Academic interests
Statistics, Artificial Intelligence, Econometrics, Machine Learning, Operations Research
My research interests are focused on Bayesian model selection, averaging, and automatic model configuration in complex regression contexts ranging from simple linear models to highly nonlinear Bayesian regressions and Neural Networks. In general, Bayesian statistics, both applied (survey data, epigenetic data, QTL studies) and methodological (MCMC, Variational inference, EM-like algorithms), are my fields of expertise. I am also interested in Weak Supervision in Machine Learning through mixture modelling. Read more about master projects with me here.
Research profiles
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)
Academic Background
University of Oslo, Oslo, Norway — PhD
August 2014 - August 2018
Faculty of Mathematics and Natural Sciences
Speciality: Statistics
Dissertation: "Bayesian model configuration, selection and averaging in complex regression contexts".
Molde University College - Specialized University in Logistics, Molde, Norway — Master of Science
August 2012 - June 2014
Faculty of Economics, Informatics and Social Research.
Speciality: Operations Research
Research Thesis: "Evaluation of Supply Vessel schedules robustness with a posteriori improvements".
Belarusian State University, Minsk Belarus — Specialist
September 2008 - June 2013
Faculty of Applied Mathematics and Computer Science. Department of Mathematical Modelling and Data Analysis
Speciality: Economic Cybernetics (mathematical methods and computer-based modelling in economy).
Research Thesis: "Methods and tools of investment management in conditions of international diversifications"
Awards
- The NIMA 2014 award for the best MSc graduate at Molde University College - Specialized University in Logistics, 2014
- Ministry of Education of the Republic of Belarus First Award in the section "Mathematics. Methods and algorithms of mathematical modelling and computational mathematics for solving economic, engineering and natural science problems." Research topic: "Methods and tools of investment management in conditions of international diversifications.", 2013
- Belarusian State University. Faculty of Applied Mathematics and Computer Science first award for student research in the scope of "Probabilistic and statistical models and methods", 2012
- 2nd place at the poster competition at Graybill 2017 conference for the poster entitled "A novel algorithmic approach to Bayesian logic regression", 2017
Project
Bayesian model selection: Bayesian model selection.
Positions held previously
Norwegian Computing Center, Oslo, Norway — Research scientist/Senior research scientist
September 2018 - December 2020
Fundamental research in statistics and machine learning, publishing articles and working on projects involving the development of customized statistical and machine learning methodology in various applications for private and public sectors, acting as a reviewer in several highly ranked journals including the Scandinavian Journal of Statistics, Journal of the American Statistical Association and Scientific Reports and conferences including ACL and EMNLP.
University of Oslo, Oslo, Norway —PhD candidate
August 2014 - August 2018
Bayesian variable selection and model averaging. Bayesian deep feature engineering. Applied research with Genetic and Epigenetic data (GWAS, EWAS, QTL mapping, etc.).
Compatibl, Minsk, Belarus —Business analyst
September 2011 - June 2012
Calculation of CVA and regulatory capital as well as full support, implementation and customisation services within the Analyst project. Compatibl's customers included some of the largest and most respected banks and hedge funds worldwide, including 4 dealers, 3 supranational, over 20 central banks, and 3 major financial technology vendors.
- Bayesian Statistics
- Model selection
- Probabilistic Machine Learning
- Operations Research
Liste med publikasjoner fra min forskning. (Cristin)
- 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. Full text in Research Archive
- 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. 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.
- 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. Full text in Research Archive Show summary
- 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. Full text in Research Archive
- 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. Full text in Research Archive
- 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. Full text in Research ArchiveShow summary
- 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; Storvik, Geir Olve & Frommlet, Florian (2020). Rejoinder for the discussion of the paper "A Novel Algorithmic Approach to Bayesian Logic Regression". Bayesian Analysis.ISSN 1936-0975. 15(1), p. 312–333. doi: 10.1214/18-ba1141. Full text in Research Archive
- 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. Full text in Research Archive
- 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. Full text in Research Archive
- 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. doi: 10.1016/j.csda.2018.05.020.
Mine publikasjoner
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)