About Me

I'm a PhD candidate in particle physics at the California Institute of Technology, supervised by Prof. Maria Spiropulu. My experimental research includes the physics of the Higgs boson, supersymmetry and dark matter search at the Large Hadron Collider (LHC).

As a member of the Machine Learning for Particle Physics group at CERN, led by Dr. Maurizio Pierini, I focus 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 stability of the universe. At CERN, I led a team of 6 to 8 physicists and IT professionals to manage the production workflows of data and simulation samples 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.

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Papers

Selected Publications:

- TQ Nguyen, D Weitekamp, D Anderson et al. Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC. Comput Softw Big Sci (2019) 3: 12 [ePDF]
- O Cerri, TQ Nguyen et al. Variational Autoencoders for New Physics Mining at the Large Hadron Collider. J. High Energ. Phys. (2019) 2019: 36
- J Arjona Martinez, TQ Nguyen et al. Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description. ACAT 2019 Proceeding.
- EA Moreno, TQ Nguyen et al. Interaction networks for the identification of boosted decays. Phys. Rev. D 102, 012010 (2020).
- EA Moreno et al. JEDI-net: a jet identification algorithm based on interaction networks. Eur. Phys. J. C 80, 58 (2020).
- J-R Vlimant et al. Large-Scale Distributed Training Applied to Generative Adversarial Networks for Calorimeter Simulation. CHEP 2018 Proceeding
- D Weitekamp, TQ Nguyen et al. Deep Topology Classifiers for a More Efficient Trigger Selection at the LHC. Deep Learning for Physical Science Workshop, NeurIPS 2017.

The complete list of my publications is available on my Google Scholar profile.

Talks

Invited Lectures and Tutorials

- 2019/07: Autoencoders for Jet Physics. 1st Real Time Analysis Workshop. Orsay, France.
- 2019/06: PyTorch Tutorial. 3rd CMS Machine Learning Workshop. CERN, Switzerland.
- 2019/03: PyTorch for HEP-ML Research. CMS Experiment Group Meeting. CERN, Switzerland.
- 2019/02: Lectures on Machine Learning. VBSCan COST Network Training Event. Ljubljana, Slovenia.
- 2019/01: Machine Learning in Physics Analysis. CMS Data Analysis School. Pisa, Italy.
- 2018/07: PyTorch Tutorial. CMS Machine Learning Workshop. CERN, Switzerland.

Seminars, Conferences, and Workshops

- 2019/12: Interaction networks for the identification of Higgs boson decays to bottom quark-antiquark pairs. NeurIPS 2019 Machine Learning and the Physical Sciences Workshop. Vancouver, Canada.
- 2019/11: Interaction networks for jet characterisation at the LHC. CHEP 2019. Adelaide, Australia.
- 2019/11: MPI-based tools for large-scale training and optimization at HPC sites. CHEP 2019. Adelaide, Australia.
- 2019/11: New-Physics agnostic searches for New Physics. CHEP 2019. Adelaide, Australia.
- 2019/08: Variational Autoencoders for New Physics Mining at the Large Hadron Collider. LeptonPhoton 2019. Toronto, Canada.
- 2019/03: Deep Learning for Future Triggers in HEP Experiments. Level-1 Trigger Algorithms Workshop at CIEMAT. Madrid, Spain.
- 2019/03: Recurrent GANs for Particle-Based Simulation at the LHC. ACAT 2019. Saas Fee, Switzerland.
- 2019/03: Generative Adversarial Networks for Fast Simulation: Generalisation and Distributed Training in HPC. ACAT 2019. Saas Fee, Switzerland.
- 2019/03: Tagging Higgs Bosons at the LHC with Interaction Networks. ACAT 2019. Saas Fee, Switzerland.
- 2019/03: Variational Autoencoders for New Physics Mining at the Large Hadron Collider. ACAT 2019. Saas Fee, Switzerland.
- 2018/12: New Physics Mining with Deep Learning at the LHC. Fermilab Machine Intelligence Seminar. Batavia, Illinois, USA.
- 2018/07: CMS Workflow Failures Recovery Panel, Towards AI-assisted Operation. CHEP 2018. Sofia, Bulgaria.
- 2018/07: Topology Classifiers for High-Level Trigger Cleanup. CMS Machine Learning Workshop. CERN, Switzerland.
- 2017/12: Deep topology classifiers for a more efficient trigger selection at the LHC. NeurIPS 2017 Deep Learning for Physical Sciences Workshop. Long Beach, CA.

Awards

- CMS Achievement Award, For outstanding contribution to the CMS collaboration at CERN, 2019.
- FUTI Global Leadership Award, Selected as one of the top 4 undergraduate students in the US to conduct research at UTokyo, 2014.
- Julia Williams Van Ness Merit Scholarship, Awarded to the highest-achieving undergraduate in the School of Natural Sciences and Mathematics (UT Dallas), 2014.
- RISE Scholarship, Awarded by the German Academic Exchange Service (DAAD) to conduct research in Germany, 2014.
- Undergraduate Research Scholar Award, In recognition of outstanding undergraduate researchers at UT Dallas, 2013.
- Phi Kappa Phi, A national honor society exclusive to the top 5% of the class, 2013.
- Academic Excellence Scholarship, A merit-based, full-ride scholarship for 4 years of studies at UT Dallas, 2011.

Timeline

Work

AI Resident - Google X

Sep 2020 - Present

Machine Learning Scientist Intern - Microsoft

Jun 2020 - Sep 2020

Graduate Researcher - Caltech

Jul 2015 - Expected Sep 2021

Caltech Graduate Research Associate -- CMS Collaboration, CERN

Jun 2016 - Nov 2019

Research Intern - University of Tokyo

Jun 2014 - Jul 2014

Research Intern - Karlsruhe Institute of Technology

May 2013 - Aug 2013

Teaching Assistant & Peer Tutor - UT Dallas

Feb 2013 - May 2015

Education

Caltech

July 2015 - Expected June 2021

Doctor of Philosophy, Physics

UT Dallas

August 2011 - June 2015

Bachelor of Science, Physics
Magna Cum Laude, Collegium V Honors, and Major Honors.

Hanoi Architectural University

August 2010 - June 2011

Bachelor of Architecture
(Did not graduate, but I still do design as a hobby.‎)

Hanoi-Amsterdam High School

September 2007 - May 2010

Clients

Get in touch

  • E-mail:

  • Address 1:

    1200 E. California Blvd, MC 256-48, CA, USA 91125
  • Address 2:

    CERN Bld. 32/3-A-03, Espl. des Particules 1, 1211 Genève, CH
All Rights Reserved © 2018 Thong Nguyen