Aliaksandr Hubin
Date and Place of Birth
04.05.1990, Minsk, Republic of Belarus
Academic interests
Statistics, Artificial Intelligence, Econometrics, Machine Learning, Operations Research
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)
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 in Logistics, 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
September 2008  June 2013
Faculty of Applied Mathematics and Computer Science. Department of Mathematical Modelling and Data Analys
Specialty: Economic Cybernetics (mathematical methods and computer based modeling 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 modeling and computational mathematics for solving economic, engineer 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
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 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 Analyst project. Compatibl's customers included some of the largest and most respected banks and hedge funds worldwide, including 4 dealers, 3 supranationals, over 20 central banks, and 3 major financial technology vendors.
Publications

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 9781954085565. p. 337–346. 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 1026597X. 49(4), p. 46–56. doi: 10.17713/ajs.v49i4.1124. Full text in Research Archive Show summary

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 9781952148255. p. 1518–1533. Full text in Research Archive

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 9789855668115. p. 167–171.

Hubin, Aliaksandr (2019). An adaptive simulated annealing EM algorithm for inference on nonhomogeneous 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 9781450376334. 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 19360975. 15(1), p. 263–311. doi: 10.1214/18BA1141. 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 01679473. 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 9789855533666. p. 275–278. doi: 10.1016/j.csda.2018.05.020.

Lison, Pierre; Barnes, Jeremy Claude & Hubin, Aliaksandr (2021). skweak: weak supervision made easy for NLP. Show summary

Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2020). A novel algorithmic approach to Bayesian Logic Regression. Show summary

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 19360975. 15(1), p. 312–333. doi: 10.1214/18ba1141. Full text in Research Archive

Storvik, Geir Olve & Hubin, Aliaksandr (2019). Combining model and parameter uncertainty in Bayesian neural networks.

Hubin, Aliaksandr & Storvik, Geir Olve (2019). Combining Model and Parameter Uncertainty in Bayesian Neural Networks.

Hubin, Aliaksandr & Storvik, Geir Olve (2019). Combining Model and Parameter Uncertainty in Bayesian Neural Networks.

Hubin, Aliaksandr & Storvik, Geir Olve (2019). Combining Model and Parameter Uncertainty in Bayesian Neural Networks.

Hubin, Aliaksandr (2019). An adaptive simulated annealing EM algorithm for inference on nonhomogeneous hidden Markov models.

Hubin, Aliaksandr & Storvik, Geir Olve (2019). Combining Model and Parameter Uncertainty in Bayesian Neural Networks. Show summary

Hubin, Aliaksandr & Storvik, Geir Olve (2019). Combining Model and Parameter Uncertainty in Bayesian Neural Networks.

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.

Hubin, Aliaksandr (2019). Using node embedding to obtain information from network based transactions data in a bank.

Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models.

Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models. Show summary

Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models.

Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models. Show summary

Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models. Show summary

Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models.

Hubin, Aliaksandr & Storvik, Geir Olve (2017). Efficient mode jumping MCMC for Bayesian variable selection and model averaging in GLMM.

Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2017). A novel GMJMCMC algorithm for Bayesian Logic Regression models.

Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2017). A novel algorithmic approach to Bayesian Logic Regression.

Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2017). A novel algorithmic approach to Bayesian Logic Regression.

Hubin, Aliaksandr; Storvik, Geir Olve & Grini, Paul Eivind (2017). Variable selection in binomial regression with latent Gaussian field models for analysis of epigenetic data.

Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2017). Deep nonlinear regression models in a Bayesian framework.

Hubin, Aliaksandr & Storvik, Geir Olve (2016). Variable selection in logistic regression with a latent Gaussian field models with an application in epigenomics.

Hubin, Aliaksandr & Storvik, Geir Olve (2016). On Mode Jumping in MCMC for Bayesian Variable Selection within GLMM.

Hubin, Aliaksandr & Storvik, Geir Olve (2016). Efficient mode jumping MCMC for Bayesian variable selection in GLM with random effects models.

Hubin, Aliaksandr & Storvik, Geir Olve (2016). VARIABLE SELECTION IN BINOMIAL REGRESSION WITH LATENT GAUSSIAN FIELD MODELS FOR ANALYSIS OF EPIGENETIC DATA.

Hubin, Aliaksandr & Storvik, Geir Olve (2015). Variable selection in binomial regression with a latent Gaussian field models for analysis of epigenetic data.

Hubin, Aliaksandr & Storvik, Geir Olve (2015). On model selection in Hidden Markov Models with covariates.

Hubin, Aliaksandr (2015). Statistics for Epigenetics.

Hubin, Aliaksandr & Storvik, Geir Olve (2015). Variable selection in binomial regression with a latent Gaussian field models for analysis of epigenetic data.

Hubin, Aliaksandr; Norlund, Ellen Karoline & Gribkovskaia, Irina (2014). Evaluating robustness of speed optimized supply vessel schedules. Show summary

Hubin, Aliaksandr & Aas, Kjersti (2019). FinAI: Scalable techniques to stock price time series modelling. Norsk Regnesentral.

Hubin, Aliaksandr (2018). Bayesian model configuration, selection and averaging in complex regression contexts. Universitetet i Oslo. ISSN 15017710.