emclaughlin2

Hi there 👋 I'm Emma McLaughlin.
I am interested in using data-driven approaches to drive innovation and efficiency, especially in energy, infrastructure, and technology fields.
I am currently working as a graduate researcher in experimental nuclear and particle physics with day-to-day experience analyzing large-scale datasets using statistical/machine learning methods, developing Monte Carlo simulations and supporting large capital project operations using data-driven insights.
A PDF version of my resume is available here and contains similar information to this README

Graduate Research Assistant, Columbia University, 2021 – present

Led two task forces (4 and 8 scientists) to award-winning presentation and publication of 1-2 year long physics analyses with high-volume data using statistical methods including work with iterative Bayesian unfolding and highly correlated measurements using Python, SQL, Pandas, C++, and the ROOT framework
Updated sPHENIX C++-based Monte Carlo simulation, scripting and analysis integration codebases, including low-level waveform reconstruction and analysis using least squares fit to a template function and geometry updates, with optimizations for time complexity and scalability to 10s of billions of events

Built and maintain detector state database logging and real-time monitoring data visualization using Python, PostgreSQL, and Grafana (3 years of continuous operation)
Independently investigated a suspected hardware failure in analog-to-digital converter readout boards to finding a firmware issue and enabling a software patch to avoid hardware replacement with approx. $3M savings
Serve as on-call subsystem expert responsible for 24/7 consistent operation of the sPHENIX calorimeters

Built Airtable database of contacts from NYC schools, community organizations, public housing to streamline program promotion and track participation of targeted communities

Provided technical data analysis and creative guidance to undergraduate team using NASA Earth data
Scheduled workshop review of statistical and ML analysis techniques, including Iterative Proportional Fitting for sparse data, random forest and RNN models, and provided technical guidance for successful random forest exoplanet classification project using Python and sklearn