Factorization of Power Corrections in the Drell-Yan Process in EFT
M. Inglis-Whalen, M. Luke, J. Roy and A. Spourdalakis, Factorization of Power Corrections in the Drell-Yan Process in EFT, Phys. Rev. D 104 (2021) 076018, [2105.09277]
CV
Education
- Ph.D. in Particle Physics, University of Toronto, 2022
- M.Sc. in Physics, with Distinction, University of Edinburgh, 2014
- B.Sc. in Physics, Highest Honours, with Minor in Mathematics, Carleton University, 2013
Skills
- Programming: Python, SQL, R, Mathematica, C++, C#, C, Bash
- Data Science: scikit-learn, PyTorch, Pandas, Seaborn, Tableau
- Dev Tools: Flask, Docker, AWS Cloud Servers, Linux, GitHub, Unity, Excel
- Mathematics: Numerical Optimization, Multivariable Calculus, Statistics, Linear Algebra
- Physics: Classical Mechanics, Quantum Field Theory, Nuclear Models, Relativity
Projects
Freelance Consulting (Kolabtree)
| Matlab | Python |
- Worked on fitting hydrological models to a measured time-series of borehole head heights
- Translated academic MATLAB code to Python, with refactoring for object-oriented styling
- Employed industry-standard best-practices for automated testing/linting/styling with GitHub
Thread the Void VR
| Unity | C# | Steamworks |
- Built a video game using the Unity Engine for headsets compatible with SteamVR (Vive/Oculus)
- Created a framework for procedurally generating music without using audio samples
- Published the game on Steam, with beta versions currently being tested on Android
Identifying Clusters in 2D Data
| R | GitHub |
- Developed a novel algorithm for efficiently detecting clusters in a 2D point-spread dataset in R
- Packaged and published the algorithm for use with R’s devtools/remotes install_github functionality
Cloud Deployment of Inference Model
| Python | Flask | Docker | Scikit-learn | AWS |
- Used Natural Language Processing techniques to predict the sentiment of a film review
- Deployed a containerized model to a scalable AWS-EC2 endpoint that was over 90% accurate while saving 50% of server costs compared to the standard solution.
- Generated HTML programmatically to visualize word importance in the classification
Predictive Modelling for Truancy
| Jupyter | Scikit-learn | Seaborn | Pandas |
- Data cleaning, exploratory data analysis, and predictive modelling in a Jupyter notebook
- Visualized features and their relationship with truancy rates
- Used a cross-validation grid search to tune the hyperparameters of many models
MIW’S AutoFit
| Python | Tkinter | Multiplatform | SQL | PHP |
- Developed a GUI using Python (NumPy, SciPy, Pandas) to rank and fit models to 1D data
- Configured GitHub Actions for automated testing and code-coverage reports
- Packaged the program for distribution to Windows, MacOSX, and Linux environments
- Built a WordPress website with PHP and SQL backend support to sell software products
Publications
Rapidity Logarithms in SCET Without Modes
M. Inglis-Whalen, M. Luke and A. Spourdalakis, Rapidity logarithms in SCET without modes, Nucl. Phys. A 1014 (2021) 122260, [2005.13063]
Renormalization of dijet operators at order 1/Q² in soft-collinear effective theory
R. Goerke and M. Inglis-Whalen, Renormalization of dijet operators at order 1/Q² in soft-collinear effective theory, JHEP 05 (2018) 023, [1711.09147]
Model-based dose calculations for COMS eye plaque brachytherapy using an anatomically realistic eye phantom
M. Lesperance, M. Inglis-Whalen, and R. M. Thomson, Model-based dose calculations for COMS eye plaque brachytherapy using an anatomically realistic eye phantom , Med. Phys. 41(2) (2013) 021717
Talks
Power Corrections and Rapidity Logarithms in Soft-Collinear Effective Theory
Talk at Online, Giessen, Germany
Introduction to Soft-Collinear Effective Theory
Talk at Mini Pheno Workshop 2019, Toronto, Ontario
Rapidity Logs and Overlap Subtractions in SCET
Talk at SCET Workshop 2019, San Diego, California