Universal parametrization of thermal photon rates in hadronic matter
M. Heffernan, P. Hohler, and R. Rapp. (2015). "Universal parametrization of thermal photon rates in hadronic matter." Phys. Rev. C. 95(027902).
CV
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Professional Experience
- AI Verification Engineer, Zoox (2022-Present): Working at the interface of Applied Research and Software Engineering
Education
- Ph.D. in Theoretical Nuclear Physics, McGill University, 2022
- M.Sc. in Physics, McGill University, 2018
- B.Sc.(Hons.) in Physics, The College of William & Mary, 2016
Research Experience
- Graduate Research Assistant - McGill University (2016-2022)
- Quantifying the properties of quark-gluon plasma using state-of-the-art physical models with Bayesian inference for the first time. My PhD work focused on using the best available models of heavy ion collisions, combined with methodological improvements to the Bayesian inference used in the field. Additional time is spent as a member of the JETSCAPE Collaboration’s Simulations and Distributed Computing Working Group.
- Undergraduate Student Researcher - The College of William & Mary (2013-2016)
- Undergraduate honors thesis using computational methods (Python, Fortran) to probe Big Bang Nucleosynthesis for limits on Beyond Standard Model physics. This work was performed under the direction of Andre Walker-Loud.
- Stirling Cycle Analyst for Nuclear Space Power Applications - NASA Glenn Research Center (2015)
- LERCIP Intern with the Thermal Energy Conversion Branch working to improve model fidelity and performing simulations for the Test Demonstration Unit (TDU) for the pre-testing Test Readiness Review. Additional work was done to optimize Stirling engines to map power density-efficiency space and customize the piston-displacer waveform. Future applications of this work include deep space and space exploration applications as passive power units
- REU Student - Texas A&M University and Cyclotron Institute (2014)
- National Science Foundation (US)-funded studentship under Ralf Rapp studying hot and dense hadronic matter, resulting in a state-of-the-art parametrization of thermal photon production in hadronic matter.
Skills
- Programming
- Python
- Packages include: pandas, sklearn, numpy, scipy, matplotlib, seaborn, joblib, docopt, vegas, uncertainties, flask, sqlalchemy, selenium
- Additional certifications: Udemy Python course
- Bazel
- Code review
- Data visualization
- Python
- Version control
- GitHub and Bitbucket
- LaTeX
- Linux/Unix operating systems, clusters, distributed computing (Slurm, PBS, GNU Parallel)
- Wolfram Mathematica
- Doxygen documentation
- Markdown
- Teaching
- Pedagogical development for flipping a premier introductory physics course at McGill
- Lab report and exam marking
- Preparing tutorials
- Leading student help sessions
Languages
- English
- Farsi
- German (Intermediate working fluency, but rusty)
Publications
Quantifying the sensitivity of Big Bang Nucleosynthesis to isospin breaking with input from lattice QCD
M. Heffernan, P. Banerjee, and A. Walker-Loud. (2017). " Quantifying the sensitivity of Big Bang Nucleosynthesis to isospin breaking with input from lattice QCD." [arXiv:nucl-th/1706.04991].
Hadronic transport coefficients from the linear sigma model at finite temperature
M. Heffernan, S. Jeon, and C. Gale (2020). "Hadronic transport coefficients from the linear sigma model at finite temperature." Phys. Rev. C. 102, 034906.
How about that Bayes: Bayesian techniques and the simple pendulum
M. Heffernan (2021). "How about that Bayes: Bayesian techniques and the simple pendulum." arXiv preprint 2104.08621
Bayesian quantification of strongly-interacting matter with color glass condensate initial conditions
M. Heffernan, C. Gale, S. Jeon, and J.-F. Paquet, (2023). "Bayesian quantification of strongly-interacting matter with color glass condensate initial conditions." arXiv preprint 2302.09478
Early-times Yang-Mills dynamics and the characterization of strongly interacting matter with statistical learning
M. Heffernan, C. Gale, S. Jeon, and J.-F. Paquet, (2023). "Early-times Yang-Mills dynamics and the characterization of strongly interacting matter with statistical learning." arXiv preprint 2306.09619
Talks
Comparative approaches to calculating transport coefficients in massive theories demonstrated in the Linear Sigma model
Talk at APS Division of Nuclear Physics Fall Meeting 2019, Washington, D.C.
Transport coefficients in the linear sigma model at finite temperature
Talk at Duke University QCD Group Seminar, Durham NC, USA
Constraining the initial state through many-body observables
Talk at Initial Stages 2021, Rehovot, Israel (Virtual)
Constraining the initial state through many-body observables
Talk at April Meeting 2021, Virtual
Quantification of the Quark-Gluon Plasma with statistical learning
Talk at APS DNP Fall Meeting 2023, Waikoloa, Hawaii
Teaching
Service and leadership
- Organizing Committee Member, McGill Physics Hackathon (2018-2022)
- VP Communications, McGill Graduate Association of Physics Students (2017-2019)