Before transitioning to industry, I was a physicist - if you’re looking for some deep dive topics, here you go.
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.
arXiv preprint 2306.09619 (2023).
arXiv
In ultrarelativistic heavy-ion collisions, a plasma of deconfined quarks and gluons is formed within 1 fm/c of the nuclei’s impact. We perform a systematic analysis of LHC measurements from Pb-Pb collisions by combining an ab-initio model of the early stage of the collisions with a hydrodynamic model of the plasma. We obtain state-of-the-art constraints on the shear and bulk viscosity of quark-gluon plasma, combining Bayesian model averaging with transfer learning. We show that metrics balancing predictivity with data agreement do not prefer a temperature-dependent specific shear viscosity over a constant value.
Bayesian quantification of strongly-interacting matter with color glass condensate initial conditions
M. Heffernan, C. Gale, S. Jeon, and J.-F. Paquet.
Phys. Rev. C 109, 024912 (2024). arXiv
A global Bayesian analysis of relativistic Pb + Pb collisions at 2.76 TeV using a multistage model with an IP-Glasma initial state, viscous fluid dynamics, and hadronic transport. The first use of inference with transfer learning in heavy-ion analyses, together with Bayes Model Averaging.
How about that Bayes: Bayesian techniques and the simple pendulum
M. Heffernan.
arXiv preprint 2104.08621 (2021). arXiv
Demonstrates how Bayesian techniques can be implemented to give data-driven guidance in teaching laboratories, using the simple pendulum as a case study.
Hadronic transport coefficients from the linear sigma model at finite temperature
M. Heffernan, S. Jeon, and C. Gale.
Phys. Rev. C 102, 034906 (2020). INSPIRE
We develop frameworks and calculate transport coefficients in the linear sigma model at finite temperature, using multiple frameworks to isolate the impact of approximations on results.
Quantifying the sensitivity of Big Bang Nucleosynthesis to isospin breaking with input from lattice QCD
M. Heffernan, P. Banerjee, and A. Walker-Loud.
arXiv preprint 1706.04991 (2017). arXiv
The first quantitative study of the sensitivity of Big Bang Nucleosynthesis to variations in isospin breaking with input from lattice QCD calculations.
Universal parametrization of thermal photon rates in hadronic matter
M. Heffernan, P. Hohler, and R. Rapp.
Phys. Rev. C 91, 027902 (2015). APS
A parametrization of photon emission rates from hadronic matter, including in-medium rho mesons and bremsstrahlung from pion-pion scattering.
Quantification of the Quark-Gluon Plasma with statistical learning
APS DNP Fall Meeting 2023 — Waikoloa, Hawaii
Invited talk for the 2024 APS Dissertation Award in Nuclear Physics.
APS Bulletin
Constraining the initial state through many-body observables
APS April Meeting 2021 — Virtual
APS Bulletin
Constraining the initial state through many-body observables
Initial Stages 2021 — Rehovot, Israel (Virtual)
Indico
Transport coefficients in the linear sigma model at finite temperature
Duke University QCD Group Seminar — Durham, NC (2020)
Recording
Comparative approaches to calculating transport coefficients in massive theories demonstrated in the Linear Sigma model
APS DNP Fall Meeting 2019 — Washington, D.C.
APS Bulletin
Bayesian inference in hybrid models of Heavy Ion Collisions
Developed the nascent field of Bayesian analysis in high-energy nuclear theory, in collaboration with the JETSCAPE Collaboration.
Applying statistical methods to teaching problems
Using Bayesian inference and model selection to give quantitative guidance in undergraduate laboratories. Demonstrated when commonly-made approximations fail using the simple pendulum as a case study.
Hadronic transport coefficients from the linear sigma model at finite temperature
Extended existing frameworks for calculating transport coefficients in high temperature gauge theories for progressively more realistic quasiparticle gases.
Quantifying the sensitivity of Big Bang Nucleosynthesis to isospin breaking
Tested for beyond-Standard Model physics by varying fundamental constants. Constraints demonstrated that isospin symmetry violation produces conditions inconsistent with low-metallicity gas cloud data.
Universal Parametrization of Thermal Photon Production in Hadronic Matter
Expanded existing parametrization procedures to improve accuracy, including nonzero baryochemical potential for the first time.
Teaching Assistant — Physics 101, McGill University (Fall 2020)
Redesigned laboratories for COVID-19, led development of a rolling without slipping lab adaptable to student situations worldwide.
Teaching Assistant — Physics 102, McGill University (Spring 2021)
Managed online learning in a flipped classroom environment during the pandemic.
Teaching Assistant — Physics 102, McGill University (Fall 2019)
Completed teaching training (EdX certificate from EPFL), prepared for tutorial sessions of ~100 students.
STEM Graduate Teaching Development Fellow — Physics 102, McGill University (Spring 2019)
Mentored students, delivered a lecture to McGill’s largest lecture theatre, produced YouTube walkthroughs.
STEM Graduate Teaching Development Fellow — Physics 102, McGill University (Fall 2018)
Wrote a semester of questions for flipping McGill’s 700-student introductory electromagnetism course.
Teaching Assistant — Physics 102, McGill University (Spring 2018)
Marked homework and led tutorial sessions for groups of 20-30 undergraduates.
Teaching Assistant — Physics 203, McGill University (Fall 2017)
Marked homework and led tutorials for “Dynamics of Simple Systems.”
Teaching Assistant — Physics 102, McGill University (Spring 2017)
Graded midterm and final exams using Crowdmark.
Teaching Assistant — Physics 101, McGill University (Fall 2016)
Accurate grading of laboratory materials and handling student questions.
Data Scientist, Perception V&V @ Zoox