Active Learning and Optimization for Next Generation Wireless Systems

IEEE Santa Clara Valley Information Theory Society

Title: Active Learning and Optimization for Next Generation Wireless Systems 
Speaker: IEEE Information Theory Society Distinguished Lecturer, professor Tara Javidi 
Agenda:
6:00 P.M. Refreshments and Conversation
6:30 P.M. Presentation
Sponsorship:
The Santa Clara Valley Chapter of the IEEE Computational Intelligence (Artificial Intelligence) and Signal Processing Societies are co-sponsors of the event, along with the primary sponsor, the Information Theory chapter.
Abstract: 
Network management and configuration is an essential attribute of any wireless network with reliable self-tuning capabilities. However,the cost and overhead of network management has rarely been accounted for from a fundamental limit (information theoretic) perspective. In contrast to the past generations of networking solutions, on the other hand, in the ever-increasingly mobile and large-scale networks of tomorrow the network reconfiguration overhead may not be insignificant; this includes the initial beam alignment, link maintenance, spectrum sensing, packet resizing, etc. Our work aims to provide fundamental limits on the overhead associated with learning, network tuning, and optimization of network parameters. 
Our approach relies on fundamental notions in information theory and statistics to quantify the networking overhead and utilizes recent data analytic and machine learning algorithms to develop practical learning/optimization algorithms. In the first part of the talk, we consider the problem of reliably and quickly searching for a parameter of interest in a large signal space in face of measurement-dependent noise. This problem naturally arises in many practical communications systems such as the directional link establishment and maintenance (beam alignment) as well as spectrum sensing for cognitive radios. In the second part of the talk, we consider an important variant of the search problem: data-driven (Bayesian and non-Bayesian) function maximization and its connection to network parameter tuning.
Speaker's Biography:
Tara Javidi studied electrical engineering at Sharif University of Technology, Tehran, Iran from 1992 to 1996. She received her MS degrees in electrical engineering (systems) and in applied mathematics (stochastic analysis) from the University of Michigan, Ann Arbor, in 1998 and 1999, respectively. She received her Ph.D. in electrical engineering and computer science from the University of Michigan, Ann Arbor, in 2002.
From 2002 to 2004, Tara Javidi was an assistant professor at the Electrical Engineering Department, University of Washington, Seattle. In 2005, she joined the University of California, San Diego, where she is currently a professor of electrical and computer engineering. In 2013-2014, she spent her sabbatical at Stanford University as a visiting faculty. At the University of California, San Diego, Tara Javidi is a founding co-director of the Center for Machine-aware Computing and Security,  directs the Advanced Networking Science Lab and is a faculty member of the Centers of Information Theory and Applications (ITA), Wireless Communications (CWC), and Networked Systems (CNS). She is also a member of Board of Governors of the IEEE Information Theory Society (2017/18/19).
Tara Javidi’s research interests are in theory of active learning, information theory with feedback,  stochastic control theory, and stochastic resource allocation in wireless communications and communication networks. She was the guest editor for the IEEE Journal of Selected Areas in Communications special issue on Communications and Control. From 2011 to 2014, she was an associate editor for ACM/IEEE Transactions on Networking and the editor for the IEEE Information Theory Society Newsletter. She currently serves as an associate editor for IEEE Transactions on Information Theory and IEEE Transactions on Network Science and Engineering. Furthermore, she is currently on the board of the series "Foundations and Trends in Communications and Information Theory."
Tara Javidi was a recipient of the National Science Foundation early career award (CAREER) in 2004, Barbour Graduate Scholarship, University of Michigan, in 1999, and the Presidential and Ministerial Recognitions for Excellence in the National Entrance Exam, Iran, in 1992. In addition to numerous contributed and invited talks, she was a tutorial speaker at various international and prestigious conferences: International Conference on Cognitive Radio Oriented Wireless Networks (CROWNCOM) 2010, ACM International Symposium on Mobile Ad Hoc Networking and Computing (Mobihoc) 2013, International Symposium on Information Theory (ISIT) 2014, and IEEE Conference on Decision and Control (CDC) 2016. Tara Javidi is a Distinguished Lecturer of the IEEE Information Theory Society (2017/18).

David Packard Electrical Engineering Building

Our {communityGuidelinesLink} describe the sort of content we prohibit on Eventbrite. If you suspect an event may be in violation, you can report it to us so we can investigate.

To report other categories of prohibited or illegal content, submit {link}

Active Learning and Optimization for Next Generation Wireless Systems

More events from IEEE Santa Clara Valley Information Theory Society

Discover more events from IEEE Santa Clara Valley Information Theory Society, from Science & Tech to other experiences you might love.

Still looking for the right event?

Explore all events in Stanford and filter by date, category, and more to find the perfect fit.

Active Learning and Optimization for Next Generation Wireless Systems

More events from IEEE Santa Clara Valley Information Theory Society

Discover more events from IEEE Santa Clara Valley Information Theory Society, from Science & Tech to other experiences you might love.

You might also like...

Browse more events with different dates, prices, and formats to find your next great experience.

Still looking for the right event?

Explore all events in Stanford and filter by date, category, and more to find the perfect fit.