Adam Ścibior
Vancouver, British Columbia, Canada
Summary
🤩
Rockstar🎓
Top SchoolAdam Ścibior is a machine learning researcher and engineer with 11 years’ experience building probabilistic and generative models for autonomous vehicle motion prediction. As Co-Founder and CTO of Inverted AI and an adjunct professor at UBC, he bridges cutting-edge research and production systems to help self-driving cars reason about human behavior. His PhD work formalized the semantics of approximate inference and compositional implementations, giving him deep expertise in probabilistic programming—reflected in contributions to the well-known Turing.jl Bayesian inference ecosystem. Comfortable in both academia and startup execution, he combines rigorous theory with practical benchmarking and visualization of inference algorithms. Based in Vancouver, he is equally fluent in physics and machine learning, a background that proves useful when modeling complex, real-world dynamics.
10 years of coding experience
Doctor of Philosophy (Ph.D.), Machine Learning, Doctor of Philosophy (Ph.D.), Machine Learning at University of CambridgeMaster’s Degree, Physics, Bardzo dobry (Very good), Master’s Degree, Physics, Bardzo dobry (Very good) at Uniwersytet im. Adama Mickiewicza w PoznaniuDoctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at Max Planck Institute for Intelligent Systems
Github Skills (9)
bayesian10
probabilistic-programming10
julialang10
bayesian-inference10
julia10
mcmc9
artificial-intelligence8
machine-learning8
bayesian-statistics8
Programming languages (6)
Github contributions (5)
TuringLang/Turing.jl
Apr 2016 - May 2016
Bayesian inference with probabilistic programming.
Role in this project:
Data Scientist
Data ScientistContributions:26 commits, 3 PRs, 12 pushes in 18 days
Contributions summary:Adam contributed to the development and evaluation of Bayesian inference models within the Turing.jl framework. Their primary contributions include the addition of examples demonstrating Bayesian inference, such as a Gaussian model, branching model, and a hidden Markov model. They also created scripts for benchmarking different inference algorithms and generating visualizations of the results.
julia-languagebayesian-inferencebayesian-methodsbayesian-neural-networkshamiltonian-monte-carlo
inverted-ai/torchdriveenv
Apr 2024 - Feb 2025
TorchDriveEnv is a lightweight 2D driving reinforcement learning environment, supported by a solid simulator and smart non-playable characters
Contributions:7 releases, 11 reviews, 15 PRs in 10 months
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