Arthur Porto
Curator Of Artificial Intelligence
Gainesville, Florida, United States
Summary
👤
Senior🎓
Top SchoolArthur Porto is a curator of artificial intelligence and researcher with nine years of interdisciplinary experience at the intersection of machine learning, computer vision, and quantitative biology. He combines a PhD in ecology and evolution with hands-on work in genetics and big data, translating complex biological problems into scalable computational solutions. His academic trajectory spans postdoctoral research on computer vision for fossil images to assistant professorships and current AI curation at the University of Florida, reflecting both research depth and leadership. An active open-source contributor, he implemented constrained deformable registration and low-rank Coherent Point Drift improvements in a widely used pycpd repository, demonstrating practical algorithmic rigor. Based in Gainesville, FL, he brings a rare blend of evolutionary biology insight and machine learning engineering that enables novel approaches to biological image analysis and quantitative research.
10 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Ecology, Evolution, Systematics, and Population Biology, Doctor of Philosophy - PhD, Ecology, Evolution, Systematics, and Population Biology at Washington University in St. LouisUniversity of São Paulo
English, Portuguese
Github Skills (10)
point-cloud10
python10
numpy10
data-structure9
machine-learning9
algorithm9
data-structures9
algorithms9
computer-vision8
pytest8
Programming languages (9)
Github contributions (5)
siavashk/pycpd
May 2021 - Aug 2022
Pure Numpy Implementation of the Coherent Point Drift Algorithm
Role in this project:
Data Scientist
Data ScientistContributions:10 commits, 1 PR, 13 comments in 1 year 2 months
Contributions summary:Arthur primarily contributed to the `pycpd` repository by implementing and refining the Constrained Deformable Registration algorithm. Their work involved adding new functionalities such as constrained deformable registration, low-rank implementations, and examples. Furthermore, the user corrected syntax errors, refined the documentation, and added testing to validate their changes. The contributions demonstrate a strong understanding of the Coherent Point Drift algorithm and its practical application.
numpypoint-cloudregistrationexpectation-maximizationpython
agporto/ml-morph
Jul 2019 - Aug 2020
Machine-learning-based morphometrics
Contributions:1 release, 34 commits, 33 pushes in 1 year 1 month
machine-learning
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