Measurement-dependent Noisy Search: An Information Acquisition Approach

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• Prof. Tara Javidi, University of Calfornia, San Diego
• Monday 05 March 2018, 14:30-15:30
• LT6, Baker Building, CUED.

If you have a question about this talk, please contact Prof. Ramji Venkataramanan.

This talk has been canceled/deleted

Information acquisition problems form a class of stochastic control and decision problems in which a decision maker is faced with utilizing a stochastically varying (and uncontrollable) environment whose state is only partially observable to the decision maker. To best utilize the system, our decision maker can carefully control the acquisition process via which the set of (noisy) measurements are collected. I will discuss my research program to characterize the fundamental (information theoretic) limits and gains associated with dynamically controlling the acquisition process.

In this talk, we focus on the special case of measurement-dependent noisy search; this problem arises in a broad spectrum of applications such as medical diagnosis, spectrum sensing, sensor management, initial access in mmWave communication, and noisy group testing. We provide a non-asymptotic characterization of the optimal tradeoff between search time, resolution, and search reliability. Our framework rests upon reframing the contributions due to Wald, Blackwell, and DeGroot and identifying the missing link of information acquisition rate in Chernoff’s seminal work on active hypothesis testing. Our analysis is sequential in nature and connects De Groot’s notion of information utility with the Shannon theoretic concept of uncertainty reduction. Our achievability scheme, i.e. the optimal (adaptive) search strategy, generalizes the Posterior Matching of Shayevitz and Feder for channel coding with feedback.

This work was done in collaborations with my PhD students as well as Y. Kaspi, O. Shayevitz, and M. Wigger.

Bio: 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.

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. Tara Javidi is a Distinguished Lecturer of the IEEE Information Theory Society (2017/18).

This talk is part of the Signal Processing and Communications Lab Seminars series.

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