Jeremy
Wolcott
Research Assistant Professor
Physics & Astronomy
I study neutrinos---the smallest, and wackiest, of the known fundamental particles in the universe. Unlike any of the other basic particles we know about—including the more familiar ones, like the electron, as well as the quarks that make up protons and neutrons—the three known types of neutrinos are simultaneously both stable (don't undergo radioactive decay) and yet likely to exchange their identities with each other while traveling along. We think these "neutrino oscillations" likely have important consequences for what we can learn about really deep questions in physics: like why the universe is made almost entirely of matter, and almost no antimatter; how (and why) particles get mass in the first place; and why it is that fundamental particles seem to always come in threes. I'm a collaborator (and hold leadership positions) on two large-scale neutrino oscillation experiments hosted at the Fermi National Accelerator Laboratory (Fermilab): NOvA (https://novaexperiment.fnal.gov) and DUNE (https://www.dunescience.org/).
Big particle physics experiments like NOvA and DUNE use "big data" (companion experiments at the Large Hadron Collider produce real-time data that's orders of magnitude more information than Netflix!), and I'm also interested in the infrastructure and analysis techniques we use to analyze that data. How we use data analysis tools, from big C++ frameworks to standalone Python notebooks based on numpy, has big implications for how efficiently we can do science.
Finally, particle physics's huge collaborations and distributed management structures present challenges for mentoring and developing scientists-in-training, from undergraduate researchers to postdocs. I'm fascinated by how we learn in these modified apprentice-expert situations, and am thinking about how to apply the existing research on mentoring in higher education to this unique context.
