Presented By: Electrical and Computer Engineering
Active Data Collection, Hypothesis Testing, and Learning
Dr. Tara Javidi, Professor of ECE at UC San Diego
Dr. Tara Javidi is the 2020 ECE Distinguished Educator award winner.
This talk revisits the problem of active hypothesis testing: a classical problem in statistics in which a decision maker is responsible to actively and dynamically collect data/samples so as to enhance the information about an underlying phenomena of interest while accounting for the cost of communication, sensing, or data collection. This talk, specifically, explores an often overlooked connection between active hypothesis testing and a wide variety of problems in engineering and the next generation artificial intelligence. This connection, we argue, has significant implications for next generation of information acquisition and machine learning algorithms where data is collected actively and/or by cooperative yet local agents.
In the first part of the talk, we discuss the history of active hypothesis testing (and experiment design) in statistics and the seminal contributions by Blackwell, Chernoff, De Groot, and Stein. In the second part of the talk, we discuss the information theoretic view of feedback and actions. We will illustrate the utility of this information theoretic analysis in a number of practically relevant problems in the design of next generation of networks.
Bio:
Tara Javidi received her MS degrees in electrical engineering (systems) and in applied mathematics from the University of Michigan, Ann Arbor where she her Ph.D. in electrical engineering and computer science in 2002. She is currently a professor of electrical and computer engineering and a founding co-director of the Center for Machine-Intelligence, Computing and Security at the University of California, San Diego. She is also a co-PI at The NSF AI Institute for Learning-enabled Optimization at Scale (TILOS).
Tara Javidi’s research interests are in theory of active learning and statistical inference, information theory with feedback, stochastic control theory, and wireless communications and communication networks.
Tara Javidi is a Fellow of IEEE. She and her Phd students are recipients of the 2021 IEEE Communications Society & Information Theory Society Joint Paper Award. She was awarded University of Michigan ECE’s 2021 Distinguished Alumni Educator Award. She also received the 2018 and 2019 Qualcomm Faculty Award for her contributions to wireless technology. 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. At UCSD, she has also received awards for her exceptional University service/leadership and contributions to diversity.
This is being offered as a hybrid event. U-M authentication is required to join the webinar.
https://umich.zoom.us/j/99392452117
This talk revisits the problem of active hypothesis testing: a classical problem in statistics in which a decision maker is responsible to actively and dynamically collect data/samples so as to enhance the information about an underlying phenomena of interest while accounting for the cost of communication, sensing, or data collection. This talk, specifically, explores an often overlooked connection between active hypothesis testing and a wide variety of problems in engineering and the next generation artificial intelligence. This connection, we argue, has significant implications for next generation of information acquisition and machine learning algorithms where data is collected actively and/or by cooperative yet local agents.
In the first part of the talk, we discuss the history of active hypothesis testing (and experiment design) in statistics and the seminal contributions by Blackwell, Chernoff, De Groot, and Stein. In the second part of the talk, we discuss the information theoretic view of feedback and actions. We will illustrate the utility of this information theoretic analysis in a number of practically relevant problems in the design of next generation of networks.
Bio:
Tara Javidi received her MS degrees in electrical engineering (systems) and in applied mathematics from the University of Michigan, Ann Arbor where she her Ph.D. in electrical engineering and computer science in 2002. She is currently a professor of electrical and computer engineering and a founding co-director of the Center for Machine-Intelligence, Computing and Security at the University of California, San Diego. She is also a co-PI at The NSF AI Institute for Learning-enabled Optimization at Scale (TILOS).
Tara Javidi’s research interests are in theory of active learning and statistical inference, information theory with feedback, stochastic control theory, and wireless communications and communication networks.
Tara Javidi is a Fellow of IEEE. She and her Phd students are recipients of the 2021 IEEE Communications Society & Information Theory Society Joint Paper Award. She was awarded University of Michigan ECE’s 2021 Distinguished Alumni Educator Award. She also received the 2018 and 2019 Qualcomm Faculty Award for her contributions to wireless technology. 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. At UCSD, she has also received awards for her exceptional University service/leadership and contributions to diversity.
This is being offered as a hybrid event. U-M authentication is required to join the webinar.
https://umich.zoom.us/j/99392452117
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