Giuseppe Casalicchio
Postdoctoral Researcher at Ludwig-Maximilians-Universität München
Greater Munich Metropolitan Area Germany
Summary
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Rockstar🎓
Top SchoolGiuseppe Casalicchio is a statistician-turned-data scientist with 11 years of experience, currently a postdoctoral researcher at Ludwig-Maximilians-Universität München and CEO of Essential Data Science Training GmbH. He holds a PhD in Statistics (magna cum laude) from LMU and blends rigorous academic research with practical machine-learning engineering. Giuseppe contributes to notable open-source work such as mlr-org/mlr, where he improved stacked learners by enabling original feature inclusion in super learners to boost flexibility and performance. He excels at translating statistical theory into reproducible, production-ready models and training programs, making complex methods accessible to practitioners. Based in the Greater Munich area, he combines scholarly depth with entrepreneurial drive and hands-on coding.
12 years of coding experience
Ludwig Maximilian University of MunichMaster of Science (MSc), Statistics, Master of Science (MSc), Statistics at Ludwig-Maximilians-Universität München
Github Skills (7)
machine-learning10
predictive-modeling10
mlr10
supervised-learning10
r10
hyperparameter-optimization8
feature-selection7
Programming languages (12)
Github contributions (5)
mlr-org/mlr
Aug 2014 - Mar 2018
Machine Learning in R
Role in this project:
Data Scientist
Data ScientistContributions:53 commits, 29 PRs, 109 pushes in 3 years 7 months
Contributions summary:Giuseppe's contributions focus on enhancing a stacked learner for machine learning in R, specifically within the context of the `mlr-org/mlr` repository. Their primary work involved adding an option to incorporate original features into the super learner model. This modification enables the user to make the model more flexible. These changes also include adjustments to improve model performance and code style.
machine-learningrdata-sciencetuningcran
slds-lmu/ame
May 2017 - Nov 2018
average marginal effects for machine learning
Contributions:22 commits, 1 PR, 3 pushes in 1 year 6 months
machine-learningmarginal-effects
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