Angel López-urrutia
Senior Research Scientist at Instituto Español de Oceanografía
Gijón, Asturias, Spain
Summary
👤
Senior🎓
Top SchoolAngel López-urrutia is a Senior Research Scientist and marine ecologist with 11+ years at the Spanish Institute of Oceanography, combining deep domain expertise in plankton ecology with strong data analysis and mathematical modelling skills. He bridges laboratory studies of plankton physiology and long-term pelagic monitoring with hands-on development of imaging systems and deep learning pipelines for automatic plankton classification. His work applies macroecological theory and data mining to link cellular processes to community and biogeochemical dynamics, enabling scalable ecological inference. An active contributor to ML tooling for image classification within projects like Apache MXNet, he leverages open-source practices to make scientific workflows more automated and reproducible. Based in Gijón, Spain, he is notable for translating ecological theory into practical software that frees researchers from repetitive tasks and accelerates discovery.
11 years of coding experience
4 years of employment as a software developer
Biology, Biology, Biology, Biology at Universidad de OviedoDoctor of Philosophy (PhD), Ecology, cum laude, Doctor of Philosophy (PhD), Ecology, cum laude at University of OviedoBachelor's degree, Marine Biology and Biological Oceanography, Bachelor's degree, Marine Biology and Biological Oceanography at Swansea University
Github Skills (7)
mxnet10
machine-learning10
deep-learning10
python10
image-classification10
pandas9
kaggle9
Programming languages (4)
Github contributions (5)
apache/mxnet
Jan 2016 - Mar 2016
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 10 PRs, 33 comments in 1 month
Contributions summary:Angel contributed to the development of machine learning models within the MXNet framework for a deep learning project related to image classification. They implemented a prediction and submission generation script (submission_dsb.py) to format results for a competition. They also made minor adjustments to the training process to align with the image-classification examples. Furthermore, the user worked on generating image list files.
pythonschedulerdataflowmutationdata-science
Lopezurrutia/woa
Oct 2019 - Feb 2020
Contributions:17 commits, 2 pushes in 4 months
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