I am a scientist at Microsoft. I leverage machine learning to transform the Office suite into an intelligent ecosystem that learns from trillions of user interactions to understand user behaviors and boost their productivity. I co-created and led the development of the LLM orchestrating system for Microsoft 365 Copilot in Office.
Prior to Microsoft, I obtained a PhD in Particle Physics at Caltech, advised by Maria Spiropulu. My experimental research included the physics of the Higgs boson, supersymmetry and dark matter searches at the Large Hadron Collider (LHC).
As a former member of the Machine Learning for Particle Physics group at CERN, led by Maurizio Pierini, I focused on using machine learning techniques to extend the discovery reach of the LHC physics program and to deliver solutions that address the big challenges that High Energy Physics will face in the next decade.
I introduced and developed various algorithms to be used at the LHC experiments—such as the first anomaly detector based on autoencoder to catch New Physics events at the collider, the topology classifer to improve the efficiency of real-time event selection system that can reduce the false-positive rate by more than one order of magnitude while retaining 99% of the signal, or the graph interaction network for jet physics that improved upon the existing state-of-the-art algorithm by 4 times. I have been invited to deliver a number of lectures and tutorials, primarily on machine learning in high energy physics, at CERN and around Europe.
My physics analysis paved the way to the world's most stringent constraints on double-Higgs production's cross section that can be used to probe the metastability of the universe. At CERN, I led a team of 6 to 8 physicists and IT professionals to manage the production workflows of data processing over the worldwide LHC computing grid, consisting of ∼80 computing centers in 5 continents. For this work, I was nominated and selected for the 2019 Achievement Award by the CMS collaboration at CERN.
I was also a former intern at Microsoft and a former AI resident at X, Google's moonshot factory. Prior to my PhD, I did particle physics research with groups led by Joseph Izen (UT Dallas), Hiroaki Aihara (University of Tokyo), and Klaus Eitel (Karlsruhe Institute of Technology).
Selected Publications:- E Govorkova, TQ Nguyen et al., Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider. Nature Machine Intelligence 4, 154–161 (2022).
Invited Lectures and Tutorials- 2023/06: Training and Finetuning Large Language Models with DeepSpeed on Azure Machine Learning. Microsoft Machine Learning, AI, and Data Science (MLADS) Conference. Redmond, WA.
Seminars, Conferences, and Workshops- 2023/06: Natural Language Commanding for Program Synthesis. Microsoft Machine Learning, AI, and Data Science (MLADS) Conference. Redmond, WA.
Doctor of Philosophy, Physics
Thesis: Searches for Nonresonant Higgs Boson Pair Production and Long-Lived Particles at the LHC & Machine-Learning Solutions for the High-Luminosity LHC Era
Master of Science, Physics
Bachelor of Science, Physics
Magna Cum Laude, Collegium V Honors, and Major Honors.
Bachelor of Architecture
Class President. Dropped out after the 1st year, but I still do design as a hobby.
Address:Bld 36, One Microsoft Way, Redmond, WA 98052