Matthew Schlegel
Postdoctoral Research Associate
Canada
Summary
👤
Senior🎓
Top SchoolMatthew Schlegel is a Postdoctoral Research Associate and applied ML researcher with a decade of experience bridging reinforcement learning and real-world systems, currently focused on power systems control, forecasting, and simulation at the University of Calgary. He holds a PhD from the University of Alberta and has worked at the intersection of ML and games as an Applied AI Research Scientist at modl.ai, bringing practical RL experience to safety-critical infrastructure. Matthew has contributed to the Flux.jl community—enhancing RNN support, GRU implementations, CUDA tests, and performance benchmarks—demonstrating both library-level engineering and research rigor. His background includes industry research internships (Borealis AI, Huawei) and extensive teaching, giving him a strong mix of mentorship, reproducible research, and production-oriented ML engineering. Notably, he combines deep academic RL expertise with hands-on contributions to open-source ML tooling, making him adept at moving algorithms from prototype to reliable deployment.
10 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of AlbertaMaster's degree, Computer Science, Year 1, Master's degree, Computer Science, Year 1 at Indiana University Bloomington
Github Skills (14)
neural-network10
machine-learning10
rnn-model10
fluxor10
gru10
n10
artificial-neural-networks10
flux10
deeplearning-ai9
deep-learning9
cuda9
julia8
data-science7
testing7
Programming languages (8)
Github contributions (5)
FluxML/Flux.jl
Jul 2021 - Mar 2022
Relax! Flux is the ML library that doesn't make you tensor
Role in this project:
ML Engineer
ML EngineerContributions:30 reviews, 32 commits, 6 PRs in 8 months
Contributions summary:Matthew primarily focused on implementing and testing recurrent neural network (RNN) layers within the Flux.jl machine learning library. Their contributions included adding support for GRUv3 cells and a FoldedRNN structure, improving documentation, and creating CUDA tests. Furthermore, they refactored the RNN implementation using a stack approach and added performance benchmarks.
machine-learningtensorfluxneural-networksdeep-learning
mkschleg/RLTutorial.jl
May 2020 - Jun 2021
Contributions:12 commits, 10 pushes, 2 branches in 1 year 1 month
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