My research pursuits range from quantum information theory and machine learning to network analysis to mathematics and physics education. In particular, I am interested in the development of methods to automatically identify stable configurations of single electron spins for use in semiconductor-based quantum computing systems. I also want to develop novel linear and non-linear positive maps that characterize entanglement and 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 in mathematical physics. Currently I am examining the constructions of maps in arbitrary dimensions, nonlinear extension and the nature of detectable entangled states.

Within the network analysis, I seek to develop and analyze methodology for studying large-scale networks, such as characterization of the topological structures related to the efficiency of the spread of information or contagion.

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 a 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.