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Irene Tubikanec

I am currently holding a postdoctoral position at the Institute of Applied of Statistics
of the Johannes Kepler University (JKU) Linz in Austria. Before I was a postdoc
at the Department of Statistics, University of Klagenfurt, Austria.

foto_irene_3.png

Dr. Irene Tubikanec

Postdoc at the

Institute of Applied Statistics

 JKU Linz (Austria)

Irene.Tubikanec@jku.at

Research interests

Research interests

My interests lie at the interface of (multi-dimensional and non-linear) stochastic differential equations, efficient and structure-preserving numerical simulation methods and statistical inference procedures.

I like to connect different ideas and disciplines, and to deal with real-life applications.

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Keywords:

  • Stochastic models (mainly SDEs/diffusion processes and Hawkes processes)

  • Mathematical neuroscience

  • Numerical methods for SDEs (Splitting methods, Structure-preservation)

  • Statistical inference for stochastic processes (Approximate Bayesian computation)

  • Network inference

  • EEG data

Publications

Publications

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  1. Buckwar, E., Tamborrino, M. and Tubikanec, I. Spectral density-based and measure-preserving ABC
    for partially observed diffusion processes. An illustration on Hamiltonian SDEs.     Statistics and Computing 30, 
    627-648 (2020). 10.1007/s11222-019-09909-6​
     

  2. Chevallier, J., Melnykova, A. and Tubikanec, I. Diffusion approximation of multi-class Hawkes processes: theoretical and numerical analysis. Advances in Applied Probability 53(3), 716-756 (2021). doi:10.1017/apr.2020.73
     

  3. ​Tubikanec, I., Tamborrino, M., Lansky, P. and Buckwar, E. Qualitative properties of different numerical methods for the inhomogeneuos geometric Brownian motion. Journal of Computational and Applied Mathematics 406, 113951 (2022). 10.1016/j.cam.2021.113951
     

  4. Buckwar, E., Samson, A., Tamborrino, M. and Tubikanec, I. A splitting method for SDEs with locally Lipschitz drift: Illustration on the FitzHugh-Nagumo model. Applied Numerical Mathematics 179, 191-220 (2022) 10.1016/j.apnum.2022.04.018
     

  5. Ditlevsen, S., Tamborrino, M. and Tubikanec, I. Network inference in a stochastic multi-population neural mass model via approximate Bayesian computation. (2023). Preprint available at: arxiv.org/abs/2306.15787
     

  6. Hlavackova-Schindler, K., Melnykova, A. and Tubikanec, I. Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length. Journal of Machine Learning Research 25(133):1−26 (2024)​ 
    http://jmlr.org/papers/v25/23-1066.html
     

  7. Samson, A., Tamborrino, M. and Tubikanec, I. Inference for the stochastic FitzHugh-Nagumo model from real action potential data via approximate Bayesian computation. Computational Statistics & Data Analysis 204,108095 (2025)
    10.1016/j.csda.2024.108095

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Talks

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Conference talks​

  1. ISAAC-ICMAM Conference of Women in Mathematics, Virtual Conference (11/2024)
    Invited Plenary Speaker

  2. 12th Austrian Stochastics Days, Innsbruck, Austria (09/2024)
    Invited Session Speaker

  3. Workshop New Directions for Stochastic Differential Equations and Machine Learning, Edinburgh, United Kingdom (06/2024)

  4. 14th International Conference on Monte Carlo Methods and Applications, Paris, France (06/2023)

  5. Workshop on Numerical Analysis and Applications of SDEs, Bedlewo, Poland (09/2022)

  6. 10th Austrian Stochastics Days, Vienna, Austria (09/2022)

  7. SIAM Conference on Analysis of Partial Differential Equations (PD22), Virtual Conference, (03/2022)
    Invited to a minisymposium on Partial Differential Equations in Neurosciences: from Modelling to Theoretical Analysis
    (organised by Pierre Roux and Susanne Solem)     

  8. Dynamics Days 2021, Nice, France (08/2021)
    Invited to a minisymposium on inference and data-driven modeling of large chaotic and noisy systems (organised by Kevin Lin and Fei Lu)

  9. Neural Coding, held online (07/2021)

  10. 28th Nordic Conference in Mathematical Statistics (NORDSTAT), Tromsø, Norway (06/2021)
    Invited to a contributed session on simulation-based inference (organised by Umberto Picchini)

  11. Bernoulli-IMS One World Symposium 2020, Virtual Symposium (08/2020)

  12. 6th International Conference on Mathematical Neuroscience (ICMNS), Digital Edition (07/2020)
    Invited to a session on numerical methods in mathematical neuroscience (organised by Evelyn Buckwar)

  13. Neural Coding, Turin, Italy (09/2018)

  14. 11th European Conference on Mathematical and Theoretical Biology (ECMTB), Lisbon, Portugal (07/2018)
    Invited to a minisymposium on computational statistics and Bayesian inference for stochastic biological models (organised by Massimiliano Tamborrino and Evelyn Buckwar)

  15. 6th Austrian Stochastics Days, Salzburg, Austria (09/2017)
     

Invited seminar talks

  1. Research Seminar, University of Avignon, France (09/2023)

  2. Doctoral Seminar, University of Klagenfurt, Austria (04/2022)

  3. One World Stochastic Numerics and Inverse Problems Seminar, University of Edinburgh, United Kingdom (11/2021)

  4. Research Seminar of the Institute of Applied Statistics, Johannes Kepler University Linz, Austria (11/2020)

  5. One World Approximate Bayesian Computation (ABC) Seminar, University of Warwick, United Kingdom (06/2020)

  6. Workshop on Numerical Methods for Stochastic Differential Equations, Lund University, Sweden (10/2019)

  7. Seminar in Applied Mathematics and Statistics, University of Copenhagen, Denmark (02/2019)

  8. Seminar Laboratoire Jean Kuntzmann on Probability and Statistics, University Grenoble Alpes, France (10/2018)
     

Poster presentations

  1. 11th International Workshop on Bayesian Inference in Stochastic Processes (BISP), Madrid, Spain (06/2019) 

  2. 5th International Conference on Mathematical Neuroscience (ICMNS), Copenhagen, Denmark (06/2019)

  3. Bayes Comp, Barcelona, Spain (03/2018)

  4. Computational Brain Connectivity Mapping, Winter School Workshop, Juan-les-Pins, France (11/2017)

Talks
Grants and awards

Grants and awards

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Research in pairs grant

from the Oberwolfach Research Center for Mathematics (MFO), Germany 2020, with Anna Melnykova

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Kepler Award for Excellence in Teaching 2023

for the lecture Stochastic Simulations, from the Johannes Kepler University Linz, Austria

 

JKU Young Researchers' Award 2020

for my PhD thesis, from the Johannes Kepler Universtiy Linz, Austria

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Zonta Award

from the Zonta Club Linz and Department of Gender & Diversity of the Johannes Kepler University Linz, Austria

Scientific stays

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Research in pairs stay

at the Oberwolfach Research Center for Mathematics (MFO), Germany 2020, with Anna Melnykova

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Cooperations with other institutions​

  1. Laboratory of Mathematics (LMA), University of Avignon, France 2023 (2 weeks) and 2024 (2 weeks)
    to work with Anna Melnykova

  2. Department of Mathematical Sciences, University of Copenhagen, Denmark 2019 (3 weeks) and 2023 (2 weeks) 
    to work with Susanne Ditlevsen  

  3. Laboratoire Jean Kuntzmann (LJK), University Grenoble Alpes, France 2018-2020 (5 weeks)
    to work with Adeline Samson 

  4. Department of Computational Neuroscience, Institute of Physiology of the Czech Academy of Sciences, 2018-2019 (5 weeks)
    to work with Petr Lansky

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Schools

  1. Winter school Computational Brain Connectivity Mapping, Juan-les-Pins, France (11/2017)

  2. Summer school Mathematical Modeling in Neuroscience, Bornholm, Denmark (06/2017)

  3. Summer school Probabilistic Numerics, Dobbiaco, Italy (06/2017)

Scientific stays

Teaching

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Project week and seminars for high-school students (2018-2019)

  1. Coordination of a one-week project in applied mathematics

  2. Applied math seminars

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​Teaching (since 2016)

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Lectures, Seminars, Exercises and Tutorials:

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  1. Probability theory and statistics

  2. Probability calculus for teacher education

  3. Stochastic processes 

  4. Stochastic differential equations

  5. Stochastics for engineers

  6. Statistical methods (for math students)

  7. Statistical methods (for business students)

  8. Stochastic simulations

  9. Exploratory data analysis in R

  10. Mathematics for chemists

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Co-supervision of academic theses (since 2021)

  1. Two bachelor students (Topics: Stochastic processes and applications)

  2. One master student (Topic: Markov chain Monte Carlo methods)

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Teaching
Contact

Contact me.

E-Mail:    Irene.Tubikanec@jku.at

LinkedIn:  linkedin.com/in/irene-tubikanec-0b1b05230

Address:  Johannes Kepler University Linz, Altenberger Straße 69, 4040 Linz, Austria

Office:  Science Park 2, Zwischengeschoß, S2 0069-2

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