Aliaksandr Hubin

Postdoctoral Fellow - Statistics and Data Science
Image of Aliaksandr Hubin
Norwegian version of this page
Mobile phone +47 45171361
Username
Visiting address Moltke Moes vei 35 Niels Henrik Abels hus 0851 Oslo
Postal address Postboks 1053 Blindern 0316 Oslo

Date and Place of Birth

04.05.1990, Minsk, Republic of Belarus

Academic interests

StatisticsArtificial IntelligenceEconometricsMachine LearningOperations Research

Research profiles

Google Scholar

Researchgate 

LinkedIn

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

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.

Tags: Bayesian Statistics, Model selection, Probabilistic Machine Learning, Operations Research

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 978-1-954085-56-5. 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 1026-597X. 49(4), p. 46–56. doi: 10.17713/ajs.v49i4.1124. 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 (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. 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.

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  • Lison, Pierre; Barnes, Jeremy Claude & Hubin, Aliaksandr (2021). skweak: weak supervision made easy for NLP.
  • Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2020). A novel algorithmic approach to Bayesian Logic Regression.
  • 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
  • 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 non-homogeneous hidden Markov models.
  • 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; 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.
  • Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models.
  • Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models.
  • Hubin, Aliaksandr; Storvik, Geir Olve & Frommlet, Florian (2018). Deep Bayesian regression models.
  • 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 non-linear 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.
  • 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 1501-7710.

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Published Jan. 5, 2021 12:09 PM - Last modified Jan. 21, 2021 10:41 AM