Aliaksandr Hubin - Academic Profile

About Me

I am an Associate Professor with a focus on Statistics, Artificial Intelligence, Machine Learning, and Operations Research. My academic training spans across three institutions, culminating in a PhD in Statistics from the University of Oslo, Norway. My research interests include Bayesian statistics, Bayesian model selection and averaging, efficient MCMC and Variational Bayes inference procedures, and probabilistic machine learning. I have published extensively in these areas and am actively involved in mentoring the next generation of scholars. I am also the chair of the board of the Oslo section of the norwegian statistical association.

Research and Social Profiles:

Major news

Gave a talk at a special invited sesstion: Contrasting Bayesian and frequentist model building for descriptive research questions - a paired-design experiment of STRATOS-TG2, at ROES 2025.
FBMS version 1.2 released, CRAN. With accompanying tutorials, arXiv:2509.00753.
Proud of the my four new master students (Ylva, Sigurd, Sander, and Kristoffer) graduated at UiO.
Joined the scientific commitee of: NORDSTAT 2026.
A new preprint: "Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks."
Co-organizing IWSM 2026. Submissions open soon.
A new paper in Nordic Machine Intelligence: "Outlier Detection in Bayesian Neural Networks: Exploring Pre-activations and Predictive Entropy."
Gave an invited lecture: "A subsampling MCMC approach for Bayesian model selection and model averaging" at Universitat Pompeu Fabra, slides.

Our discussion on: Sparse Bayesian factor analysis when the number of factors is unknown by Frühwirth-Schnatter, Hosszejni, and Lopes is now published in Bayesian Analysis (pages 109-112).
My interview for Østfold AI hub is out.
My PhD student's first paper is now published in TMLR: Sparsifying Bayesian neural networks with latent binary variables and normalizing flows.
New paper in Neural Computing and Applications out Evolutionary variational inference for Bayesian generalized nonlinear models.
Humbled to be mentioned among top reviewers in UAI auai.org/uai2024/top_reviewers.
Two ICML 2024 papers out Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI and Incorporating probabilistic domain knowledge into deep multiple instance learning.
Organized by us NSM24 was a success story. Thanks to eveyone involved! Det 21. norske statistikermøtet.

Academic Background

University of Oslo, Oslo, Norway — PhD
August 2014 - August 2018
Faculty of Mathematics and Natural Sciences
Specialty: Statistics
Dissertation: "Bayesian model configuration, selection and averaging in complex regression contexts".
Molde University College (Specialized University), Molde, Norway — Master of Science
August 2012 - June 2014
Faculty of Economics, Informatics and Social Research
Specialty: Operations Research
Research Thesis: "Evaluation of Supply Vessel schedules robustness with a posteriori improvements".
Belarusian State University, Minsk, Belarus — Specialist (Diploma)
September 2008 - June 2013
Faculty of Applied Mathematics and Computer Science
Specialty: Econometrics and Statistics
Research Thesis: "Methods and tools of investment management in conditions of international diversifications".

Teaching

Courses Taught:

STK2130 - Modeling by Stochastic Processes (plenary sessions and exercises)
STK3100 - Introduction to generalized linear models (exercises)
STK4900 - Statistical methods and applications (plenary sessions and exercises)
STK2100 - Machine Learning and Statistical Methods for Prediction and Classification (lecturing)
STIN300 - Statistical programming in R (co-lecturing)

Mentoring

Master theses supervised:

Extending BGNLM and GMJMCMC for Time Series.
Sander F. Ørnes. 2025, UIO
Read Thesis
Genetic Ensemble of Trees as a special case of BGNLM.
Sigurd Myhre Vittersø. 2025, UIO
Read Thesis
Sampling from multimodal posteriors.
Kristoffer Solli Larsen, UIO
Read Thesis
Improved Algorithms for BGNLM: Introducing New Transformations for Model Search.
Ylva Sofie Tollefsen. 2025, UIO
Defence details
Improving sparsity and interpretability of latent binary Bayesian neural networks by introducing input-skip connections.
Eirik Høyheim. 2024, NMBU
Read Thesis
Subsampling Strategies for Bayesian Variable Selection and Model Averaging in GLM and BGNLM.
Jon Lachmann. 2021, Stockholm University
Read Thesis
Combining Variational Bayes and GMJMCMC for Scalable Inference on Bayesian Generalized Nonlinear Models.
Philip Sebastian Hauglie Sommerfelt. 2023, UIO
Read Thesis
Using Graph Bayesian Neural Networks for fraud pattern detection and classification from bank transactions data.
Osama Abidi. 2023, NMBU
Read Thesis
Outlier Detection in Bayesian Neural Networks.
Herman Ellingsen. 2023, NMBU
Read Thesis
Spherical Priors for Bayesian Deep Learning.
Leif-Martin Sæther Sunde. 2023, UIO
Read Thesis
Improving latent binary Bayesian neural networks using the local reparametrization trick and normalizing flows.
Lars Skaaret-Lund. 2022, UIO
Read Thesis

Selected Publications

Hauglie Sommerfelt, Philip Sebastian & Hubin, Aliaksandr (2024). Sparsifying Bayesian neural networks with latent binary variables and normalizing flows. Transactions on Machine Learning Research (TMLR). Full text
al Hajj, Ghadi; Hubin, Aliaksandr; Kanduri, Chakravarthi; Pavlovic, Milena; Rand, Knut Dagestad & Widrich, Michael [Show all 11 contributors for this article] (2024). Incorporating probabilistic domain knowledge into deep multiple instance learning. Proceedings of Machine Learning Research (PMLR). ISSN 2640-3498. 235, p. 17279–17297. Full text
Hauglie Sommerfelt, Philip Sebastian & Hubin, Aliaksandr (2024). Evolutionary variational inference for Bayesian generalized nonlinear models. Neural Computing & Applications. ISSN 0941-0643. p. 1–18. doi: 10.1007/s00521-024-10349-1
Papamarkou, Theodore; Skoularidou, Maria; Palla, Konstantina; Aitchison, Laurence; Arbel, Julyan & Dunson, David [Show all 25 contributors for this article] (2024). Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. Proceedings of Machine Learning Research (PMLR). ISSN 2640-3498. 235, p. 39556–39586. doi: 10.48550/arXiv.2402.00809
Digranes, Nora; Hoeberg, Emma; Lervik, Andreas; Hubin, Aliaksandr; Nordgreen, Janicke & Haga, Henning Andreas (2024). Motor effects of fentanyl in isoflurane-anaesthetized pigs and the subsequent effect of ketanserin or naloxone. Veterinary Anaesthesia and Analgesia. ISSN 1467-2987. 51(5), p. 491–499. doi: 10.1016/j.vaa.2024.07.002
Siciliani, Daphne; Hubin, Aliaksandr; Ruyter, Bente Synnøve; Chikwati, Elvis Mashingaidze; Thunes, Vebjørn Gunnarson & Valen, Elin Christine [Show all 10 contributors for this article] (2024). Effects of dietary fish to rapeseed oil ratio on steatosis symptoms in Atlantic salmon (Salmo salar L) of different sizes. Scientific Reports. ISSN 2045-2322. 14(1), p. 1–18. doi: 10.1038/s41598-024-68434-3
Hubin, Aliaksandr & Storvik, Geir Olve (2024). Sparse Bayesian Neural Networks: Bridging Model and Parameter Uncertainty through Scalable Variational Inference. Mathematics. ISSN 2227-7390. 12(6). doi: 10.3390/math12060788
Hubin, Aliaksandr; Heinze, Georg & De Bin, Riccardo (2023). Fractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Models. Fractal and Fractional. 7(9), p. 1–23. doi: 10.3390/fractalfract7090641
Lachmann, Jon; Storvik, Geir Olve; Frommlet, Florian & Hubin, Aliaksandr (2022). A subsampling approach for Bayesian model selection. International Journal of Approximate Reasoning. ISSN 0888-613X. 151, p. 33–63. doi: 10.1016/j.ijar.2022.08.018
Hubin, Aliaksandr & De Bin, Riccardo (2022). On a genetically modified mode jumping MCMC approach for multivariate fractional polynomials. In Torelli, Nicola; BELLIO, RUGGERO & MUGGEO, VITO (Ed.), Proceedings of the 36th International Workshop on Statistical Modelling. EUT Edizioni Università di Trieste. ISSN 978-88-5511-309-0. p. 478–483.
Gåsemyr, Jørund Inge & Hubin, Aliaksandr (2022). Prior distributions expressing ignorance about convex increasing failure rates. Scandinavian Journal of Statistics. ISSN 0303-6898. doi: 10.1111/sjos.12588
Hubin, Aliaksandr; Storvik, Geir & Frommlet, Florian (2021). Flexible Bayesian Nonlinear Model Configuration. The Journal of Artificial Intelligence Research. ISSN 1076-9757. 72, p. 901–942. doi: 10.1613/JAIR.1.13047
Hubin, Aliaksandr; Frommlet, Florian & Storvik, Geir Olve (2021). Reversible genetically modified mode jumping MCMC. In Makridis, Andreas; Milienos, Fotios; Papastamoulis, Panagiotis; Parpoula, Christina & Rakitzis, Athanasios (Ed.), 22nd European Young Statisticians Meeting – Proceedings. Department of Psychology & Department of Sociology, School of Social Science, Panteion University of Social and Political Sciences. ISSN 978-960-7943-23-1. p. 35–40.
Lison, Pierre; Barnes, Jeremy & Hubin, Aliaksandr (2021). skweak: Weak Supervision Made Easy for NLP. In Ji, Heng & Park, Jong (Ed.), Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations. Association for Computational Linguistics. ISSN 978-1-954085-56-5. p. 337–346. doi: 10.18653/v1/2021.acl-demo.40
Lison, Pierre; Barnes, Jeremy; Hubin, Aliaksandr & Touileb, Samia (2020). Named Entity Recognition without Labelled Data: A Weak Supervision Approach. In Jurafsky, Dan; Chai, Joyce; Schluter, Natalie & Tetreault, Joel (Ed.), Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics. ISSN 978-1-952148-25-5. p. 1518–1533.
Hubin, Aliaksandr; Storvik, Geir Olve; Grini, Paul Eivind & Butenko, Melinka Alonso (2020). A Bayesian binomial regression model with latent gaussian processes for modelling DNA methylation. Austrian Journal of Statistics. ISSN 1026-597X. 49(4), p. 46–56. doi: 10.17713/ajs.v49i4.1124
Hubin, Aliaksandr; Storvik, Geir Olve; Grini, Paul Eivind & Butenko, Melinka Alonso (2019). Bayesian binomial regression model with a latent Gaussian field for analysis of epigenetic data. In Kharin, Y & Filzmoser, Peter (Ed.), Proceedings of Computer Data Analysis and Modeling: Stochastics and Data Science 2019. Belarusian State University Press. ISSN 978-985-566-811-5. p. 167–171.
Hubin, Aliaksandr (2019). An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov models, ACM International Conference Proceeding Series (ICPS): AIIPCC '19: Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing. Association for Computing Machinery (ACM). ISSN 978-1-4503-7633-4. p. 1–9. doi: 10.1145/3371425.3371641
Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). A Novel Algorithmic Approach to Bayesian Logic Regression. Bayesian Analysis. ISSN 1936-0975. 15(1), p. 263–311. doi: 10.1214/18-BA1141
Hubin, Aliaksandr & Storvik, Geir Olve (2018). Mode jumping MCMC for Bayesian variable selection in GLMM. Computational Statistics & Data Analysis. ISSN 0167-9473. 127, p. 281–297. doi: 10.1016/j.csda.2018.05.020
Hubin, Aliaksandr & Storvik, Geir Olve (2016). On Mode Jumping in MCMC for Bayesian Variable Selection within GLMM. In Aivazian, S; Filzmoser, Peter & Kharin, Y (Ed.), COMPUTER DATA ANALYSIS AND MODELING. Theoretical and Applied Stochastics. Proceedings of the XI International Conference. Belarusian State University. ISSN 978-985-553-366-6. p. 275–278.

Software

Here you can find officially published software tools and packages I have developed or contributed to:

EMJMCMC: Evolutionary Mode Jumping Markov Chain Monte Carlo Expert Toolbox - CRAN Link
FBMS: Flexible Bayesian Model Selection and Model Averaging - CRAN Link
skweak: Weak supervision for NLP - Spacy Link

Reviewing

I have served as a reviewer for the following journals and conferences:

Contact Information

Email: aliaksandr.hubin@nmbu.no

Connect with me on LinkedIn

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