Stanford University
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Lauren Tompkins
Associate Professor of Physics
Current Research and Scholarly InterestsProfessor Tompkins’s research focuses on understanding the relationships which govern matter’s most fundamental constituents. As a member of the ATLAS experiment at the Large Hadron Collider (LHC), she utilizes the world’s highest energy person-made particle collisions in order to understand the mechanism that gives particles mass, whether or not our current model of elementary particle interactions is a complete description of nature, and if dark matter can be produced and studied in colliders.
In order to search for the exceedingly rare interactions which may provide insight to these questions, the LHC will produce a blistering rate of 50 to 80 proton-proton collisions every 25 nanoseconds in 2015 and beyond. Professor Tompkins works on the design and implementation of custom electronics which will improve the ATLAS experiment’s ability to pick out the collisions which produce the Higgs bosons, dark matter particles and other rare events out of the deluge of ordinary interactions. Her group focuses on particles called heavy flavor fermions, the most massive particles not responsible for mediating interactions. Because they are so heavy, they may have a special connection to the origin of mass or physics beyond our current models of particle interactions.
She is additionally a member of the Light Dark Matter Experiment (LDMX), a proposed experiment to produce and detect dark matter in the laboratory utilizing an accelerated beam of electrons, and the Heavy Photon Search Experiment, which searches for visible decays of dark photons.
Please see her group website for a full description of her research activities. -
Dat Tran
Masters Student in Statistics, admitted Autumn 2024
BioDat Tran is an M.S. Statistics/ Data Science student in the Stanford Statistics department. Prior to joining Stanford, Dat was a Data Scientist at Mobilewalla, where he was the co-author of Anovos, one of the most efficient PySpark open-source libraries for large-scale data, as well as multiple B2B Data Science solutions in Telecommunications, FinTech and Large Language Models (LLMs). Dat graduated Cum Laude with a bachelor's in Data Science at University of Texas at Dallas.
