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My research pursuits range from quantum information theory and machine learning to network analysis to mathematics and physics education. In my current research, I explore the use of machine learning in quantum computing platforms. In particular, I am investigating methods to automatically identify stable configurations of electron spins in semiconductor-based quantum computing. I am also developing a complete software suite to enable research in this field (i.e., a suite that models quantum dot devices, trains recognition networks, and auto-tunes experimental setups). Such suite will eliminate the need for heuristic calibration and help scale up quantum computing into larger quantum dot arrays.

I also want to develop novel linear and non-linear positive maps that to characterize entanglement in quantum systems and delineate the space of quantum states. I have been successful in describing several families of such linear maps in even dimensions. However, a rigorous proof of the behavior of their non-linear counterparts remains a challenging open issue. Currently, I am examining the constructions of maps in arbitrary dimensions, nonlinear extension and the nature of detectable entangled states.

In education research, I am specifically interested in understanding what factors affect student retention and persistence in pursuing their degree. Using social network analysis, I look at the correlations between students’ overall embeddedness within the in- and out-of-class network and their odds of persistence into the second semester in a sequence. I also investigate how the attitudes toward learning with peers translate into actual behaviors. My objective is to provide a concrete basis for efforts to increase overall student retention and graduation rates at both the departmental and university levels.