Vincent Dumontverfied profile
I am a Postdoctoral Researcher at the Lawrence Berkeley National Laboratory, currently working on hyperparameter optimization using surrogate modeling and uncertainty quantification. In my previous project at Berkeley Lab, I worked on deep learning applications for Distributed Acoustic Sensing data from Dark Fiber Testbed for groundwater mapping and broadband seismic event detection. My current areas of interest are machine learning, sensor network technology, and data scalability in high-performance computing environments. I am also interested in quantum computing and in particular the challenges regarding noise-canceling for superconducting qubit systems and I/O communication between quantum processors and classical controllers. Before joining Berkeley Lab, I was a postdoc at the University of California, Berkeley where I specialized in magnetometer networks for coherent searches of magnetic transient events. One of the networks I worked on is part of an international effort to detect inhomogeneous dark matter and consists of over a dozen magnetically shielded high-sensitivity atomic magnetometers, each of which is synchronized to the GPS timing. Another network, for which I am still heavily involved, consists of a distributed detector of triple-axis fluxgate magnetometers aimed to study the magnetic pulse of cities, that work is done collaboratively with the Center of Urban Science and Progress in New York City. Before coming to Berkeley, I completed my Ph.D. in Astrophysics at the University of New South Wales in Australia, where I analyzed quasar absorption line data to investigate whether the values of fundamental constants of nature might have been different in the early Universe.