Leonard Papenmeier
Data ScientistPostDoc at University of Münster
Münster
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
PostDoc at University of Münster
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Münster
Data Scientist
June 25, 2026 – Present
understanding-hdbo-code
May 27, 2026 – Present
understanding-hdbo-code — GitHub repository
View Projectexploring-exploration-public
June 10, 2025 – June 10, 2025
exploring-exploration-public — GitHub repository
View Projectbencher
February 13, 2024 – Present
Bencher makes evaluating black-box problems simple. See https://arxiv.org/abs/2505.21321
View Projectbounce
September 15, 2023 – April 30, 2024
Code for the paper "Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces"
View Projectvariational-entropy-search
November 15, 2022 – May 23, 2025
variational-entropy-search — GitHub repository
View ProjectBAxUS
May 25, 2022 – January 5, 2024
This is the code for our paper: Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces (Leonard Papenmeier, Luigi Nardi, and Matthias Poloczek)
View ProjectEDAN95-Assignment-2
November 4, 2021 – November 7, 2022
Second assignment for the EDAN95 - Applied Machine Learning course at LTH
View ProjectLassoBench
June 21, 2021 – Present
Benchmark library for high-dimensional HPO of black-box models based on Weighted Lasso regression
View Projecthypermapper
December 20, 2019 – April 15, 2024
Black-box Optimizer based on Bayesian Optimization
View ProjectCultural Fit Analysis
The candidate's projects are heavily focused on research-oriented machine learning and optimization, which aligns well with a data scientist role requiring strong analytical and problem-solving skills. However, the projects are predominantly personal, and there is only one current professional experience listed with no details, making it difficult to assess team collaboration or broader industry experience. The diversity of technologies is good, but the project types are very similar (research/optimization).
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The psychometric test score is 0, providing no insights.