Leonard Papenmeier
Assistant Professor at Universität Münster
Gronau, North Rhine-Westphalia, Germany
Summary
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Senior🎓
Top SchoolLeonard Papenmeier is an Assistant Professor and researcher with a decade of experience building and applying machine learning systems, currently transitioning from a PhD at WASP to a faculty role at Universität Münster. His work specializes in Bayesian optimization for expensive black-box problems, with applied impact across neural architecture search, robotics, and life sciences and a NeurIPS paper on adaptive, robust high-dimensional BO. He combines strong software engineering chops from roles at img.ly and adesso with practical deep learning deployment experience in Python, TensorFlow and PyTorch, and has recently expanded industry-facing research through an internship at Bosch. Leonard’s profile blends rigorous academic training (PhD at Lund, MSc with 95% at Ruhr-Universität Bochum) with hands-on product engineering, making him adept at translating novel algorithms into production-ready systems. An often overlooked asset is his cross-disciplinary background—exchange study in data science and early full-stack engineering—that helps him bridge research, engineering and real-world constraints.
10 years of coding experience
10 years of employment as a software developer
B.Sc., Software Engineering, B.Sc., Software Engineering at Fachhochschule DortmundDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at Lund UniversityM.Sc., Applied Computer Science, 95%, M.Sc., Applied Computer Science, 95% at Ruhr-Universität BochumExchange semester, Data Science, Exchange semester, Data Science at Norges miljø- og biovitenskapelige universitet (NMBU)
Github Skills (122)
python10
optimization10
r10
big-data10
scale10
sql10
data-processing10
spark10
apache10
analytics10
java10
large-scale10
scala10
bayesian10
jdbc10
Programming languages (9)
Github contributions (5)
LeoIV/botorch
Sep 2022 - Jan 2024
Bayesian optimization in PyTorch
Contributions:18 pushes in 1 year 3 months
pytorchoptimizationmachine-learningbayesian-optimizationbayesian
LeoIV/BAxUS
Sep 2022 - Nov 2022
Contributions:5 releases, 21 commits, 2 PRs in 1 month
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