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My research involves stochastic design and control of sensing, communication, information processing, and learning in modern engineered systems. This covers broad theoretical questions in interactive learning and information acquisition as well as a variety of practical implementations from computer vision to service drones to wireless networks. Check our latest work on a variety of drone platforms for information acquisition: http://detecdrone.ucsd.edu/
On the theoretical front, I am most concerned with the problem of 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 inconsistent response from labelers. Here our objective has been to 1) develop algorithms that acquire the most informative features with the minimum query complexity and 2) design queries 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 even 3) computer vision.
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!). Here, I try to break down our theory work in (overlapping) areas:
For a detailed discussion of our work in each of these categories, click on a link above.