About
Things that get me out of bed in the morning: Solving problems with math, science, and coding; Collaborating towards meaningful, impacting projects; Coffee.
Academically, my latest vocation was a MSc. Physics at the University of Waterloo, where
my research applied Supervised (FNN, RNN) and Unsupervised (PCA) machine learning methods to simulated liquid crystal systems for defect detection.
Before that, was Hamilton, Ontario’s McMaster University.
Currently, my focus is now solely on AI safety efforts. I have joined a couple such projects with the Supervised Program
for Alignment Research (SPAR), described below. Though I find many topics in the space engaging and important, at the moment
I am more concerned with near(er) term harms vs. catastrophic x-risk scenarios (though I don’t rate such events as safely improbable).
In addition to SPAR, I am an active participant and volunteer in the AI Governance & Safety Canada (AIGS) group, looking to ensure
positive outcomes for AI in, and from, Canada.
In past work, I was as Senior Backend Developer at QED, developing genericized platform tools and architecture
for custom instantiation in business and data management contexts. Since 2022
QED has been creating custom software for New Zealand’s
“largest transport infrastructure project ever”.
In other work placements, I joined the Hyper-K
neutrino detecting efforts—the successor to the 2015 Nobel Prize winner
Super-K—at Canada’s premier accelerator lab,
TRIUMF; researched galactic cloud astrophysics via simulation at McMaster University with James Wadsley’s group;
and worked on an anomaly detection ML model at Canada’s Communications Security Establishment. I am also an extended volunteer for Climate Change AI.
Outside academic and professional pursuits, I love hosting and making food for friends and family; taking my Olympus OM-1 outdoors for some
film shooting (though my bank account would prefer I don’t); Volunteering at The Local Community Food Centre;
and masochistically running Linux on my PC.
I also write weekly on AI/AI Safety papers on my substack, Weekly Aligned.
My strongest software skills are: Python, Django, Tensorflow, Bash, \(\LaTeX\), Git, C++, MATLAB, R
Frequently used Python libraries: numpy, pandas, tensorflow, matplotlib, scikit-learn
I also have experience in: HTML, Vue, SQL, Postgres, Geant4, ROOT
Education
University of Waterloo
MSc. Physics
2017 - 2019
Thesis-based Master’s under Prof. Jeff Chen researching the use and efficacy of neural networks in
learning phases of matter and topological defects, using a simulated liquid crystal system
as a testbed. Findings included the novel use of Recurrent Neural Networks to learn topological features in an off-lattice setting.
Additional thesis research was done using Principle Component Analysis on simulated localized defects to then identify their presence in
large-scale liquid crystal arrangements.
Coursework covered quantum mechanics, quantum many-body systems, Monte Carlo methods, and stochastic processes.
2017 Provost Graduate Scholarship recipient
McMaster University
BSc. Hons. Physics
2012 - 2017
Bachelor’s degree covering essential physics topics. With my co-ops and electives I further focused my education and experience on computational/numerical methods, quantum mechanics, quantum computing, and mathematical physics.
Coursera
Machine Learning Course
Sep - Nov, 2016
An introductory course offered by Stanford online through Coursera. Covered machine learning fundamentals, common architectures, and techniques. Link to certificate
Experience
SPAR
AI Safety Researcher
Feb. 2024 - Present
The Supervised Program for Alignment Research (SPAR) program out of Berkeley brings project leads and new researchers together to tackle questions at the forefront of AI safety and alignment. I have joined two such projects.
With Kellin Pelrine, our group is looking into applying hybrid neural network architectures, including LLMs and anomaly detection, to combat malicious coordinated group activities on social media.
With Rafael Kaufmann and Roman Leventov, our group is looking at how systems referencing a shared causal world-model can help us reason about, quantify and improve the safety of AI systems in their interactions with real-world (physical and social) environments.
QED
Senior Backend Developer
Feb. 2020 - Aug. 2023
The small team size and startup nature of the company required learning and engaging with the numerous technological layers of the product, from direct database tinkering to frontend UX, and all the surrounding programs/utilities. This demands both breadth and depth of development to get the job done.
More technically, through Django and Postgres I had a significant hand in constructing a suite of genericized models, tools, and architecture that could be instantiated and characterized swiftly yet specifically via YAML files for a given product context. The result allows sophisticated customization of not only model properties but interactions across objects on a per customer basis. This also included building out the REST API, automated testing suites for all corners of the codebase with GitHub Action workflows, and documentation. I also built many other in-house, ad-hoc data management features and reports to grant customers insights into their data.
I worked to both design and code numerous aspects of the platform backend through its iterative growth over the past three years. Other undertakings of note would include: managing a collaboration between a contracted third party to design a locomotive worksite scheduling optimizer using machine learning; Prototyping an Internet-of-Things plug-in to our software; Formulating a multivariate mathematical model for worksite Task Success/Health prediction.
CSEC
Software Developer
Sep, 2016 - Jan, 2017
At the Communications Security Establishment of Canada I programmed a hybrid neural net for anomaly detection. The language was primarily Python, using TensorFlow with a Postgres database.
McMaster University
Astrophysics Researcher
May - Sep, 2016
I simulated galactic disk portions using parallel SPH tree-code Gasoline to investigate behavioural dependencies on certain factors, primarily resolution. I prepared various initial conditions for these runs, with subsequent analysis in Python. The HPC cluster SHARCNET was used for these runs.
TRIUMF
Hyper-Kamiokande R&D
Jan - Sep, 2015
As part of the T2K Canada group, I worked on the successor to the 2015 Nobel Prize-winning neutrino oscillation experiment Super-Kamiokande. My primary work was analyzing the timing resolution of three different flash analog-to-digital signal converter modules. This included acquiring and processing signal data, for which I used the CERN analysis toolkit ROOT. I also simulated neutrino oscillation detection events in the Water-Cherenkov detector, WCSim, using Geant4.
Projects & Publications
- Writing weekly on AI/AI Safety papers on my substack, Weekly Aligned.
- Volunteering with AIGS: Contributions to the weekly Toronto AI Safety Meetup, and various website improvement efforts.
- RiskRay. Using ML to detect hips at risk of fracture. Patent pending.
- Traffic prediction Graph Neural Net. View the proof-of-concept toy model.
- My MSc. thesis: Machine learning topological defects of liquid crystals in two dimensions. Available online on UWSpace (2019).
- M. Walters, Q. Wei, J. Z. Y. Chen. Machine learning topological defects of confined liquid crystals in two dimensions. (Phys. Rev. E 99, 062701 / Read PDF) (2019).
- M. B. Bennett et al. Detailed study of the decay \(^{31}Cl(\beta\gamma)^{31}S\). Physical Review C 97 (6), 065803 (2018).
- E. Aboud et al. Toward complete spectroscopy using \(\beta\) decay: The example of \(^{32}Cl(\beta\gamma)^{32}S\). Physical Review C 98 (2), 024309 (2018).
- [Editor’s suggestion] M. B. Bennett et al. Isospin mixing reveals \(^{30}P(p,\gamma)^{31}S\) resonance influencing nova nucleosynthesis. Phys. Rev. Lett. 116, 102502, (2016).
- M. B. Bennett et al. Isobaric multiplet mass equation in the \(A = 31, T = 3/2\) quartets. Physical Review C 93 (6), 064310 (2016).
- C. Wrede et al. \(\beta\) Decay as a Probe of Explosive Nucleosynthesis in Classical Novae. Physics Procedia 66, 532-536 (2015).
