Research Group Fabian Scheipl
Fabian Scheipl
PD Dr.
Principal Investigator
Fabian Scheipl
is Head of the Workgroup Functional Data Analysis at LMU Munich.
The group works on methodology and software implementations that process, describe, visualize and model functional data, such as curves, trajectories, or even higher dimensional surfaces. The research focuses on the analysis of functional data using generalized additive regression and on both supervised and unsupervised methods for functional data, for example for automated outlier detection or dimension reduction.
Recent News @MCML
Publications @MCML
2025
[20]
B. Pulido • A. M. Franco-Pereira • R. E. Lillo • F. Scheipl
Area-based epigraph and hypograph indices for functional outlier detection.
Preprint (Jul. 2025). arXiv
Area-based epigraph and hypograph indices for functional outlier detection.
Preprint (Jul. 2025). arXiv
2024
[19]
M. Herrmann • D. Kazempour • F. Scheipl • P. Kröger
Enhancing cluster analysis via topological manifold learning.
Data Mining and Knowledge Discovery 38. Apr. 2024. DOI
Enhancing cluster analysis via topological manifold learning.
Data Mining and Knowledge Discovery 38. Apr. 2024. DOI
2023
[18]
J. Gauss • F. Scheipl • M. Herrmann
DCSI–An improved measure of cluster separability based on separation and connectedness.
Preprint (Oct. 2023). arXiv
DCSI–An improved measure of cluster separability based on separation and connectedness.
Preprint (Oct. 2023). arXiv
[17]
S. Hoffmann • F. Scheipl • A.-L. Boulesteix
Reproduzierbare und replizierbare Forschung.
Moderne Verfahren der Angewandten Statistik. Sep. 2023. DOI
Reproduzierbare und replizierbare Forschung.
Moderne Verfahren der Angewandten Statistik. Sep. 2023. DOI
[16]
A. Volkmann • A. Stöcker • F. Scheipl • S. Greven
Multivariate Functional Additive Mixed Models.
Statistical Modelling 23.4. Aug. 2023. DOI
Multivariate Functional Additive Mixed Models.
Statistical Modelling 23.4. Aug. 2023. DOI
[15]
M. Herrmann • F. Pfisterer • F. Scheipl
A geometric framework for outlier detection in high-dimensional data.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery e1491. Apr. 2023. DOI
A geometric framework for outlier detection in high-dimensional data.
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery e1491. Apr. 2023. DOI
2022
[14]
L. Bothmann • S. Strickroth • G. Casalicchio • D. Rügamer • M. Lindauer • F. Scheipl • B. Bischl
Developing Open Source Educational Resources for Machine Learning and Data Science.
Teaching Machine Learning and Artificial Intelligence Workshop @ECML-PKDD 2022 - 3rd Teaching Machine Learning and Artificial Intelligence Workshop at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Grenoble, France, Sep 19-23, 2022. URL
Developing Open Source Educational Resources for Machine Learning and Data Science.
Teaching Machine Learning and Artificial Intelligence Workshop @ECML-PKDD 2022 - 3rd Teaching Machine Learning and Artificial Intelligence Workshop at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Grenoble, France, Sep 19-23, 2022. URL
[13]
J. Goldsmith • F. Scheipl
tf: S3 classes and methods for tidy functional data. R package.
2022. GitHub
tf: S3 classes and methods for tidy functional data. R package.
2022. GitHub
[12]
J. Goldsmith • F. Scheipl
tidyfun: Clean, wholesome, tidy fun with functional data in R. R package.
2022. GitHub
tidyfun: Clean, wholesome, tidy fun with functional data in R. R package.
2022. GitHub
[11]
W. Hartl • P. Kopper • A. Bender • F. Scheipl • A. G. Day • G. Elke • H. Küchenhoff
Protein intake and outcome of critically ill patients: analysis of a large international database using piece-wise exponential additive mixed models.
Critical Care 26.7. Jan. 2022. DOI
Protein intake and outcome of critically ill patients: analysis of a large international database using piece-wise exponential additive mixed models.
Critical Care 26.7. Jan. 2022. DOI
2021
[10]
M. Herrmann • F. Scheipl
A Geometric Perspective on Functional Outlier Detection.
Stats 4.4. Nov. 2021. DOI
A Geometric Perspective on Functional Outlier Detection.
Stats 4.4. Nov. 2021. DOI
[9]
A. Bauer • F. Scheipl • H. Küchenhoff
Registration for Incomplete Non-Gaussian Functional Data.
Preprint (Aug. 2021). arXiv
Registration for Incomplete Non-Gaussian Functional Data.
Preprint (Aug. 2021). arXiv
2020
[8]
M. Herrmann • F. Scheipl
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction.
Preprint (Dec. 2020). arXiv
Unsupervised Functional Data Analysis via Nonlinear Dimension Reduction.
Preprint (Dec. 2020). arXiv
[7]
A. Bender • D. Rügamer • F. Scheipl • B. Bischl
A General Machine Learning Framework for Survival Analysis.
ECML-PKDD 2020 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Virtual, Sep 14-18, 2020. DOI
A General Machine Learning Framework for Survival Analysis.
ECML-PKDD 2020 - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Virtual, Sep 14-18, 2020. DOI
[6]
F. Scheipl • J. Goldsmith • J. Wrobel
tidyfun: Tools for Tidy Functional Data. R package.
2020. URL GitHub
tidyfun: Tools for Tidy Functional Data. R package.
2020. URL GitHub
[5]
J. Wrobel • A. Bauer • J. McDonnel • F. Scheipl
registr: Curve Registration for Exponential Family Functional Data. R package.
2020. GitHub
registr: Curve Registration for Exponential Family Functional Data. R package.
2020. GitHub
2019
[4]
F. Pfisterer • L. Beggel • X. Sun • F. Scheipl • B. Bischl
Benchmarking time series classification -- Functional data vs machine learning approaches.
Preprint (Nov. 2019). arXiv
Benchmarking time series classification -- Functional data vs machine learning approaches.
Preprint (Nov. 2019). arXiv
[3]
C. Happ • F. Scheipl • A.-A. Gabriel • S. Greven
A general framework for multivariate functional principal component analysis of amplitude and phase variation.
Stat 8.2. Feb. 2019. DOI
A general framework for multivariate functional principal component analysis of amplitude and phase variation.
Stat 8.2. Feb. 2019. DOI
[2]
J. Goldsmith • F. Scheipl • L. Huang • J. Wrobel • C. Di • J. Gellar • J. Harezlak • M. W. McLean • B. Swihart • L. Xiao • C. Crainiceanu • P. T. Reiss
refund: Regression with Functional Data.
2019. URL
refund: Regression with Functional Data.
2019. URL
2018
[1]
J. Minkwitz • F. Scheipl • E. Binder • C. Sander • U. Hegerl • H. Himmerich
Generalised functional additive models for brain arousal state dynamics.
IPEG 2018 - 20th International Pharmaco-EEG Society for Preclinical and Clinical Electrophysiological Brain Research Meeting. Zurich, Switzerland, Nov 21-25, 2018. DOI
Generalised functional additive models for brain arousal state dynamics.
IPEG 2018 - 20th International Pharmaco-EEG Society for Preclinical and Clinical Electrophysiological Brain Research Meeting. Zurich, Switzerland, Nov 21-25, 2018. DOI
©all images: LMU | TUM