Bruno Vieira
Postdoctoral Researcher at University of Zurich
Zurich, Zurich, Switzerland
Summary
👤
Senior🎓
Top SchoolBruno Vieira is a postdoctoral researcher at the University of Zurich with a decade of experience at the intersection of physics, medical imaging and machine learning. He holds a PhD in Physics Applied to Medicine and Biology and a BSc in Medical Physics from the University of São Paulo, and has blended academic research with hands-on ML engineering contributions to well-known open-source projects like Flux.jl and mlr. His work spans developing neural network layers and rigorous evaluation metrics, reflecting a knack for both low-level algorithmic detail and reproducible scientific software. Having collaborated internationally as a visiting scholar and long-term member of InBrain Lab, he brings experience translating complex biophysical problems into deployable computational tools. A less obvious strength is his fluency across Julia, R and scientific workflows, enabling him to bridge research prototypes and production-ready analysis.
10 years of coding experience
7 years of employment as a software developer
University of São Paulo
Portuguese, English, Spanish
Stackoverflow
3,800reputation
476kreached
80answers
7questions
r
top-5%
Github Skills (25)
data-science10
classification10
r10
testing10
machine-learning10
regression10
deep-learning10
neural-network10
clustering10
julia10
learnr9
r-package9
learing9
learn2learn9
statistics9
Programming languages (11)
Github contributions (5)
FluxML/Flux.jl
Oct 2018 - Feb 2021
Relax! Flux is the ML library that doesn't make you tensor
Role in this project:
ML Engineer
ML EngineerContributions:33 commits, 7 PRs, 227 comments in 2 years 4 months
Contributions summary:Bruno primarily focused on enhancing the `flux.jl` library, a machine learning library in Julia. Their contributions include implementing the `SkipConnection` layer, adding tests for various layers, and fixing issues related to the `Bilinear` layer. They also removed deprecated code and optimized the code.
ml-librarythe-human-braindata-sciencedeep-learningneural-networks
mlr-org/mlr
Jun 2016 - Mar 2017
Machine Learning in R
Role in this project:Data Scientist
Data ScientistContributions:8 commits, 8 PRs, 106 comments in 8 months
Contributions summary:Bruno contributed significantly to the `mlr-org/mlr` repository, a machine learning framework in R. Their work focused on enhancing the library's capabilities related to model evaluation and the addition of new methods. This included implementing new measures like logloss, spherical scoring rule (ssr), quadratic scoring rule (qsr), and relative error metrics (rrse, rae, mape), and updating and testing existing ones. Furthermore, the user added new clustering methods, such as kernlab::kkmeans, and implemented several tests to ensure the accuracy and reliability of the new measures and methods.
imbalance-correctionlearnersensemble-learningclassificationr-package
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