Jamie Haddock, PhD, Assistant Professor of Mathematics, specializes in mathematical data science, optimization, and applied convex geometry. Haddock earned her Ph.D. in Applied Mathematics from the University of California, Davis. Before joining the faculty at Harvey Mudd, she held a CAM postdoctoral position in the Mathematics department at UCLA.
Haddock is passionate about leveraging mathematical tools, such as those from probability, combinatorics, and convex geometry, to understand data, and the models and methods used to analyze it. Her recent areas of focus include randomized numerical linear algebra, combinatorial methods for convex optimization, tensor decomposition for topic modeling, network consensus and ranking problems, and community detection on graphs and hypergraphs. Her recent work is supported by NSF grant “Tensor Models, Methods, and Medicine” and includes collaborations with experts outside mathematics and outside academia.
Haddock especially enjoys mentoring undergraduate research in these topics and developing curriculum for introducing the mathematics of data science to students. Her teaching philosophy and outlook on the mathematics community are largely influenced by Federico Ardila’s axioms. One of the main goals of her career is to increase for groups that have been historically excluded from research mathematics access to, and representation in, this community.