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Seminars

Spectrum Morphing with Machine Learning
CMS Machine Learning Innovation (hybrid), Oct. 2023.

Re-imagining the search for fundamental interactions with machine learning
UC Berkeley, Sep. 2023.

Unbinned and High-dimensional Unfolding with Machine Learning
CERN (online), March 2023.

Building Robust Deep Learning Methods for High Energy Physics
Carnegie Mellon (online), Oct. 2022.

Towards Online Anomaly Detection for Particle Physics
University of Washington (online), August 2022.

Building Robust Deep Learning Methods for High Energy Physics
Google Core Science Seminar (online), August 2022.

Deep Generative Models for High Energy Physics
University of Bern, April 2022.

Discovering Unanticipated New Physics with Machine Learning
Berkeley Theory (BCTP) Seminar, March 2022.

Deep Learning, Quantum Information, and the LHC as a Gluon Factory
Harvard LPPC Seminar, Nov. 2021.

A first data result with deep learning-based unfolding
Berkeley Lab Physics Division ML Group Meeting, October 2021.

High Energy Scattering on a Quantum Computer
Berkeley Lab Thursday Science Forum on Quantum Information Science, September 2021.

Quantum Algorithms for High Energy Physics Simulations
IISc Quantum Technologies Initiative (IQTI), July 2021.

Extracting the Most from Collider Data with Deep Learning
Jefferson Lab AI Lunch, June 2021.

Quantum Algorithm for High Energy Physics Simulations
KIAS QUC-AIHEP, June 2021.

Simulating collider physics on quantum computers using effective field theories
All Things EFT, June 2021.

Group Anomaly Detection with Machine Learning
AICamp, June 2021.

Extracting the Most from Collider Data with Deep Learning
UCSD, virtual, May 2021.

Machine Learning in the Physical Sciences at National Laboratories
UC Irvine, virtual, May 2021.

Machine Learning for Fundamental Physics
AICamp, virtual, May 2021.

Radiation effects in the LHC experiments: Impact on detector performance and operation
LBNL Brown Bag Instrumentation Seminar, May 2021.

Where the wild things are: outlier detection with ML, a path to new discoveries
ML Club (virtual), April 2021.

Machine Learning for Fundamental Physics
University of San Francisco Seminar Series in Data Science (virtual), March 2021.

Building Robust AI/ML Methods for High Energy Physics
Berkeley Lab Science Forum (virtual), March 2021.

OmniFold: A Method to Simultaneously Unfold All Observables
T2K General Cross Section Meeting (virtual), March 2021.

Modeling final state radiation on a quantum computer
University of Geneva HEP Seminar (virtual), March 2021.

Extracting the Most From Collider Data With Deep Learning
University of Michigan HEP-Astro Seminar (virtual), March 2021.

Disentangling Boosted higgs boson Production Modes with Machine Learning
Michigan State Univesity High Energy Physics Seminar (virtual), Feb. 2021.

High dimensional jet measurements in pp and ep collisions
RHIP group, University of Tennessee (virtual), Feb. 2021.

A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties
CMS ML Forum, Virtual, Jan. 2021.

Extracting the most from collider data with deep learning
China, Virtual, Nov. 2020.

Anomaly detection with machine learning at the LHC
University of Liverpool HEP Seminar, Virtual, Oct. 2020.

Simulation-based and label-free deep learning for fundamental physics
HIT seminar, Nuclear Science Division, LBL, Virtual, October 2020.

Modeling final state radiation on a quantum computer
University of Washington EPE Seminar, Virtual, October 2020.

Modeling Final State Radiation on a Quantum Computer
Yao Group Meeting, Berkeley (Virtual), September 2020.

Jet Substructure and Machine Learning
High Energy Nuclear Physics in China Seminar, Virtual, September 2020.

Simulation-based and label-free deep learning for science
NERSC Data Seminar, Berkeley, May 2020.

Correcting for Detector Effects (Unfolding/Deconvolution) with Machine Learning
University of Washington EPE Seminar, May 2020.

Extracting the most from collider data with deep learning
Oxford ML and Physics Seminar, May 2020.

Extracting the most from collider data with deep learning
Cambridge University Cavendish HEP Seminar, May 2020.

Modeling final state radiation on a quantum computer
LBNL Physics Division Seminar (RPM), May 2020.

Gearing up for the 2021 APS DPF Community Planning Process (aka Snowmass)
LBNL Physics Division Seminar (RPM), April 2020.

Deep Learning, Quantum Information, and the LHC as a Gluon Factory
University of Pennsylvania particle physics seminar, Jan. 2019.

Modeling final state radiation on a quantum computer
Joint Johns Hopkins - University of Maryland seminar, Dec. 2019.

Extracting the most from collider data with deep learning
Heidelberg theory seminar, Nov. 2019.

Mysterious trends in radiation measurements for the ATLAS pixel detector
Brown Bag Instrumentation Seminar, LBNL, Nov. 2019.

Exploring hypervariate phase space with likelihood-free and label-free deep learning
Computations in Science Seminar, University of Chicago, Oct. 2019.

Deep Learning, Quantum Information, and the LHC as a Gluon Factory
Particle Physics Seminar, University of Chicago, Oct. 2019.

Precision Jet Substructure with the ATLAS Detector at 13 TeV
UCLA Nuclear Physics Seminar, May 2019.

Weak supervision for fundamental physics
CS Seminar, Stanford, April 2019.

Deep learning for LHC classification, regression, generation, and beyond
LUX/LZ ML working group meeting, April 2019.

Modeling final state radiation on a quantum computer
Cornell theory seminar, March 2019.

Preparing ATLAS pixels for the high rate and radiation environment of the HL-LHC
Cornell HEP experimental seminar, March 2019.

Advancing High Energy Physics with Machine Learning
UCR Data Science Seminar, Riverside, Jan. 2019.

A model agnostic machine learning search for resonant new physics
UCI HEP seminar, Irvine, Jan. 2019.

Generative Modeling with Machine Learning for High Energy Physics
Harvard Theory Seminar, Boston, Oct. 2018.

The Need for Online and Offline Speed When Analyzing the Biggest Scientific Dataset: Particle Physics at the Large Hadron Collider
Real-Time Decision Making Seminar, Simons Institute for the Theory of Computing, Berekeley, May 2018.

Modern Machine Learning with Jet Images for High Energy Physics
LPNHE Paris HEP Seminar; RAL UK Particle Physics Department Seminar, April 2018.

Jet mass measurements with substructure tools
Collider Cross Talk (with Gregory Soyez), CERN, April 2018.

Deep Learning with the Largest Scientific Dataset
Neural Systems and Engineering Lab Seminar, LBNL, April 2018.

Accelerating HEP Inference with Deep Neural Networks
Fermilab Machine Learning Group, April 2018.

Modern Machine Learning for Jet Physics at the Large Hadron Collider
Michigan State University Machine Learning Seminar, March 2018.

Precision Jet Substructure with the ATLAS Detector at 13 TeV
Los Alamos Nuclear and Particle Physics, Astrophysics and Cosmology Seminar, March 2018.

Machine Learning for Jet Physics at the Large Hadron Collider
AI@SLAC Seminar; University of Maryland Particle Theory Seminar; Johns Hopkins Experimental HEP Seminar, February 2018.

Advanced Machine Learning for Classification, Regression, and Generation in Jet Physics
INFN Genova joint Theory/Experiment Seminar; Fermilab Theory Seminar; Argonne Theory Seminar; Enrico Fermi Institute Data Analytics Workshop (University of Chicago), Fall 2017.

Observables for possible QGP signatures in central pp collisions
CERN Theory Seminar; LBNL Nuclear Science Theory/Experiment Seminar, Fall 2017.

Jet Substructure
LBNL xTalk Seminar, September 2017.

Preparing ATLAS pixels for the high rate and radiation environment of the HL-LHC, SLAC Experimental Seminar, September 2017.

CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
LHCb Statistics WG meeting, CERN, June 2017.

Track reconstruction inside collimated jets
Tracking Progress Meeting, LBNL, February 2017.

Grand Challenges for Jet Substructure
HEP Seminar, University of Oregon; University of Göttingen; University of Valencia, January 2017.

Jet Metacognition: What can we learn from machine learning about jets?
LEPP Journal Club, Cornell University, April 2016.

Measuring and exploiting the quantum properties of jets with the ATLAS detector
LBNL Physics Division Research Progress Meeting, Berkeley, December 2015.

Light Stops, Kinematic Variables, and Gaps from LHC Run I
Cavendish High Energy Physics Seminar, Cambridge University, April 2015.

Recent Progress on the Stealth Stop
SLAC/Stanford joint Theory/Experiment Jamboree, November, 2014.

Where is sWaldo? Searching for sTops amongst the SM crowd with the ATLAS Detector
SLAC Experimental Seminar; UC Irvine Joint Particle Seminar, October 2014.

A loophole from loops: consequences of multiple solutions in the CMSSM
SLAC Theory Seminar, February 2014.

Jet Charge Studies with ATLAS
HEP Seminar, University of Nebraska at Lincoln; LEPP Journal Club, Cornell University; LPPC Seminar, Harvard University, Fall 2013.

Search for direct stop pair production in single lepton events at sqrt(s) = 8 TeV at ATLAS
ATLAS Conference Note: ATLAS-CONF-2013-037. Collider-Cross-Talk, CERN, January 24, 2013.

Measuring the Top Quark Mass at the LHC with Application to New Physics
Cavendish High Energy Physics Seminar, Cambridge University, November 6, 2012.