Employment
August 2021 - | SocïVolta | Research
My role at SocïVolta involves multiple tasks and research subjects, including:
- Strategy design (related blog post);
- Portfolio optimization and backtesting;
- Prediction of power arbitrage using time-series analysis and machine learning techniques;
- Short-term probabilistic forecasting using Monte-Carlo methods;
- High-performance, real-time, global optimization of resource allocations (related blog post).
December 2020 - April 2021 | Department of Chemistry, University of Toronto | Data Engineer
Design and implementation of an automated data processing pipeline to reconstruct femtosecond‐scale molecular dynamics from ultrafast electron diffraction measurements.
July 2015 - July 2017 | NASA Jet Propulsion Laboratory | Software Architect
Design and implementation of real-time holographic reconstruction pipeline for the Submersible Holographic Astrobiology Microscope with Ultraresolution (SHAMU) project, in collaboration with the California Institute of Technology and the University of Washington.
Education
2016-2021 | McGill University | Ph.D. Physics
Doctoral advisor: Bradley J. Siwick
Dissertation: From carrier cooling to polaron formation: ultrafast phonon dynamics across the Brillouin zone. All source material, including text files, figure scripts, and more, are available on GitHub.
See the research group website for more details.
2014-2016 | McGill University | M.Sc. Physics
Advisors: Bradley J. Siwick and Jay L. Nadeau.
My thesis was titled New Data Analysis and Visualization Methods for Ultrafast Electron Diffraction.
See the research group website for more details.
2011-2014 | McGill University | B.Sc. Joint Honours in Mathematics and Physics.
Advisor: Jack C. Sankey
My undergraduate thesis was titled High-Speed Information Processing for Laserlocking System Applied to Optomechanical Cavities. It had an associated poster.
Volunteering
August 2022 - | Haskell Foundation | Technical Working Group member
The Haskell Foundation Technical Working Group discusses community proposals based on their potential positive impact on the whole Haskell community.
While I’m interested in all technical aspects brought forth by the Haskell community, I’m keeping an eye on proposals related to my personal expertise: high-performance scientific computing, data science, and machine learning.