José Licón-saláiz
Data Scientist
Berlin, Germany
Summary
👤
Senior🎓
Top SchoolJosé Licón-saláiz is a data scientist and applied mathematician with over a decade of professional experience and 4+ years focused on industry-grade data science and statistical modelling. He holds a Dr. rer. nat. in Applied Mathematics and has translated novel topological data analysis methods from his doctoral work into practical solutions for atmospheric science. José has worked across academia and industry—from postdoctoral research at the University of Hamburg to quantitative and data science roles in the private sector—and now applies his expertise at PIK in Berlin. He contributes to open-source Bayesian optimization tooling (RoBO), emphasizing robust core functionality and testing, which reflects his attention to code quality and algorithmic detail. Colleagues know him for bridging rigorous mathematical research with production-ready engineering to tackle complex environmental and quantitative problems.
11 years of coding experience
11 years of employment as a software developer
Dr. rer. nat., Applied Mathematics, Dr. rer. nat., Applied Mathematics at University of CologneMaster's Degree, Computer Science, Master's Degree, Computer Science at Albert-Ludwigs-Universität Freiburg im BreisgauBachelor's Degree, Applied Mathematics, Bachelor's Degree, Applied Mathematics at Instituto Tecnológico Autónomo de México
Spanish, English, German, French
Github Skills (10)
algorithm10
algorithms10
machine-learning10
bayesian10
python10
optimisation10
optimization10
testing9
gpy9
matlab7
Programming languages (2)
Github contributions (5)
automl/RoBO
Nov 2014 - Mar 2015
RoBO: a Robust Bayesian Optimization framework
Role in this project:
Back-end Developer
Back-end DeveloperContributions:32 commits in 4 months
Contributions summary:José contributed to the development of the robo framework, adding a test directory and initial test code, indicating a focus on testing and code quality. Further contributions include merging branches and modifying code related to minimization algorithms, including modifications in the `minimize.m` file. Additional work was done on acquisition functions. The user seems to be focused on the core functionality of the framework.
robooptimization-frameworkoptimizationmachine-learningbayesian-optimization
antworteffekt/EDeN
Mar 2015 - Jan 2016
Contributions:61 pushes in 10 months
decompositionpythonneighborhoodspython-version
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