Julius Marozas
Ph.D. Student at Chalmers University of Technology
Gothenburg, Sweden
Summary
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Rockstar🎓
Top SchoolJulius Marozas is a PhD student at Chalmers University of Technology with eight years of hands-on experience bridging programming language research, type theory, and practical engineering. His work focuses on language-based security for GDPR, and he brings deep expertise in functional and dependently typed languages (Haskell, Agda) as well as experience with Rust and PyTorch. Julius has contributed to Hasktorch—implementing gradual, type-safe tensor APIs and improving doctests—demonstrating an unusual blend of advanced type-level programming and applied ML tooling. He has a track record across research and product contexts, from optimizing Agda’s type-checker to building full-stack and Android applications. Based in Gothenburg, he combines formal rigor from his MSc and BSc background with a pragmatic knack for shipping robust tools and prototypes.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Science - BS, Mathematics and Computer Science, First class, Bachelor of Science - BS, Mathematics and Computer Science, First class at The University of ManchesterHigh School Diploma, Computer Science, High School Diploma, Computer Science at Kaunas University of Technology GymnasiumMSc Computer science – algorithms, languages and logic, Computer Science, MSc Computer science – algorithms, languages and logic, Computer Science at Chalmers University of Technology
English, Lithuanian, Swedish
Stackoverflow
1reputation
0reached
0answers
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Github Skills (9)
tensorrt10
machine-learning10
tensor10
tensorflow10
lib10
operation10
haskell10
doctest9
cabal8
Programming languages (23)
Github contributions (5)
hasktorch/hasktorch
Apr 2021 - Aug 2021
Tensors and neural networks in Haskell
Role in this project:
Backend Developer
Backend DeveloperContributions:9 reviews, 75 commits, 10 PRs in 4 months
Contributions summary:Julius primarily contributed to the development of the experimental `Torch.GraduallyTyped` module, which introduces tensors and neural networks in Haskell. They refactored and updated the source code, including code changes to various files, such as cabal files, to align the module with best practices. Additionally, the user worked on enhancing and fixing doctests within the module. The changes reflect an effort to maintain and improve the Haskell library.
haskellneural-networksmachine-learningneural-networktensors
jul1u5/dotfiles
Aug 2019 - Jan 2024
dotfiles for NixOS and ArchLinux
Contributions:4 PRs, 35 pushes, 4 branches in 4 years 5 months
dotfilesnixosnixos-configurationarchlinux
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