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My research aims to develop a new generation of inference methods and theory for modern statistics and machine learning, especially focusing on:

  • Combinatorial functionals like connectivity, degree, and other topological structures of graphs, ranking, clustering, hyper graphs, etc;
  • Complex data structures like high dimensionality, heterogeneity, nonlinearity, heavy-tailness, time-dependency, etc;
  • Complicated algorithms like distributed algorithms, nonconvex optimization, kernel methods, etc.

With the problems above, I am interested in studying the uncertainty assessment methodology, probabilistic universality phenomenon, and information-theoretical lower bound theory. My research finds main applications in computational neuroscience.


Ph.D., 2018, Princeton University