Selected Publications
2022
L. Gaedke-Merzhäuser, J. Van Niekerk, O. Schenk, H. Rue
Parallelized integrated nested Laplace approximations for fast Bayesian inference
Statistics and Computing, December 2022, pages 1-20,
https://www.springer.com/journal/11222
, accepted, in press.
I. Horenko, E. Vecchi , J. Kardoš, O. Schenk, A. Waechter, T. O’Kane, P. Gagliardini, S. Gerber
On cheap entropy-sparsified regression learning
Proceedings of the National Academy of Sciences (PNAS), November 2022, pages 1-12,
https://www.pnas.org/, accepted, in press.
C. Alappat, G. Hager, O. Schenk and G. Wellein
Level-based Blocking for Sparse Matrices: Sparse Matrix-Power-Vector Multiplication
IEEE Transactions on Parallel and Distributed Systems, November 2022, pages 1-18,
https://doi.org/10.1109/TPDS.2022.3223512
J. Kardos, D. Kourounis, O. Schenk
BELTISTOS: A robust interior point method for large-scale optimal power flow problems
Electric Power Systems Research, July 2022, pages 1-17,
https://www.journals.elsevier.com/electric-power-systems-research
J. Kardos, T. Holt, V. Fazio, L. Fabietti, F. Spazzini, O. Schenk
Massively Parallel Data Analytics for Smart Grid Applications
Sustainable Energy, Grids and Networks, June 2022, pages 1-17,
doi.org/10.1016/j.segan.2022.100789
D. Pasadakis, M. Bollhoefer, O. Schenk
Sparse Quadratic Approximation for Graph Learning
TechRxiv. April 2022, pages 1-12.
doi.org/10.36227/techrxiv.19635990.v1
A. Eftekhari, L. Gaedke-Merzhäuser, D. Pasadakis, M. Bollhoefer, S. Scheidegger, O. Schenk
Large-Scale Precision Matrix Estimation With SQUIC
SSRN, Elsevier, August 2021, Pages1-15, dx.doi.org/10.2139/ssrn.390400
I. Horenko
Cheap robust learning of data anomalies with analytically solvable entropic outlier sparsification
Proceedings of the National Academy of Sciences (PNAS), Volume 119, Issue 9, Feb 2022, Pages 95-102, doi.org/10.1073/pnas.2119659119
S. van der Tuin, SE. Balafas, AJ. Oldehinkel, EC. Wit, SH. Booij, H. Wigman
Dynamic symptom networks across different at-risk stages for psychosis: An individual and transdiagnostic perspective
Schizophrenia Research, Volume 239, January 2022, Pages 95-102, doi.org/10.1016/j.schres.2021.11.018
R. Juozaitienė, EC. Wit
Non-parametric estimation of reciprocity and triadic effects in relational event networks
Social Networks, Volume 68, January 2022, Pages 296-305, doi.org/10.1016/j.socnet.2021.08.004
2021
Refereed Journal Articles
L. Augugliaro, V. Vinciotti, EC. Wit
Extending graphical models for applications: on covariates, missingness and normality
Statistical Methods & Applications, 1-11, October 2021, doi.org/10.1007/s10260-021-00605-2
F. Abegaz, ACMF. Martines, MA. Vieira-Lara, M. Rios-Morales, EC. Wit, B. Bakker
Bistability in fatty-acid oxidation resulting from substrate inhibition
PLoS Computational Biology, 17(8), August 2021, e1009259, doi.org/10.1371/journal.pcbi.1009259
A. Calabria, G. Spinozzi, F. Benedicenti, D. Cesana, L. Del Core, S. Scala, EC. Wit, E. Montini
Hematopoietic Reconstitution and Lineage Commitment in HSC Gene Therapy Patients Are Influenced by the Disease Background
Molecular Therapy, Volume 29, Number 4, 2021, Pages 42-42
SN. Wood, EC. Wit
Was R < 1 before the English lockdowns? On modelling mechanistic detail, causality and inference about Covid-19.
PLoS ONE 16(9): e0257455, September 2021 doi.org/10.1371/journal.pone.0257455
SN. Wood, EC. Wit, M. Fasiolo, PJ. Green
COVID-19 and the difficulty of inferring epidemiological parameters from clinical data
The Lancet Infectious Diseases, Volume 21, Issue 1, Pages 27–28, doi.org/10.1016/S1473-3099(20)30437-0
I. Horenko
Robust learning of data anomalies with analytically-solvable entropic outlier sparsification
arXiv preprint arXiv:2112.11768, arxiv.org/abs/2112.11768
I. Horenko, D. Rodrigues, T. O’Kane, K. Everschore-Sitte
Scalable computational measures for entropic detection of latent relations and their applications to magnetic imaging
Communications in Applied Mathematics and Computational Science, Volume 16, Number 2, Pages 267-297, doi.org/10.2140/camcos.2021.16.267
I. Horenko, L. Pospisil, E. Vecchi, S. Albrecht, A. Gerber, B. Rehbock, A. Stroh, S. Gerber
Quality-preserving low-cost probabilistic 3D denoising with applications to Computed Tomography
bioRxiv 2021.08.10.455778, doi.org/10.1101/2021.08.10.455778
J.B. Isbister, V. Reyes-Puerta, J. Sun, I. Horenko, H.J. Luhmann
Clustering and control for adaptation uncovers time-warped spike time patterns in cortical networks in vivo
Scientific Reports, Volume 11, Number 1, 2021, Pages 1-20, doi.org/10.1038/s41598-021-94002-0
O. Kaiser, D. Igdalov, O. Martius, I. Horenko
On Computationally-Scalable Spatio-Temporal Regression Clustering of Precipitation Threshold Excesses
arXiv preprint arXiv:2103.16687, arxiv.org/abs/2103.16687
D. Röhe Rodrigues, K. Everschor-Sitte, S. Gerber, I. Horenko,
A deeper look into natural sciences with physics-based and data-driven measures
iScience,
Volume 24, Issue 3,
2021, Pages 202171, doi.org/10.1016/j.isci.2021.102171
M. Bollhöfer, O. Schenk, F. Verbosio
High Performance Block Incomplete LU Factorization
Applied Numerical Mathematics, Volume 162, April 2021, Pages 265-2021, doi.org/10.1016/j.apnum.2020.12.023
A. Eftekhari, D. Pasadakis, S. Scheidegger, M. Bollhöfer, O. Schenk
Block-Enhanced Precision Matrix Estimation for Large-Scale Datasets
Journal of Computational Science, Volume 53, July 2021, Pages 1-39, doi.org/10.1016/j.jocs.2021.101389
D. Pasadakis, C. L. Alappat, O. Schenk, G. Wellein
Multiway p-spectral graph cuts on Grassmann manifolds
Machine Learning, November 2021, Pages 1-39,
doi.org/10.1007/s10994-021-06108-1
J. van Niekerk, H. Bakka, H. Rue, and O. Schenk
New frontiers in Bayesian modeling using the INLA package
Journal of Statistical Software, November 2021, Pages 1-39,
doi.org/10.18637/jss.v100.i02
Refereed Conference Articles
T. Holt, J. Kardoš, V. Fazio, L. Fabietti, F. Spazzini, O. Schenk
High-Performance Data Analytics Techniques for
Power Markets Simulation
2021 International Conference on Smart Energy Systems and Technologies (SEST)
Mon, Sep 6, 2021 – Wed, Sep 8, 2021, doi: 10.1109/SEST50973.2021.9543110
2020
Refereed Journal Articles
A. Buetti-Dinh, M. Herold, S. Christel, M.E. Hajjami, S. Bellenberg, O. Ilie, P. Wilmes, A. Poetsch, W. Sand, M. Vera, I.V. Pivkin, M. Dopson
Systems biology of acidophile biofilms for efficient metal extraction
Scientific Data, 2020
doi.org/10.1038/s41597-020-0519-2
S. Bellenberg , A. Buetti-Dinh, V. Galli, O. Ilie, M. Herold, S. Christel, M. Boretska, I.V. Pivkin, P. Wilmes, W. Sand, M. Vera, M. Dopson
Automated microscopic analysis of metal sulfide colonization by acidophilic microorganisms
Applied and Environmental Microbiology, 6-86, 2020
doi.org/10.1128/AEM.02702-19
A. Buetti-Dinh, M. Herold, S. Christel , M.E.Hajjami, F. Delogu, O. Ilie, S. Bellenberg, P. Wilmes, A. Poetsch, W. Sand, M. Vera, I.V. Pivkin, R. Friedman, M. Dopson
Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations
BMC Bioinformatics, 1-21, 2020
doi.org/10.1186/s12859-019-3337-9
I. Horenko, G. Marchenko, P. Gagliardini
On a computationally-scalable sparse formulation of the multidimensional and non-stationary maximum entropy principle
Communications in Applied Mathematics and Computational Science, Volume 15, Number 2, Pages 1-38, 2020
https://msp.org/soon/coming.php?jpath=camcos
I. Horenko, D. Rodrigues, T. O'Kane, K. Everschor-Sitte
Physically-inspired computational tools for sharp detection of material inhomogeneities in magnetic imaging
ArXiv:1907.04601v2
arxiv.org/abs/1907.04601
I. Horenko
On a scalable entropic breaching of the overfitting barrier in machine learning
Neural Computation, Volume 32, Number 8, pages 1563-1579, 2020
www.mitpressjournals.org/toc/neco/32/8
S. Gerber, L. Pospisil, M. Navandar, I. Horenko
Low-cost scalable discretization, prediction and feature selection for complex systems.
Science Advances, Volume 6, Number 5, eaaw0961, 2020
DOI: 10.1126/sciadv.aaw0961
R. Užupytė, E. C. Wit
Test for triadic closure and triadic protection in temporal relational event data
Social Network Analysis and Mining, 1-21(10), 2020
scopus.com
V. Vinciotti, E. C. Wit
Statistica Neerlandica special issue on Statistical Network Science
Statistica Neerlandica, 74(3), 220-221, 2020.
doi.org/10.1111/stan.12212
F. Richter, B. Haegeman, R. S. Etienne, E. C. Wit
Introducing a general class of species diversification models for phylogenetic trees
Statistica Neerlandica, 74(3), 261-274, 2020
doi.org/10.1111/stan.12205
S. Ranciati., E.C. Wit, C. Viroli
Bayesian smooth-and-match inference for ordinary differential equations models linear in the parameters
Statistica Neerlandica, 74(3), 125-144, 2020
scopus.com
E. C. Wit, L. Augugliaro, H. Pazira, J. González, F. Abegaz
Sparse relative risk regression models
Biostatistics, 21(2), e131–e147, 2020
scopus.com
W. Kruijer, P. Behrouzi, D. Bustos-Korts, M. X. Rodríguez-Álvarez, S. M. Mahmoudi, B. Yandell, E. C. Wit, F. A. van Eeuwijk
Reconstruction of Networks with Direct and Indirect Genetic Effects
Genetics, 214(4), 781-807, 2020
doi.org/10.1534/GENETICS.119.302949
M. Signorelli, E. C. Wit
Model-based clustering for populations of networks
Statistical Modelling, 20(1), 9-29, 2020
doi.org/10.1177/1471082X19871128
S. de Vos, S. Patten, E. C. Wit, E. H. Bos, K. J. Wardenaar, P. de Jonge
Subtyping psychological distress in the population: A semi-parametric network approach
Epidemiology and psychiatric sciences, 29, 1-8, 2020
doi.org/10.1017/S204579601900026X
S. N. Wood, E. C. Wit, M.Fasiolo, P. J. Green
COVID-19 and the difficulty of inferring epidemiological parameters from clinical data
Lancet Infectious Diseases, 2020
doi.org/10.1016/S1473-3099(20)30437-0
I. Artico, I. Smolyarenko, V. Vinciotti, E. C. Wit
How rare are power-law networks really?
Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 476(2241), 2020
doi.org/10.1098/rspa.2019.0742
P. Arbenz, L. Giraud, O. Schenk.W. Vanroose
Guest editorial: Special Issue on Parallel Matrix Algorithms and Applications (PMAA’18)
Parallel Computing, Volume 84, December 2020, Pages 1-2
doi.org/10.1016/j.parco.2018.01.003
P. Sanan. D. May, B. Bollhöfer, O. Schenk
Pragmatic Solvers for 3D Stokes and Elasticity Problems with Heterogeneous Coefficients: Evaluating Modern Incomplete LDLT Preconditioners
Solid Earth Discussions, 1-23, 2020,
doi.org/10.5194/se-2020-79
A. Klawonn, M. Lanser, M. Uran, O. Rheinbach, S. Köhler, J. Schröder, L. Scheunemann, D. Brands, D. Balzani, A. Gandhi, G. Wellein, M. Wittmann, O. Schenk, R. Janalík
Towards A Virtual Laboratory - Computation of Forming Limit Curves
In: Bungartz HJ., Reiz S., Uekermann B., Neumann P., Nagel W. (eds) Software for Exascale Computing - SPPEXA 2016-2019. Lecture Notes in Computational Science and Engineering, vol 136. Springer, Cham,
doi.org/10.1007/978-3-030-47956-5_13
J. Kardos, D. Kourounis, and O. Schenk
Two-Level Parallel Augmented Schur Complement Interior-Point Algorithms for the Solution of Security Constrained Optimal Power Flow Problems
IEEE Transactions on Power Systems, 1340 - 1350, Volume: 35 , Issue: 2 , March 2020,
doi.org/10.1109/TPWRS.2019.2942964
C. Alappat, G. Hager, O. Schenk, J. Thies, A. Basermann, A. Bischop, H. Fehske, G. Wellein
A Recursive Algebraic Coloring Technique for Hardware-Efficient Symmetric Sparse Matrix-Vector Multiplication
ACM Transactions on Parallel Computing, Vol. 7, No. 3, Article 19, June 2020
doi.org/10.1145/3399732
2019
Book Contributions
M. Bollhöfer, O. Schenk , R. Janalik, S. Hamm, and K. Gullapalli
State-of-The-Art Sparse Direct Solvers
In: Grama A., Sameh A. (eds) Parallel Algorithms in Computational Science and Engineering. Modeling and Simulation in Science, Engineering and Technology. pp 1-32, Birkhäuser, Cham.
doi.org/10.1007/978-3-030-43736-7_1
J. Kardos, D. Kourounis, and O. Schenk
Parallel Structure Exploiting Interior Point Methods
In: Grama A., Sameh A. (eds) Parallel Algorithms in Computational Science and Engineering. Modeling and Simulation in Science, Engineering and Technology. pp 63-93, Birkhäuser, Cham.
doi.org/10.1007/978-3-030-43736-7_3
Refereed Journal Articles
A. Buetti-Dinh, V. Galli, S. Bellenberg, O. Ilie, M. Herold, S. Christel, M. Boretska, I.V. Pivkin, P. Wilmes, W. Sand, M. Vera, M. Dopson
Deep neural networks outperform human expert's capacity in characterizing bioleaching bacterial biofilm composition
Biotechnology Reports, 2019
doi.org/10.1016/j.btre.2019.e00321
D. Pellin, L. Biasco, A. Aiuti, M.C. Di Serio, E.C. Wit
Penalized inference of the hematopoietic cell differentiation network via high-dimensional clonal tracking
Applied Network Science, 1-115, 2019
doi.org/10.1007/s41109-019-0225-1
K. Kamphorst, B.C. Oosterloo, A.M. Vlieger, N.B. Rutten, C.M. Bunkers, E.C. Wit, R.M Van Elburg
Antibiotic Treatment in the First Week of Life Impacts the Growth Trajectory in the First Year of Life in Term Infants
Journal of Pediatric Gastroenterology and Nutrition, 131-136, 2019
doi.org/10.1097/MPG.0000000000002360
A. Abbruzzo , I. Vujačić, A.M. Mineo, E.C. Wit
Selecting the tuning parameter in penalized Gaussian graphical models
Statistics and Computing, 559-569, 2019
doi.org/10.1007/s11222-018-9823-5
P. Behrouzi, E.C. Wit
De novo construction of polyploid linkage maps using discrete graphical models
Bioinformatics, 1-3, 2019
doi.org/10.1093/bioinformatics/bty777
N. Demetrashvili, N. Smidt, H. Snieder, E.R. Van Den Heuvel, E.C. Wit
Variance components models for analysis of big family data of health outcomes in the lifelines cohort study
Twin Research and Human Genetics, 1083-1093, 2019
https://doi.org/10.1017/thg.2019.1
E.C. Wit
Introduction to network inference in genomics
Network Science: An Aerial View, 99-119, 2019
doi.org/10.1007/978-3-030-26814-5_7
R. Mohammadi, E.C. Wit
BDgraph: An R package for Bayesian structure learning in graphical models
Journal of Statistical Software, 1-2, 2019
doi.org/10.18637/jss.v089.i03
S. E. Balafas, W. P. Krijnen, W. J. Post, E. C. Wit
An R Package for Nonparametric IRT Modelling of Unfolding Processes
The R Journal,
journal.r-project.org
N. P. Gill, L. D’Arrigo, E. C Wit
Comparison of Postoperative Fever and Effectiveness of Percutaneous Nephrolithotomy (PCNL) Versus Retrograde Intrarenal Surgery (RIRS) for the Treatment of Renal Stones
SN Comprehensive Clinical Medicine, 1(3), 154-159.019, 2019
doi.org/10.1007/s42399-018-0023-6
P. Behrouzi, E. C. Wit
Detecting epistatic selection with partially observed genotype data by using copula graphical models
Journal of the Royal Statistical Society. Series C: Applied Statistics, 68(1), 141-160, 2019
doi.org/10.1111/rssc.12287
M. Rosenau, I. Horenko, F. Corbi, M. Rudolf, R. Kornhuber and O. Oncken
Synchronization of great subduction megathrust earthquakes: Insights from scale model analysis.
Journal of Geophysical Research: Solid Earth, 124(4):3646-3661, 2019
agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018JB016597
M. Bollhoefer, A. Eftekhari, S. Scheidegger, and O. Schenk
Large-Scale Sparse Inverse Covariance Matrix Estimation
SIAM J. Sci. Comput., 41(1), A380–A401, January 2019, DOI:
doi.org/10.1137/17M1147615