My research area involves stochastic analysis, design, and control of information collection, processing, and transfer in modern networked systems. This covers broad theoretical questions as well as practical implementation of various solutions. In my research group, we often prove theorems but we also build and test our theoretical findings when possible (graduate applicants, please refer to this link!). In particular, the work can be broadly broken into the following areas:
1. Active Learning, Active Hypothesis Testing and Sequential Information Theory,
2. Stochastic Control and Optimization of Networks, and
3. AI and Learning-enabled Optimization of Wireless Communications and Networks
On the theoretical front, I am most concerned with the problem of sequential information acquisition and interactive learning where the cost of data collection and/or labeling can be substantially reduced. Here the challenge is to deal with imperfect and noisy data as well as the dynamics of data. Here our objective has been to 1) develop algorithms that acquire the most informative features with the minimum cost and 2) design queries and data collections that account for the uncertainty and inconsistency (of humans) in the loop.
On the more practical front, I am interested to apply our developed algorithms in the following three application domains: 1) next generation wireless networks , 2) service drones, and other 3) decentralized learning and control systems.
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