• Member of a 16-person international team who develops, maintains, and implements code packages written in Python and C++ to search for undiscovered particles and perform statistical analyses using the immense 10+ petabyte dataset collected with the ATLAS Experiment at the Large Hadron Collider, CERN.• Designed and executed an analysis strategy utilizing a C++-based statistical analysis framework to arrive at final analysis deliverables such as the observed significance of potential new heavy boson resonances in the ATLAS dataset.• Pioneered unsupervised machine learning techniques using a Variational Recurrent Neural Network to enhance sensitivity to potentially undiscovered signatures.• Built a PyTorch-based data analysis framework to build, train, and evaluate the model on both simulated and real particle collision data, resulting in an enhanced sensitivity to potential new signatures of up to 700%.• First analysis in the 5,500+ member ATLAS Collaboration’s history to employ fully unsupervised searching methods.• 8+ years of teamworking experience as a part of the world’s largest scientific collaboration with over 10,000 members.• Led the development and deployment of a Python-based analysis package to characterize the effectiveness of Analog-to-Digital Converter upgrades for use in the ATLAS Experiment as part of a $5 million R&D budget granted to Columbia University.• Developed a C++-based analysis package to study and implement corrections to high-energy photon objects digitized by the ATLAS Detector, ultimately resulting in an improvement in timing resolution of such objects by around 25%.• Leveraged global computing networks and C++-based software to determine simulation-to-data scale factors for new jet tagging algorithms developed as a part of a suite of object tagging recommendations for the 2015-2018 data taking period.

• Aided the Liquid Argon (LAr) group in the commissioning and installation of hardware upgrades by means of testing, validating, and performing low-level checks of the new hardware readout and trigger paths. • Helped develop new calibrations and low-level tests using C++-based interfaces with the ATLAS Detector hardware.• Helped develop GUIs using PyQt5 to allow shifters in the ATLAS Control Room to be able to use these tools without the prior expertise necessary to do so otherwise.• Monitored the activity of the Liquid Argon Calorimeter and performed regular calibrations of its instruments in the ATLAS Control Room during the final months of the 2018 data-taking period.