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Senior Machine Learning Engineer

April 2023 - Present

SLAC National Accelerator Laboratory

Software Engineer II, Machine Learning Engineer

August 2021 - October 2022

  • Designed an online machine-learning system for particle accelerator control systems.

  • Designed and managed the deployment workflow for the lab's Python research environments.

  • Implemented and managed CI workflows for machine learning research projects.

  • Built visualization tools for various accelerator applications.

Software Engineer I, Advanced Controls Systems

April 2020 - August 2021

  • Designed the Next Alarm System (NALMS) for use with particle accelerator control systems running EPICS.

  • Supported development efforts in the lab's machine learning group. Built tools to serve the inputs and outputs of models over the accelerator control system and developed applications for visualizing model output. 


Lead Data Scientist/Machine Learning Engineer

December 2018 - April 2020

  • Maintained company natural language processing infrastructure including data preprocessing, entity tagging, and sentiment scoring workflows for the company document repository.

  • Implemented feature selection methods (recursive elimination, CV threshold, chi-squared, etc.) for auto-ML pipeline.

  • Designed text summarization method applying k-means clustering to named entities.

  • Built model registry service and API.

Vertex Pharmaceuticals

Clinical Trial Blockchain Intern

May 2018 - Aug 2018

  • Developed proof-of-concept application for the transfer of clinical trial documents using Quorum, JP Morgan's Ethereum fork.

Healthcare Systems Engineering Institute at Northeastern University

Graduate Research Assistant

August 2017 - September 2018

  • Applied mathematical epidemiology methods to prescription opioid and heroin co-epidemics.

  • Conducted bifurcation analysis on differential equations model of opioid epidemic.

Staff Engineer

September 2016 - August 2017

  • Developed simulations of the prescription opioid and heroin co-epidemic and formulated policy proposals by running inverse optimizations over simulation drivers and historical benchmarks.

  • Authored the first draft of NSF EAGER grant, (awarded, $250,000) and led the grant's modeling team in simulation development (systems dynamics,cellular automata, agent-based).

AddiLat Inc.

Semiconductor Characterization Intern

September 2016 - December 2016

  • Managed experiments in characterizing the electrical properties of nanoparticle-printed films.

  • Handled furnace annealing of semiconductor film plates.

Publications & Presentations

Zhang, Z., Edelen, A. L., Garrahan, J. R., Hidaka, Y., Mayes, C. E., Miskovich, S. A., ... & Wang, G. M. (2022). Badger: The Missing Optimizer in ACR (No. TUPOST058). SLAC National Accelerator Lab., Menlo Park, CA (United States).

Garrahan, J. Mayes, C. Slepicka, H. Edelen, A. & Gupta, L. (2021). The LUME-EPICS Python package for supporting simulations over CA and PVA. Spring 2021 EPICS Collaboration Meeting.

C.E. Mayes, P.H. Fuoss, J.R. Garrahan, H. Slepicka, A. Halavanau, J. Krzywinski et al., Lightsource unified modeling environment (lume), a start-to-end simulation ecosystem, in Proceedings of IPAC, Campinas, SP, Brazil, 24–28 May 2021, THPAB217.

J. Benneyan, J. Garrahan, I. Ilieş and X. Duan, "Modeling approaches, challenges, and preliminary results for the opioid and heroin co-epidemic crisis," 2017 Winter Simulation Conference (WSC), 2017, pp. 2821-2832, doi: 10.1109/WSC.2017.8248006.

J. Garrahan, I. Ilieş and J. Benneyan, "Preliminary Integrated Approach to Modeling the Prescription and Illicit Opioid Epidemic," 2017 IINFORMS Annual Meeting, 2017.

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