engineering and M.S. degree in Mathematics from the University of Illinois at Urbana-Champaign, in 2014 and 2013, respectively. Prior to that, he received the M.S. degree in Electrical Engineering from Nile University and the B.S. degree in Computer Engineering from Cairo University, in 2009 and 2007, respectively. His research interests include information theory and machine learning. Dr. El Gamal has received a number of awards, including the Purdue Seed for Success Award, the Purdue CNSIP Area Seminal Paper Award, the Purdue Engineering Outstanding Teaching Award, the DARPA Spectrum Collaboration Challenge (SC2) Contract Award and Preliminary Events 1 and 2 Team Awards, and the Huawei Innovation Research Program (HIRP) OPEN Award. He is currently serving as the lead engineer for the Purdue/West Point participant team in the Indy Autonomous Challenge, as an associate editor in the area of Machine Learning and AI for Wireless at the IEEE Transactions on Wireless Communications, and as a reviewer for the American Mathematical Society (AMS) Mathematical Reviews.
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