Research Engineer AI/ML

• Jul 2023 - Present

Technical and management support to several large-scale Defense Advanced Research Projects Agency (DARPA) programs in the Information Innovation Office (I2O) using my expertise in artificial intelligence and program management • Portfolios I provide technical/management support to encompass a wide range of technical areas including knowledge management, machine learning, geoscience, biomedicine, climate science, and statistics. • Extensive experience briefing executive-level DoD stakeholders on DARPA program capabilities, use-cases, and evaluation criteria. • Advise clients in optimal strategies related to program inception, design, source selection, full-lifecycle management, transition partner support, and technical program solicitation writing. Show less

Associate

• Apr 2023 - Jul 2023

Technical and management support to several large-scale Defense Advanced Research Projects Agency (DARPA) programs in the Defense Sciences Office (DSO) and Strategic Technology Office (STO) using my expertise in physics, artificial intelligence, and program management.• Portfolios I provide technical/management support to account for ~$80M in total funding and encompass a wide range of technical areas including atmospheric physics, machine learning, climate science, and space systems development.• Extensive experience briefing executive-level DoD stakeholders on DARPA program capabilities, use-cases, and evaluation criteria.• Advise clients in optimal strategies related to program inception, design, source selection, full-lifecycle management, transition partner support, and technical program solicitation writing.• Served as a Booz Allen compliance lead on multiple proposal teams for multimillion-dollar bids. Reviewed technical and management volumes to ensure that they met compliance standards set out by the U.S. Government.• Co-hosted and organized the 3n30 monthly speaker series highlighting three Booz Allen systems and digital engineering functional capabilities in 30 minutes. Duties included identifying key speakers providing critical capabilities to Booz Allen clients, scheduling, and dry run presentation advice to speakers. Show less

Senior Consultant

• Nov 2021 - Apr 2023

SUPA Higgs Fellow

• Oct 2017 - Oct 2021

• Showed that a machine learning (ML) technique (conditional variational autoencoders) could be used to provide fast estimates on parameters which characterize noisy gravitational wave time series. Published in Nature Physics. • Demonstrated a 6-orders-of-magnitude speed-up in detection of low-amplitude gravitational wave time series buried in Gaussian noise using a novel convolutional neural network approach. Published in Physical Review Letters. • Developed and baselined novel ML methods through comparison with traditional signal analysis techniques such as matched filtering and Bayesian inference. • Created and packaged a user-friendly signal detection and parameter estimation toolbox to be deployed in future observation runs by the Laser Interferometer Gravitational Wave Observatory (LIGO) for real-time gravitational wave parameter estimation. Show less

Research Intern

• Sep 2019 - Mar 2020

• Programmed a signal classification pipeline, leveraging emerging ML techniques, to be deployed directly onboard space-based satellite quantum key delivery systems. • Outcome of work was used by Craft Prospect to win a £100,000 joint industry-government grant for further research in signal classification methods for reliable quantum key assurance. • Programmed a signal classification pipeline, leveraging emerging ML techniques, to be deployed directly onboard space-based satellite quantum key delivery systems. • Outcome of work was used by Craft Prospect to win a £100,000 joint industry-government grant for further research in signal classification methods for reliable quantum key assurance.

Fulbright Scholar

• Sep 2016 - Jul 2017

• Implemented a strategy using neural networks to identify noise artifacts hindering gravitational wave search algorithms. • Developed a rapid detection statistic using deep learning classification methods to determine the significance of a gravitational wave event. Work was integrated into the primary gravitational wave signal detection pipeline currently used by the LIGO Scientific Collaboration. • Implemented a strategy using neural networks to identify noise artifacts hindering gravitational wave search algorithms. • Developed a rapid detection statistic using deep learning classification methods to determine the significance of a gravitational wave event. Work was integrated into the primary gravitational wave signal detection pipeline currently used by the LIGO Scientific Collaboration.

Instructor

• Jun 2016 - Sep 2016

Undergraduate Research Assistant

• Jan 2013 - Aug 2016

• Constructed a rapid noise artefact identification software package using genetic programming to better localize the sources of LIGO detector glitches.• Authored the “Terramon” machine learning monitor tool used at the LIGO mission control rooms to help predict the low frequency noise effects of imminent earthquakes to the detection of gravitational wave events.

Physics 108 Lab Teaching Assistant

• Jan 2016 - May 2016

- I Instructed an introductory physics lab with over twenty students. Directed students on the weekly lab procedures and answered any questions they may have, then graded the lab assignments.

LIBA 150 Teaching Assistant

• Aug 2015 - Dec 2015

Instructed an interdisciplinary science lab with over twenty students. Directed students on the weekly lab procedures and answered any questions they may have, then graded the lab assignments.

Undergraduate Research Assistant for NSF REU Fellowship

• May 2015 - Aug 2015

• Developed a fast noise artefact classification algorithm for gravitational wave data analysis using unsupervised ML clustering methods (self-organizing maps). • Developed a fast noise artefact classification algorithm for gravitational wave data analysis using unsupervised ML clustering methods (self-organizing maps).

Undergraduate Research Assistant for NSF IREU Fellowship

• May 2014 - Aug 2014

Characterized and improved one of the standard LIGO noise characterization software packages (Omicron) using a suite of statistical figures of merit. Characterized and improved one of the standard LIGO noise characterization software packages (Omicron) using a suite of statistical figures of merit.