Nezakati, E. & Pircalabelu, E.
Estimation and inference in sparse multivariate regression and conditional Gaussian graphical models under an unbalanced distributed setting (Under review)
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⚠ In case you are interested in a PhD/Post doctoral position in high-dimensional statistics, federeated learning, graphs and networks.... please reach out to me! ⚠
I am a Lecturer (Chargé de cours) at UCLouvain working at the Institute of Statistics, Biostatistics and Actuarial Sciences within LIDAM at the Faculty of Science.
Prior to moving at UCLouvain, I held a Visiting professor position at Ghent University affiliated with the Department of Applied Mathematics, Computer Science and Statistics within the Faculty of Sciences and a Postdoctoral position at KU Leuven affiliated with the ORSTAT department within the Faculty of Economics and Business.
My research focuses on: Models for high-dimensional data, Probabilistic graphical models, Social network models, Copula models and Information criteria.
I am currently teaching
Technical reports
Pircalabelu, E.
WB-graphs: a within versus between group similarity interplay (Under review)
Pircalabelu, E. & Bing, X.
Overlapping clustering of time dependent variables for fMRI data. (Under review)
Pircalabelu, E. & Mohammadi, A.
Functional connectivity estimation using informative prior knowledge. (In preparation)
Publications
Nezakati, E. & Pircalabelu, E. (2023)
Unbalanced distributed estimation and inference for precision matrices. Statistics and Computing, 33, 47.
The final publication is available at https://link.springer.com/article/10.1007/s11222-023-10211-9#citeasPircalabelu, E. (2022)
A spline-based time-varying reproduction number for modelling epidemiological outbreaks. Journal of the Royal Statistical Society (C), 72(3), 688-702
The final publication is available at https://academic.oup.com/jrsssc/article/72/3/688/7107468Pircalabelu, E. & Claeskens, G. (2022)
Linear manifold modeling and graph estimation based on multivariate functional data. Journal of Computational and Graphical Statistics, 32(2), 378-387
The final publication is available at https://www.tandfonline.com/doi/full/10.1080/10618600.2022.2108818Pircalabelu, E. & Artemiou, A. (2022)
"High-dimensional Sufficient Dimension Reduction through principal projections", Electronic Journal of Statistics, 16(1), 1804-1830.
The final publication is available at https://projecteuclid.org/journals/electronic-journal-of-statistics/volume-16/issue-1/High-dimensional-sufficient-dimension-reduction-through-principal-projections/10.1214/22-EJS1988.fullPircalabelu, E. & Artemiou, A. (2021)
"Graph informed sufficient dimension reduction", Computational Statistics & Data Analysis, 164, 107302
The final publication is available at https://www.sciencedirect.com/science/article/pii/S0167947321001365Pircalabelu, E. & Claeskens, G. (2020).
"Community-Based Group Graphical Lasso", Journal of Machine Learning Research, 21(64), 1-32.
The final publication is available at https://www.jmlr.org/papers/v21/19-181.htmlPircalabelu, E., Claeskens, G. & Waldorp, L.J. (2019)
"Zoom-in/out joint graphical lasso for different coarseness scales." Journal of the Royal Statistical Society (C), 69(1), 47-67
The final publication is available at https://rss.onlinelibrary.wiley.com/doi/10.1111/rssc.12378.An online supplement is available at https://rss.onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1111%2Frssc.12378&file=rssc12378-sup-0001-Supinfo.pdf
Pircalabelu, E., Claeskens, G. & Gijbels, I. (2017).
"Copula directed acyclic graphs", Statistics and Computing, 27(1), 55-78
The final publication is available at http://link.springer.com/article/10.1007/s11222-015-9599-9Pircalabelu, E., Claeskens, G. & Waldorp, L.J. (2016) .
"Mixed scale joint graphical lasso", Biostatistics, 17(4), 793-806.
The final publication is available at http://biostatistics.oxfordjournals.org/content/early/2016/06/17/biostatistics.kxw025.full.pdf+htmlPircalabelu, E. & Claeskens, G. (2016).
"Focused model selection for social networks", Social Networks, 46, 76-86
The final publication is available at http://www.sciencedirect.com/science/article/pii/S0378873315301763Pircalabelu, E., Claeskens, G., Jahfari, S. & Waldorp, L.J. (2015).
"A focused information criterion for graphical models in fMRI connectivity with high-dimensional data", The Annals of Applied Statistics, 9(4), 2179-2214
The final publication is available at http://dx.doi.org/10.1214/15-AOAS882Pircalabelu, E., Claeskens, G. & Waldorp, L.J. (2015).
"A focused information criterion for graphical models", Statistics and Computing, 25(6), 1071-1092
The final publication is available at http://dx.doi.org/10.1007/s11222-014-9504-yProceedings
Jacquemain, A., Heuchenne, C., & Pircalabelu, E. (2021).
"A lasso-type estimation for the Lorenz regression."
Proceedings of the 22nd European Young Statistician Meeting, September 6-10th 2021,
Athens. Panteion University . Pages 41-45.
The final publication is available at
https://www.eysm2021.panteion.gr/files/Proceedings_EYSM_2021.pdf
Pircalabelu, E., Claeskens, G., & Waldorp, L.J. (2017).
"Top-down joint graphical lasso."
Proceedings of the 32nd International Workshop on Statistical Modelling, July 2-7th 2017,
Groningen. University of Groningen. Pages 47-50.
The final publication is available at
https://iwsm2017.webhosting.rug.nl/IWSM_2017_V2.pdf
Pircalabelu, E., Claeskens, G., Jahfari, S. & Waldorp, L.J. (2014).
"Nodewise graphical modeling using the Focused Information Criterion for 'p larger than n' settings."
Proceedings of the 29th International Workshop on Statistical Modelling, July 14-18th 2014, Göttingen. Georg-August-Universität Göttingen. Pages 273-278.
The final publication is available at
http://www.statmod.org/files/proceedings/iwsm2014_proceedings_vol1.pdf
Book chapters
Claeskens, G., Pircalabelu, E. & Waldorp, L.J.
"Constructing Graphical Models via the Focused Information Criterion."
In: Lecture Notes in Statistics: Modeling and Stochastic Learning for Forecasting in High Dimension (2015).
Editors: A. Antoniadis, X. Brossat, J.-M. Poggi. Pages 55-78.
The final publication is available at link.springer.com via
http://www.springer.com/us/book/9783319187310
Research team
Ensiyeh Nezakati
Third-year PhD student in Statistics at the Institute of Statistics, Biostatistics and Actuarial Sciences of UCLouvain. Ensiyeh got her Bachelor's and Master's degrees in Statistics from Ferdowsi University of Mashhad, Iran. In her research, she worked on reliability modeling of the degradation systems and her current interest is modeling high-dimensional data and estimation of probabilistic graphical models. Currently, she is working on new methodological aspects for the estimation of probabilistic graphical models to the setting where datasets are stored on distributed computer clusters.
Alexandre Jacquemain
Alexandre is a PhD student and teaching assistant at the Institute of Statistics, Biostatistics and Actuarial Sciences of UCLouvain since 2017. He first obtained a Joint Master Degree in Economics from UCLouvain, UNamur and Bocconi University. He then got a Master in Statistics from UCLouvain. Being trained in Economics and Statistics, he likes to work at the boundary between the two disciplines. His PhD thesis develops a semiparametric regression procedure based on the use of Lorenz curves, the so-called Lorenz regression procedure. The methodology promises interesting applications in the measurement of economic inequality. He is currently developing a penalized version of the Lorenz regression. Jointly supervised with Cédric Heuchenne (main supervisor)
Mengxue Li
Mengxue is a Ph.D. student at the Institute of Statistics, Biostatistics and Actuarial Sciences of UCLouvain since 2022. She obtained her Master's degree in Statistics from Shandong University, China. Her main research interests lie in modeling of spatio-temporal data, probabilistic graphical models and network analysis. Currently, she is working on modeling and analysis of time-varying brain functional networks. Jointly supervised with Rainer von Sachs (main supervisor).
Lise Léonard
Lise is a first-year PhD student and a teaching assistant in statistics at the Institute of Statistics, Biostatistics and Actuarial Sciences at UCLouvain. She received her Bachelor's degree in mathematical sciences from the Université libre de Bruxelles, and her Master's degree in Statistics from UCLouvain. In her research, Lise develops inference tools for high-dimensional models. She is currently working on inference for model-averaging estimators based on the desparsified LASSO estimator. Jointly supervised with Rainer von Sachs.
Past PhD theses
Alexandre Jacquemain (2023). Lorenz regressions.
Jointly supervised with Cédric Heuchenne (main supervisor)
R packages
Involvement
We are in full preparations for the 30th Annual Meeting of the Royal Statistical Society of Belgium .. The website of the conference is here. Looking forward to seing you in Louvain-la-Neuve!.
As part of the open-science paradigme that UCLouvain strongly believes in, I am currently together with other colleagues at UCLouvain, developing the RShiny@UCLouvain platform that will host didactical apps created in Shiny for classroom usage.
COVID - 19
https://eugenpircalabelu.shinyapps.io/covid19-forecasting .
More on the subject here: https://uclouvain.be/fr/instituts-recherche/lidam/actualites/how-can-we-predict-the-evolution-of-covid-19-in-belgium.html .
Contact
Room d.125, 1st floor; Voie du Roman Pays 20, 1348 Louvain-la-Neuve, Belgium