Probability and Statistics

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Prof Dr. Marco Andreas Grzegorczyk

  • room number:
    449 (Bernoulliborg, building 5161)
  • Address:
    Nijenborgh 9
    9747 AG Groningen
    Netherlands
  • phone numbers:
    mobile: 0680 184 155
    international: +31 680 184 155
  • e-mail: m.a.grzegorczyk@rug.nl


Curriculum Vitae

  • Since 04/2019 Associate Professor for Computational Statistics, research unit: 'Statistics and Probability', Bernoulli Institute (BI), Groningen University, Netherlands.
  • 12/2013 - 03/2019 Assistant Professor, research unit: 'Statistics and Probability', Bernoulli Institute (BI), Groningen University, Netherlands.
  • 10/2011 - 11/2013 Personal Research Grant, funded by the German Research Foundation (DFG), project title: "Development of new Bayesian network models for systems biology research" (GR3853/1-1), Department of Statistics, TU Dortmund University.
  • 04/2011 - 09/2011 Interim guest lectureship for Applied Statistics, Department of Mathematics, Carl von Ossietzky University Oldenburg.
  • 01/2011 - 03/2011 Post-doctoral research fellow, Department of Statistics, TU Dortmund University.
  • 01/2009 - 12/2010 Post-doctoral research fellow in the Graduate School 'Statistical Modelling', Department of Statistics, TU Dortmund University.
  • 07/2007 - 12/2008 Post-doctoral research fellow at the Centre for Systems Biology at Edinburgh, School of Biological Sciences, Edinburgh University, UK and Associate at Biomathematics and Statistics Scotland (BioSS), Edinburgh, Scotland, UK (7/2007-12/2011).
  • 07/2003 - 06/2007 Research Fellow (PhD student and later post-doctoral fellow) in the research project 'Chemical Biology and Biotechnology' at the Department of Statistics, Dortmund University.


Research Interests

  • Bayesian networks and other graphical models for extracting gene-regulatory networks and protein pathways in systems biology research.


PhD students:

  • Chang Ma
  • Kezhuo Li
  • Leah Dijkshoorn
  • Albert Silvans
  • Mohammad Masjkur (external)


Former PhD students:

  • Dr. Luca del Core (supervised, 09/2018-04/2023)
  • Dr. Abdul Salam (supervised, 11/2018-10/2022)
  • Dr. Victor Bernal (co-supervised, 07/2016-04/2022)
  • Dr. Spyros Balafas (co-supervised, 04/2017-02/2022)
  • Dr. Mahdi Shafiee Kamalabad (supervised, 01/2015-12/2018)


Visiting researchers:

  • Dr. Ahmad Ahmadi Yazdi (Isfahan University of Technology, 06/2018-10/2018)
  • Dr. Zulfiqar Ali (Quaid-i-Azam University, 10/2018-03/2019)


Statistical Modelling Society (SMS)

  • I am Treasurer of the Statistical Modelling Society (since January 2019) Webpage
  • I was a member of the Executive Committee of the Statistical Modelling Society (2019-2022) Webpage
  • I was chair of the 32nd International Workshop on Statistical Modelling (3-7 July 2017, Groningen, Netherlands) Webpage


Associate Editorships

  • I am an Associate Editor of Computational Statistics (since January 2015) Webpage
  • I am an Associate Editor of the Journal of Applied Statistics (since March 2017) Webpage
  • I am an Associate Editor of Statistica Neerlandica (since May 2023) Webpage


Other involvements:

  • I am member of the Representative Council (Dutch region) of the International Biometrical Society (IBS) (since January 2018) Webpage
  • I am am member of the Board of Examiners for Mathematics and Applied Mathematics of Groningen University (since 09/2020)
  • I was a member of the Admission Board Mathematics and Applied Mathematics of Groningen University (10/2017-02/2024)
  • I was a member of the Ethics Committee of the Bernoulli Institute (05/2021-02/2024)
  • I was guest editor of a special issue on Statistical Modelling of Statistica Neerlandica Webpage Editorial
  • I participated in NWO assessment panels: VIDI-2020, VIDI-2021 and M-2022/23.
  • I am member of DSSC (Data Science & System Complexity), Groningen University Webpage
  • I was awarded the 'Basis Kwalificatie Onderwijs' (University Teaching Qualification), 2017, Groningen University.
  • I was awarded a Fellowship 'Innovation of Teaching' (FIT), 2017, Groningen University.Webpage
  • I participated in COST Action CA15109 -- European Cooperation for Statistics of Network Data Science (COSTNET) [2016-2020]


Former Grants,

Research grant: "Development of new Bayesian network models for systems biology research" funded by German Research Foundation (DFG), 2011-2014 Webpage


Former Webpage at TU Dortmund University


Theses

  • Habilitation (Venia Legendi for Statistics), Department of Statistics, TU Dortmund University, Title of thesis: "Bayesian Networks for Reconstructing Gene Regulatory Networks in Systems Biology" (6/2012). Available here
  • Promotion (Dr. rer. nat.), Department of Statistics, Dortmund University, Title of doctoral thesis: "Comparative evaluation of different Graphical Models for the Analysis of Gene Expression Data." (8/2006). Available here
  • Diplom in Statistics with special focus on Biometrics ("Studienschwerpunkt: Biometrie"), Department of Statistics, Dortmund University, Title of Diplom thesis: "Auswertung einer Brustkrebsstudie mit logistischen Regressionsmodellen" (2/2003). Available here


Journal Publications

  • Grzegorczyk, M. (2025): Being Bayesian about learning Bayesian networks from hybrid data, International Journal of Approximate Reasoning, 187, article 109549. Available here
  • Grzegorczyk, M. (2024): Being Bayesian about learning Bayesian networks from ordinal data, International Journal of Approximate Reasoning, 170, article 109205. Available here
  • Salam, A. and Grzegorczyk, M. (2024): Learning the structure of the mTOR protein signalling pathway from protein phosphorylation data, Journal of Applied Statistics, 51(5), 845-865. Available here
  • Grzegorczyk, M. (2023): Being Bayesian about learning Gaussian Bayesian networks from incomplete data, International Journal of Approximate Reasoning, 160, article 108954. Available here
  • Del Core, L., Pellin, D., Wit, E.C., and Grzegorczyk, M. (2023): Scalable inference of cell differentiation networks in gene therapy clonal tracking studies of haematopoiesis, Bioinformatics, 39(10), article btad605. Available here
  • Del Core, L., Pellin, D., Wit, E.C., and Grzegorczyk, M. (2023): A mixed-effects stochastic model reveals clonal dominance in gene therapy safety studies, BMC Bioinformatics, 24, article number 228. Available here
  • Salam, A. and Grzegorczyk, M. (2023): Model averaging for sparse seemingly unrelated regression using Bayesian networks among the errors, Computational Statistics, 38, 779-808. Available here
  • Van Oppen, Y., Milder-Mulderij, G., Brochard, C., Wiggers, R., de Vries, S., Krijnen, W., and Grzegorczyk, M.A. (2023): Modeling dragonfly population data with a Bayesian bivariate geometric mixed-effects model, Journal of Applied Statistics, 50 (10), 2171-2193. Available here
  • Bernal, V., Soancatl-Aguilar, V., Bulthuis, J., Guryev, V., Horvatovich, P., and Grzegorczyk, M. (2022): GeneNetTools: Tests for Gaussian graphical models with shrinkage, Bioinformatics, 38 (22), 5049-5054. Available Here
  • Del Core, L., Cesana, D., Gallina, P., Serina Secanechia, Y.N., Rudilosso, L., Montini, E., Wit, E.J.C., Calabria, A. and Grzegorczyk, M.A. (2022): Normalization of clonal diversity in gene therapy studies using shape constrained splines. Scientific Reports, 12:3836. Available here
  • Bernal, V., Bischoff, R., Horvatovich, P., Guryev, V. and Grzegorczyk, M. (2021): The `Un-Shrunk' Partial Correlation in Gaussian Graphical Models. BMC Bioinformatics, 22, article number 424. Available here
  • Shafiee Kamalabad, M. and Grzegorczyk, M. (2021): A new Bayesian piecewise linear regression model for dynamic network reconstruction. BMC Bioinformatics, 22 (Supplement 2), article number 196. Available here
  • Shafiee Kamalabad, M. and Grzegorczyk, M. (2020): Non-homogeneous dynamic Bayesian networks with edge-wise sequentially coupled parameters. Bioinformatics, 36 (4), 1198-1207. Available here
  • Bernal, V., Bischoff, R., Guryev, V., Grzegorczyk, M. and Horvatovich, P. (2019): Exact hypothesis testing for shrinkage based Gaussian Graphical Models. Bioinformatics, 35 (23), 5011–5017. Available here
  • Imkamp, K., Bernal, V., Grzegorczyk, M., Horvatovich, P., Vermeulen, C.J., Heijink, I.H., Guryev, V., Kerstjens, H.A.M., van den Berge, M. and Faiz, A. (2019): Gene network approach reveals co-expression patterns in nasal and bronchial epithelium. Scientific Reports, 9, article number: 15835. Available here
  • Shafiee Kamalabad, M., Heberle, A.M., Thedieck, K. and Grzegorczyk, M. (2019): Partially non-homogeneous dynamic Bayesian networks based on Bayesian regression models with partitioned design matrices. Bioinformatics, 35 (12), 2108–2117. Available here
  • Shafiee Kamalabad, M. and Grzegorczyk, M. (2018): Improving nonhomogeneous dynamic Bayesian networks with sequentially coupled parameters. Statistica Neerlandica, 72 (3), 281-305. Available here
  • Grzegorczyk, M., Aderhold, A., and Husmeier, D. (2017): Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration. Computational Statistics, 32 (2), 717-761. Available here
  • Aderhold, A., Husmeier, D., and Grzegorczyk, M. (2017): Approximate Bayesian inference in semi-mechanistic models. Statistics and Computing, 27 (4), 1003-1040. Available here
  • Grzegorczyk, M. and Shafiee Kamalabad, M. (2016): Comparative evaluation of various frequentist and Bayesian non-homogeneous Poisson counting models, Computational Statistics, 32 (1), 1-33. Available here
  • Grzegorczyk, M. (2016): A non-homogeneous dynamic Bayesian network with a hidden Markov model dependency structure among the temporal data points. Machine Learning, vol. 102 (2), 155-207. Available here
  • Grzegorczyk, M., Aderhold, A., and Husmeier, D. (2015): Inferring bi-directional interactions between circadian clock genes and metabolism with model ensembles. Statistical Applications in Genetics and Molecular Biology (SAGMB), vol. 14 (2), 143-167. Weblink
  • Aderhold, A., Husmeier, D., and Grzegorczyk, M. (2014): Statistical inference of circadian regulatory networks. Statistical Applications in Genetics and Molecular Biology (SAGMB), vol. 13 (3), 227-273. Weblink
  • Grzegorczyk, M. and Husmeier, D. (2013): Regularization of Non-Homogeneous Dynamic Bayesian Networks with Global Information-Coupling based on Hierarchical Bayesian models. Machine Learning, vol. 91 (1), 105-154. Weblink
  • Grzegorczyk, M. and Husmeier, D. (2012): Bayesian regularization of non-homogeneous dynamic Bayesian networks by globally coupling interaction parameters. Journal of Machine Learning Research (JMLR) Workshop and Conference Proceedings (AISTATS2012), 22, 467-476. Download PDF
  • Grzegorczyk, M. and Husmeier, D. (2012): A non-homogeneous dynamic Bayesian network model with sequentially coupled interaction parameters for applications in systems and synthetic biology. Statistical Applications in Genetics and Molecular Biology (SAGMB), vol. 11 (4), Article 7. Weblink
  • Marbach, D., Costello J.C., Küffner, R., Vega, N.M., Prill, P.J., Camacho, D.M., Allison, K.R., The DREAM5 Consortium, Kellis, M., Collins, J.J., and Stolovitzky, G. (2012): Wisdom of crowds for robust gene network inference. Nature Methods, 9, 796-804. Weblink
  • Grzegorczyk, M. and Husmeier, D. (2011): Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes. Bioinformatics, 27 (5), 693-699. Weblink
  • Grzegorczyk, M. and Husmeier, D. (2011): Non-homogeneous dynamic Bayesian networks for continuous data. Machine Learning, 83 (3), 355-419. Weblink
  • Grzegorczyk, M., Husmeier, D., and Rahnenführer, J. (2011): Modelling non-stationary dynamic gene regulatory processes with the BGM model. Computational Statistics, 26 (2), 199-218. Weblink
  • Grzegorczyk, M., Husmeier, D. and Rahnenführer, J. (2010): Modelling Non-Stationary Gene Regulatory Processes. Advances in Bioinformatics, Volume 2010, Article ID 749848. Weblink
  • Grzegorczyk, M. and Husmeier, D. (2009): Non-stationary continuous dynamic Bayesian networks. In Bengio, Schuurmans, Lafferty, Williams and Culotta (editors) Proceedings of the Twenty-Third Annual Conference on Neural Information Processing Systems, NIPS 2009, Curran Associates, ISBN 9781605603520, 682-690. Weblink
  • Grzegorczyk, M., Husmeier, D., Edwards, D.E., Ghazal, P. and Millar, A.J. (2008): Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler. Bioinformatics, 24 (18), 2071-2078. Weblink
  • Grzegorczyk, M. and Husmeier, D. (2008): Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. Machine Learning, 71 (2-3), 265-305. Weblink
  • Grzegorczyk, M. (2008): Comparison of two different stochastic models for extracting protein regulatory pathways with Bayesian networks. Journal of Toxicology and Environmental Health, Part A, 71, 780–787. Weblink
  • Grzegorczyk, M. (2007): Extracting protein regulatory networks with graphical models. Proteomics, 7, 51-59. Weblink
  • Urfer, W., Grzegorczyk, M. and Jung, K. (2006): Statistics for Proteomics: A review of tools for analyzing experimental data. Proteomics, 6, 48-55. Weblink
  • Werhli, A.V., Grzegorczyk, M. and Husmeier, D. (2006): Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks. Bioinformatics, 22 (20), 2523-2531. Weblink


Conference Proceedings Publications'

  • Grzegorczyk, M. (2025): A New Bayesian Approach to Learning Hybrid Bayesian Networks. Proceedings of the International Workshop on Statistical Modelling (IWSM2025), 223-228, Limerick, Ireland. Download Proceedings PDF
  • Grzegorczyk, M. (2024): Learning Bayesian Networks from Ordinal Data - The Bayesian Way. In: Einbeck, J., Maeng, H., Ogundimu, E., Perrakis, K. (eds) Developments in Statistical Modelling, IWSM 2024 in Durham (UK), Contributions to Statistics, pp 7-13. Springer, Cham. Webpage
  • Hill, A., Groefsema, M., Sabatelli, M., Carloni, R. and Grzegorczyk, M. (2024): Contextual Online Imitation Learning (COIL): Using Guide Policies in Reinforcement Learning, In Proceedings of the 16th International Conference on Agents and Artificial Intelligence (Volume 3: ICAART), 178-185, SciTPress, Rome, Italy. Available here
  • Grzegorczyk, M. (2023): Learning Gaussian Bayesian networks from incomplete data - The Bayesian way. Proceedings of the International Workshop on Statistical Modelling (IWSM2023), 445-450, Dortmund, Germany. Download Proceedings PDF
  • Salam, A. and Grzegorczyk, M. (2022): Learning the structure of the mTOR pathway. Proceedings of the International Workshop on Statistical Modelling (IWSM2022), 305-310, Trieste, Italy. Download Proceedings PDF
  • Salam, A. and Grzegorczyk, M. (2022): A new non-homogeneous Bayesian network with globally coupled interaction parameters. Proceedings of the International Workshop on Statistical Modelling (IWSM2022), 567-572, Trieste, Italy. Download Proceedings PDF
  • Shafiee Kamalabad, M. and Grzegorczyk, M. (2020): A new partially segment-wise coupled piece-wise linear regression model for statistical network structure inference. In M. Raposo et al. (eds.) Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2018), Revised Selected Papers, Lecture Notes in Bioinformatics, Springer, 139-152. Webpage
  • Bernal, V., Guryev, V., Bischoff, R., Horvatovich, P., and Grzegorczyk, M. (2020): Correction for the shrinkage effect in Gaussian graphical models. Proceedings of the International Workshop on Statistical Modelling (IWSM2020), 281-284. Bilbao, Spain. Webpage
  • Bernal, V., Guryev, V., Bischoff, R., Horvatovich, P., and Grzegorczyk, M. (2020): Uncertainty propagation in shrinkage-based partial correlations. Proceedings of the International Workshop on Statistical Modelling (IWSM2020), 285-288. Bilbao, Spain. Webpage
  • Shafiee Kamalabad, M. and Grzegorczyk, M. (2018): Non-homogeneous dynamic Bayesian networks with edge-wise coupled parameters. Proceedings of the International Workshop on Statistical Modelling (IWSM2018), vol. 1, 270-275, Bristol, England. Webpage
  • Shafiee Kamalabad, M. and Grzegorczyk, M. (2017): A sequentially coupled non-homogeneous dynamic Bayesian network model with segment-specific coupling strengths. Proceedings of the International Workshop on Statistical Modelling (IWSM2017), vol. 1, 173-178, Groningen, Netherlands. Webpage
  • Shafiee Kamalabad, M. and Grzegorczyk, M. (2016): A non-homogeneous dynamic Bayesian network model with partially sequentially coupled network parameters. Proceedings of the International Workshop on Statistical Modelling (IWSM2016), vol. 1, 139-144, Rennes, France. Webpage IWSM2016
  • Grzegorczyk, M., Aderhold, A., and Husmeier, D. (2015): Network reconstruction with realistic models. In Friel and Wagner (eds.), Proceedings of the International Workshop on Statistical Modelling (IWSM2015), vol. 1, 204-209, Linz, Austria. Webpage IWSM2015
  • Grzegorczyk, M., Aderhold, A., Smith, V.A., and Husmeier, D. (2014): Inference of circadian regulatory networks. Proceedings of the International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO) 2014, Granada, Spain, 1001-1014. Download PDF
  • Aderhold, A., Husmeier, D., Smith, A.V., Millar, A.J., and Grzegorczyk, M. (2013): Assessement of regression methods for inference of regulatory networks involved in circadian regulation. Proceedings of the Tenth International Workshop on Computational Systems Biology, WCSB 2013, Tampere, Finland, 29-33. Download Proceedings PDF
  • Grzegorczyk, M. (2012): Modelling regulatory processes during morphogenesis in Drosophila melanogaster with an improved version of the BGMD dynamic Bayesian network model. Proceedings of the Nineth International Conference on Computational Systems Biology, WCSB 2012, Ulm, Germany, 27-30. Download Proceedings PDF
  • Dondelinger, F., Aderhold, A., Lèbre, S., Grzegorczyk, M., and Husmeier, D. (2011): A Bayesian regression and multiple changepoint model for systems biology. In Conesa, D. et al. (editors) Proceedings of the 26th International Workshop on Statistical Modelling (IWSM2011), Copiformes S.L., Valencia, ISBN 978-84-694-5129-8, 189-194. Proceedings PDF
  • Ickstadt, K., Bornkamp, B., Grzegorczyk, M., Wieczorek, J., Sheriff, M.R., Grecco, H.E. and Zamir, E. (2010): Nonparametric Bayesian Networks. In: Bernardo, Bayarri, Berger, Dawid, Heckerman, Smith and West (eds.): Bayesian Statistics 9, Oxford University Press, 283-316. Download PDF
  • Lohr, M., Godoy, P., Hengstler, J.G., Rahnenführer, J. and Grzegorczyk, M. (2010): In: Nykter, Ruusuvuori, Carlberg and Yli-Harja (eds.) Extracting differential regulatory sub-networks from genome-wide microarray expression data. Proceedings of the Seventh International Workshop on Computational Systems Biology, WCSB 2010, Luxembourg, 63-66. Download Proceedings PDF
  • Grzegorczyk, M. and Husmeier, D. (2009): Avoiding Spurious Feedback Loops in the Reconstruction of Gene Regulatory Networks with Dynamic Bayesian Networks. In: V. Kadirkamanathan et al. (eds.): Pattern Recognition in Bioinformatics, Lecture Notes in Bioinformatics, Springer-Verlag, Berlin Heidelberg, 113-124. Weblink
  • Grzegorczyk, M. and Husmeier, D. (2009): Modelling non-stationary gene regulatory processes with a non-homogeneous dynamic Bayesian network and the change point process. In: Manninen et al. (eds.) Proceedings of the Sixth International Workshop on Computational Systems Biology, WCSB 2009, Aarhus, Denmark, 51-54. Download Proceedings PDF
  • Werhli, A.V., Grzegorczyk, M., Chiang, M.T. and Husmeier, D. (2006): Improved Gibbs sampling for detecting mosaic structures in DNA sequence alignments In: Urfer and Turkman (eds.) Statistics in Genomics and Proteomics, Centro Internacional de Matematica, Coimbra. [no link]


Book Chapter Publications

  • Grzegorczyk, M. and Husmeier, D. (2019): Chapter 32: Modelling Non-homogeneous Dynamic Bayesian Networks with Piecewise Linear Regression Models. In: Balding, D.J., Moltke, I., Marioni J. (editors), Handbook of Statistical Genetics, 4th edition, volume2, pages 899-931, John Wiley & Sons. Webpage
  • Grzegorczyk, M., Aderhold, A. and Husmeier, D. (2019): Overview and Evaluation of Recent Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data. In: Sanguinetti, G. and Huynh-Thu, V. (editors), Gene Regulatory Networks. Methods in Molecular Biology, vol. 1883, pages 49-94, Humana Press, New York, NY. Webpage
  • Husmeier, D., Werhli, A.V., and Grzegorczyk, M. (2011): Chapter 13: Advanced Applications of Bayesian networks in Systems Biology. In D.J. Balding et al. (editors): Handbook of Statistical Systems Biology, 270-289, Wiley, Chichester, UK. Weblink
  • Grzegorczyk, M. (2010): An Introduction to Gaussian Bayesian Networks. In: Yan Qing (Ed.): Systems Biology in Drug Discovery and Development (Springer Series: Methods and Protocols: Methods in Molecular Biology, Vol. 662). Humana Press. ISBN 978-1-60761-799-0. Weblink
  • Grzegorczyk, M., Husmeier, D. and Werhli, A.V. (2008) Reverse Engineering Gene Regulatory Networks with various Machine Learning Methods. In Emmert-Streib and Dehmer (eds.) Analysis of Microarray Data. A Network-Based Approach, Wiley-WCH. Weblink


More Journal Publications

  • Masjkur, M., Saefuddin, A., Mangku, I.W., Folmer, H., van der Vlist, A.J., and Grzegorczyk, M. (2025): Bias correction methods for spatially lagged covariates measured with errors, Spatial Statistics, 68, article 100909. Webpage
  • Been, R.A., van Helsdingen, M.R.X., Zammit, D., Grzegorczyk, M., Krijnen, W.P., Tamasi, K., Gans, R.O.B., van Beek, A.P., and van Dijk, P.R. (2025): The efficacy of intermittent scanning continuous glucose monitoring in the elderly: A case–control study, Diabetes, Obesity and Metabolism, 27(7), 3882-3890. Available here
  • Ahmadi Yazdi, A., Shafiee Kamalabad, M., Oberski, D. and Grzegorczyk, M. (2024): Bayesian multivariate control charts for multivariate profiles monitoring, Quality Technology & Quantitative Management, 21(3), 386-421. Webpage
  • Sidorenkov, G., Vonk J.M., Grzegorczyk M., Cortés-Ibañez F.O., de Bock G.H. (2023): Factors associated with SARS-COV-2 positive test in Lifelines, PLOS ONE, 18(11), article e0294556 Available here
  • Flores-Guerrero, J.L., Grzegorczyk, M.A., Connelly, M.A., Garcia, E., Navis, G., Dullaart, R.P.F. and Bakker, S.J.L. (2021): Mahalanobis distance, a novel statistical proxy of homeostasis loss is longitudinally associated with risk of type 2 diabetes, EBioMedicine, 71, article ID 103550 Available here
  • Qamar, S., Khalique, A. and Grzegorczyk, M. (2021): On the Bayesian network based data mining framework for the choice of appropriate time scale for regional analysis of drought Hazard, Theoretical and Applied Climatology, 143, 1677-1695. weblink
  • Ali, Z., Hussain, I., Grzegorczyk, M., Ni, G., Faisal, M., Qamar, S., Shoukry, A.M., Wahab Sharkawy, M. , Gani, S., and Al-Deek, F. (2020): Bayesian network based procedure for regional drought monitoring: The Seasonally Combinative Regional Drought Indicator. Journal of Environmental Management, 276, article: 111296. weblink
  • Ali, Z., Hussain, I., Faisal, M., Grzegorczyk, M., Qamar, S., Shoukry, A.M., Warhab Sharkawy, M., and Gani, S. (2020): On the more generalized non?parametric framework for the propagation of uncertainty in drought monitoring. Meteorological Applications, 27 (3), DOI: 10.1002/met.1914 Available here
  • Ali, Z., Hussain, I., Faisal, M., Grzegorczyk, M., Almanjahie, I.M., Nazeer, A. and Ahmad, I. (2020): Characterization of regional hydrological drought using improved precipitation records under multi-auxiliary information. Theoretical and Applied Climatology, 140, 25-36. Weblink
  • Kaufmann, T., Castela Forte, J., Hiemstra, B., Wiering, M.A., Grzegorczyk, M., Epema, A.H. and van den Horst, I.C.C. (2019): A Bayesian network analysis of the diagnostic process and its accuracy to determine how clinicians estimate cardiac function in critically ill patients: Prospective observational cohort study. JMIR Medical Informatics, 7(4), online article. Available here
  • Ahmadi Yazdi, A., Zeinal Hamadani, A., Amiri, A. and Grzegorczyk, M. (2019): A New Bayesian Multivariate Exponentially Weighted Moving Average Control Chart for Phase II Monitoring of Multivariate Multiple Linear Profiles. Quality and Reliability Engineering International, 35 (7), 2152-2177. Webpage
  • Ali, Z., Hussain, I., Faisal, M., Almanjahie, I.M., Ahmad, I., Kahn, D.M., Grzegorczyk, M., and Quamar, S. (2019): A Probabilistic Weighted Joint Aggregative Drought Index (PWJADI) criterion for drought monitoring systems. Tellus A: Dynamic Meteorology and Oceanography, 71 (1), DOI: 10.1080/16000870.2019.1588584 Weblink
  • Sievers, S., Fritsch, C., Grzegorczyk, M., Kuhnen, C. and Müller, O. (2006): Absolute ß-catenin concentrations in Wnt pathway stimulated and non-stimulated cells. Biomarkers, 11 (3), 270-278. Weblink
  • Kutzner, N., Hoffmann, I., Linke, C., Thienel, T. and Grzegorczyk, M., Urfer, W., Martin, D., Winde, G., Traska, T., Hohlbach, G., Müller, K.-M., Kuhnen, C. and Müller, O. (2005): Non-invasive detection of colorectal tumours by the combined application of molecular diagnosis and the faecal occult blood test. Cancer Letters, 1, 33-41. Weblink


Non-peer-reviewed paper contributions (since December 2013)

  • The Honour's College Consortium 2024/2025 and Grzegorczyk, M. (2025): The Potato Chips Experiment Periodiek, no. 2025-2, 14-17. Weblink
  • The Honour's College Consortium 2023/2024 and Grzegorczyk, M. (2024): Speak Dutch or go home? Periodiek, no. 2024-2, 16-20. Weblink
  • Grzegorczyk, M. and the Honour's College Consortium 2022/2023 (2023): Humans vs. ChatGPT. Periodiek, no. 2023-1, 16-19. Weblink
  • Grzegorczyk, M. and the Honour's College Consortium 2021/2022 (2023): The Cola Experiment. Periodiek, no. 2023-1, 5-9. Weblink
  • Grzegorczyk, M., Krijnen, W., and van Oppen, Y. (2022): Statistical analysis of the green hawker dragonfly populations in the Northern Netherlands. Periodiek, no. 2022-1, 20-23. Weblink
  • Bernal, V., Horvatovich, P., Guryev, V. and Grzegorczyk, M. (2019): COPD, Statistics, and Network Inference. Periodiek, no. 2019-3, 9-11. Weblink
  • Grzegorczyk, M., Shafiee Kamalabad, M., Bernal, V., Barafas, S. and Del Core, L. (2018): Supporting the Life Sciences with Statistics. Periodiek, no. 2018-3, 8-10. Weblink
  • short contribution in the section: `New at the Institute' of the Bernoulli Gazet, 2016 Weblink


(Co-)Supervised PhD theses

  • Del Core, L. (2023): The stochastic route of haematopoiesis: modelling and inference methods in clonal tracking studies. Weblink
  • Salam, A. (2022): Advanced Bayesian regression and dynamic network modelling, PhD thesis, Groningen University. Weblink
  • Bernal, V. (2022): Improved reconstruction of molecular networks with Gaussian graphical models, PhD thesis, Groningen University. Weblink
  • Balafas, S. (2022): The Links in our Mind: Multivariate Statistical Methods for Psychological Data, PhD thesis, Groningen University. Weblink
  • Shafiee Kamalabad, M. (2019): Advanced non-homogeneous dynamic Bayesian network models for statistical analyses of time series data, PhD thesis, Groningen University. Weblink


Teaching at Groningen University (since December 2013)

  • Statistical Reasoning, BSc [2023/2024] scheduled
  • Statistical Genomics, MSc [2025/2026]
  • Statistics, BSc [2025/2026]
  • Student Colloquium, MSc [2024/2025]
  • Honour's College (on Practical Statistical Hypothesis Testing), BSc [2024/2025]
  • Contemporary Statistics with Applications, MSc [2024/2025]
  • Statistics, BSc [2024/2025]
  • Honour's College (on Practical Statistical Hypothesis Testing), BSc [2023/2024]
  • Statistical Reasoning, BSc [2023/2024]
  • Contemporary Statistics with Applications, MSc [2023/2024]
  • Statistics, BSc [2023/2024]
  • Honour's College (on Practical Statistical Hypothesis Testing), BSc [2022/2023]
  • Statistical Modelling, BSc [2022/2023], 50%
  • Contemporary Statistics with Applications, MSc [2022/2023]
  • Statistics, BSc [2022/2023]
  • Honour's College (on Practical Statistical Hypothesis Testing), BSc [2021/2022]
  • Statistical Reasoning, BSc [2021/2022]
  • Statistics, BSc [2021/2022]
  • Honour's College (on Practical Statistical Hypothesis Testing), BSc [2020/2021]
  • Contemporary Statistics with Applications, MSc [2020/2021]
  • Statistical Reasoning, BSc [2020/2021]
  • Statistics, BSc [2020/2021]
  • Honour's College (on Bayesian networks), BSc [2019/2020]
  • Statistical Reasoning, BSc [2019/2020]
  • Statistical Genomics, MSc [2019/2020]
  • Statistics, BSc [2019/2020]
  • Honour's College (on Bayesian networks), BSc [2018/2019]
  • Contemporary Statistics with Applications, MSc [2018/2019]
  • Statistical Reasoning, BSc [2018/2019]
  • Statistics, BSc [2018/2019]
  • Statistical Consulting, MSc (co-teacher) [2017/2018]
  • Honour's College (on Bayesian networks), BSc [2017/2018]
  • Statistical Genomics, MSc [2017/2018]
  • Linear Algebra and Multivariable Calculus for IEM, BSc [2017/2018]
  • Statistical Reasoning, BSc [2017/2018]
  • Honour's College (on Bayesian networks), BSc [2016/2017]
  • Contemporary Statistics with Applications, MSc [2016/2017]
  • Linear Algebra and Multivariable Calculus for IEM, BSc [2016/2017]
  • Statistical Reasoning, BSc [2016/2017]
  • Honour's College (on Bayesian networks), BSc [2015/2016]
  • Linear Algebra and Multivariable Calculus for IEM, BSc [2015/2016]
  • Statistical Genomics, MSc [2015/2016]
  • Statistical Reasoning, BSc [2015/2016]
  • Contemporary Statistics with Applications, MSc [2014/2015]
  • Statistical Reasoning, BSc [2014/2015]
  • Honour's College (on Graphical Models), BSc [2013/2014]


Supervised Master projects at Groningen University (23)

  • Andrew Packer (2025), Empirical investigation of the robustness of statistical survival analysis, MSc thesis, Applied Mathematics, Groningen University.
  • Cornelius S.L. Rook (2025), Dynamic Bayesian Networks for Business Data, MSc thesis, Applied Mathematics, Groningen University.
  • Dewi Davies-Batista (2025), Hybrid Continuous Mixtures of Probabilistic Circuits, MSc thesis, Applied Mathematics, Groningen University.
  • Radovan Rusnak (2025): Bayesian Coupled Scheme for Network Modelling with Application to the RAF Signalling Pathway, MSc thesis, Applied Mathematics, Groningen University.
  • Klaas-Daniel Sijtsma (2025): Gibbs Stochastic Variational Inference, MSc thesis, Mathematics, Groningen University.
  • Leah Dijkshoorn (2024): Enhancing Aortic Valve Diameter Prediction: Accounting for Demographic Variability and Measurement Techniques, MSc thesis, Mathematics, Groningen University.
  • Jurre Hoekstra (2024): Time Series Forecasting with ARIMA-GARCH and Neural Networks, Using Frequentist and Bayesian Frameworks, MSc thesis, Applied Mathematics, Groningen University.
  • Hanna Kern (2024): Net Benefit for Uncalibrated Models, MSc thesis, Mathematics, Groningen University.
  • Tom Schoemaker (2023): An Evaluation of Propensity Score Methods, MSc thesis, Mathematics, Groningen University.
  • Lotvi Kriouar (2023): Learning the Kalman Filter Parameters: an Expectation-Maximization Approach, MSc thesis, Applied Mathematics, Groningen University.
  • Alexander Hill (2022): Contextual Online Imitation Learning (COIL): A Novel Method of Utilising Guide Policies in Reinforcement Learning, MSc thesis, Mathematics, Groningen University.
  • Boris Luttikhuizen (2021): Information Coupling in Bayesian Networks, MSc thesis, Mathematics, Groningen University.
  • Loes Deuling (2021): Prior Information Integration for Bayesian Networks, MSc thesis, Mathematics, Groningen University.
  • Moschanthi Korre (2021): A new Bayesian network model for learning static and dynamic interactions from temporal data, MSc thesis, Mathematics, Groningen University.
  • Yulan van Oppen (2020): A Bayesian bivariate response mixed-effects model for zero-inflated count data containing large outliers, MSc thesis: Mathematics, Groningen University.
  • ChengLong Deng (2020): Elastic Net prediction on high dimensional macroeconomic data, MSc thesis: Econometrics, Groningen University.
  • Carlos Rodolfo Huerta Santiago (2020): Advanced Bayesian network models, MSc thesis: Mathematics, Groningen University.
  • Eline Pasch (2020): Girls Perform Better at School - Statistical Facts, Figures and Fallacies in Media Claims, MSc thesis: Science Education and Communication, Groningen University.
  • David Langbroek (2019): Homogeneous and non-homogeneous dynamic Bayesian network models, MSc thesis: Mathematics, Groningen University.
  • Dagmar Heeg (2019): Evaluation of a General Bayesian Method using the Cholesky Decomposition to Model the Transmission of Intelligence, MSc thesis: Science Education and Communication, Groningen University.
  • Dennis Steenhuis (2017): On the Comparison of Changepoints Models in Network Reconstruction with Themselves and their Bayesian Counterparts. MSc thesis: Mathematics, Groningen University.
  • Gerard Hekkelman (2017): Comparing Graphical Models with Pseudo-likelihood Methods and their Bayesian Counterparts on Gene Regulatory Networks. MSc thesis: Mathematics, Groningen University.
  • Martin Eisenmann (2016): Statistical Support of Ageing Research at the UMCG. MSc thesis: Mathematics, Groningen University.


Supervised Bachelor projects at Groningen University (41)

  • Jelmer Wieringa (2025): Bayesian Quadrature: Theory and Comparative Simulation Study, BSc thesis, Mathematics, Groningen University.
  • Avram Zanana (2025): A Comparison of Frequentist and Bayesian Approaches to Variable Selection in Logistic Regression for Heart Disease, BSc thesis, Mathematics, Groningen University.
  • Maria Cuder (2025): Exploring Prosperity through Bayesian Networks: A Data-Driven Analysis of the Legatum Index, BSc thesis, Mathematics, Groningen University.
  • Sandor Polgar (2025): Investigating mathematical models for electronic recoil background in the XENONnT experiment, BSc thesis, double degree Mathematics and Physics, Groningen University. (co-supervised with Jelle Aalbers)
  • Veerle Wetzels (2024): Comparative analysis of convergence diagnostics for Markov chain Monte Carlo, BSc thesis, Mathematics, Groningen University.
  • Jovan Andreevski (2024): Bayesian Network Structure Learning with a Focus on Networks with Background Information and Incomplete Information, BSc thesis, Mathematics, Groningen University.
  • Jorge Duro Garijo (2024): Comparison of Variable Selection Techniques for Linear Regression, BSc thesis, Applied Mathematics, Groningen University.
  • Maria Stulenkova (2024): Analysing key factors affecting longevity of film’s screening in cinemas, BSc thesis, Mathematics, Groningen University.
  • Jelmer Dijkstra (2023): Comparing goals and expected goals for modelling football match results, BSc thesis, Mathematics, Groningen University.
  • Mohammad Siam Shahkhan (2023): The Impact of Class-based noise on Bayesian and Frequentist Logistic Regression, BSc thesis, Mathematics, Groningen University.
  • Leana Kalve (2023): A Comparative Analysis of Regression Models for Global Health Prediction, BSc thesis, Mathematics, Groningen University.
  • Lesley Munyaneza (2023): Regression Modelling in Survival Analysis,BSc thesis, Mathematics, Groningen University.
  • Isabel Klennert (2022): Gaussian Graphical Models - A High Dimensional Problem, BSc thesis, Applied Mathematics, Groningen University.
  • Elena C. Gilland (2022): Statistical Analysis of Bat Data, BSc thesis, Applied Mathematics, Groningen University.
  • Sijia Lu (2022): Statistical Analysis of Grade Data based on ANOVA and Linear Regression Model, BSc thesis, Mathematics, Groningen University.
  • Camilla I.N. Costa (2021): Gaussian processes for prediction with a cross-comparison to cubic splines, BSc thesis, Mathematics, Groningen University.
  • Leah Dijkshoorn (2021): A comparative methodology study: using Generalised Additive Models to investigate how indicators impact risk of mortality after heart failure in different sexes, BSc thesis, Mathematics, Groningen University.
  • Herbert Nieweg (2021): How accurate can we predict match outcome based on match state, BSc thesis, Mathematics, Groningen University.
  • Oskar D.J. Kolkman (2021): A Comparative analysis of Bayesian and Frequentist approaches to linear regression, BSc thesis, Mathematics, Groningen University.
  • Hessel Beijaard (2021): Recommender Systems with Naive Bayes and Tree-Augmented Naive Bayes, BSc thesis, Mathematics, Groningen University.
  • Arend-Jan Tissing (2021): A sample size based comparison of the frequentist and Bayesian logistic regression, BSc thesis, Mathematics, Groningen University.
  • Sarah Jane Greengrass (2021): An investigation into the impact of temperature on the rise and spread of COVID-19 in the Netherlands, BSc thesis, Mathematics, Groningen University.
  • Naomi Broersma (2021): Comparison of Random Walk Metropolis and Hamiltonian Monte Carlo, BSc thesis, Mathematics, Groningen University.
  • Tom Schoemaker (2020): Comparative analysis of Frequentist and Bayesian Statistics using simulated count data, BSc thesis, Mathematics, Groningen University.
  • Mihail Skurovskii (2020): Generalized Linear Models - Comparing Frequentist and Bayesian Approaches, BSc thesis: Mathematics, Groningen University.
  • Stephanie R. Ranft (2020): Bayesian networks and analysis with incomplete data, BSc thesis: Mathematics, Groningen University.
  • Simone M.R. Kockelkorn (2020): Bayesian Network of Spotify's audio features, BSc thesis: Mathematics, Groningen University.
  • Remco Bruinsma (2020): Using Poisson regression to model football scores and exploit inaccuracies in the online betting market, BSc thesis: Mathematics, Groningen University.
  • Wieger Schipper (2020): A comparative analysis of the graphical lasso and shrinkage based Gaussian graphical models, BSc thesis: Mathematics, Groningen University.
  • Alexander Hill (2020): Random Walks: The Properties, Applications and Methods of Analysis, BSc thesis: Mathematics, Groningen University.
  • Laureando Mattia Arsendi (2020): Statistical Modelling of Trajectories with Bezier curves, BSc thesis: Statistics, Padua University (Italy).
  • Carlo de Vos (2020): Markov Chain Monte Carlo based inference in logistic regression, BSc thesis: Mathematics, Groningen University.
  • Alexandra Lecka (2019): A Cross-Comparison of Cubic Splines and Gaussian Processes in Function Approximation, BSc thesis: Mathematics, Groningen University.
  • Li Voon Loke (2019): Performance Comparison of Control Charts, BSc thesis: Mathematics, Groningen University.
  • Kaisheng Zheng (2019): A Comparison of Bayesian and Classical Statistics in Analyzing Factors Affecting Life Expectancy in OECD Countries, BSc thesis: Econometrics and Operational Research, Groningen University.
  • Guanqun Ma (2019): Forecasting football match results with the ordered logit model, BSc thesis: Mathematics, Groningen University.
  • Gerlijn Nolle (2018): Modeling the Transmission of Intelligence within a Bayesian Framework, BSc thesis: Mathematics, Groningen University.
  • Sijbren Schotanus (2018): Cross-comparison of inference methods for Gaussian Graphical Models, BSc thesis: Mathematics, Groningen University.
  • Klaas-Daniel Sijtsma (2017): Bayesian Changepoint Models. BSc thesis: Mathematics, Groningen University.
  • Dagmar Heeg (2017): Optimization of the Bayesian Poisson Changepoint Model. BSc thesis: Mathematics, Groningen University.
  • Samuel Toby Knight (2015): An Inhomogeneous Bayesian Poisson-Gamma Model for Taxi Calls in New York City. BSc thesis: Econometrics and Operational Research, Groningen University.


Supervised Internship projects at Groningen University (17)

  • Leana Kalve (2025), Applied Math MSc Internship Project: `Profit and Loss Attribution under the Black–Scholes and Heston Models', at Rabobank, Utrecht.
  • Andrei R. Secuiu (2025), Applied Math MSc Internship Project: `Temporal and correlation analysis of zooplankton population abundance data in the Arctic', at Bureau Biota, Groningen.
  • Roberto Schinina (2025), Applied Math MSc Internship Project: `Spatial and environmental drivers of zooplankton communities in Svalbard', at Bureau Biota, Groningen.
  • Cornelius S.L. Rook (2025), Applied Math MSc Internship Project: `Dynamic Bayesian networks application and implementation', at Cumlaude.ai, Groningen.
  • Jelmer Dijkstra (2025): Applied Math MSc Internship Project: `Flexible Modeling of Biomarkers in Suspected Infection Patients', at UMCG, Groningen.
  • Radovan Rusnak (2025): Applied Math MSc Internship Project: `Modelling Impedance of Lithium-Ion Batteries in Electric Vehicles at Qbuzz', at Qbuzz, Groningen.
  • Sanjit Dasgupta (2024): Applied Math MSc Internship Project: `Zooplankton Dynamics in Arctic Ponds', at Bureau Biota Groningen, Groningen.
  • Darren Zammit (2024): Applied Math MSc Internship Project: `Glucose Sensor Usage among Elderly with Diabetes Mellitus', at Department of Endocrinology, University Medical Center Groningen (UMCG), Groningen.
  • Lotvi Kriouar (2024): Applied Math MSc Internship Project: `Statistical Analysis on the Noise Impact of Intersection Take-offs', at Schiphol Airport, Amsterdam.
  • Andrew Packer (2023): Applied Math MSc Internship Project: `Survival of Dental Restorations: UMCG 2009-2019', at Dentistry Clinics, University Medical Center Groningen (UMCG), Groningen.
  • Jurre Hoekstra (2023): Applied Math MSc Internship Project: `Performance drop detection of batteries at Qbuzz', at Qbuzz, Groningen.
  • Eline Hoexum (2023): Internship Project: `Paving the way for the safe introduction of automated vehicles to Europe', at RDW, Hoogkerk, as part of the Science, Business and Policy (SBP) Master program.
  • Pelle Jonasse (2021): Internship Project: `HLV determination.', at KPM, Amsterdam, as part of the Science, Business and Policy (SBP) Master program.
  • Mark Redeman (2019): Internship Project: `An Approach in Testing Algorithms.', at KPMG, Groningen, as part of the Business and Policy (SBP) Master program.
  • Tigran Airapetian (2019): Applied Math MSc Internship Project: `Bidding for Impact: Partner specific optimization on the Google Ads Platform.', at BelSimpel, Groningen.
  • Alida Wiersma (2019): Applied Math MSc Internship Project: `Statistical Learning Methods for environmental DNA.', at Witteveen+Bos, Deventer. 2nd supervisor from Statistics.
  • Vera Velt (2016): Internship Project: `Gene Driven Drug Discovery', at University Medical Center Groningen (UMCG), Groningen, as part of the Business and Policy (SBP) Master program.


Workshops and conferences with contributions (since December 2013)

  • International Joint Workshop of Artificial Intelligence for Healthcare (HC@AIxIA) and HYbrid Models for Coupling Deductive and Inductive ReAsoning (HYDRA): HC@AIxIA+HYDRA, Bologna, Italy, 25-26 October 2025 Weblink
  • 39th International Workshop on Statistical Modelling (IWSM), Limerick, Ireland, 13-18 July 2025 Weblink
  • 6th International Conference on Statistics: Theory and Applications (ICSTA), Barcelona, Spain, 19-21 August 2024 Weblink
  • 38th International Workshop on Statistical Modelling (IWSM), Durham, England, 14-19 July 2024 Weblink
  • 16th International Conference on Agents and Artificial Intelligence (ICAART 2024), Rome, Italy, 24-26 February 2024 Weblink
  • 37th International Workshop on Statistical Modelling (IWSM), Dortmund, Germany, 17-21 July 2023 Weblink
  • 36th International Workshop on Statistical Modelling (IWSM), Trieste, Italy, 17-22 July 2022 Weblink
  • Conference on Statistical Network Science 2019, European Cooperation for Statistics of Network Data Science (COSTNET), Bilbao, Spain, 9-11 October 2019 Weblink
  • 34th International Workshop on Statistical Modelling (IWSM), Guimaraes, Portugal, 7-12 July 2019 Weblink
  • DuPont symposium on Breeding Data: Statistical Advances in Modern Plant Breeding, Wageningen, Netherlands, 16 October 2018 Weblink
  • Conference on Statistical Network Science 2018, European Cooperation for Statistics of Network Data Science (COSTNET), Warsaw, Poland, 26-28 September 2018 Weblink
  • 15th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB), Caparica, Portugal, 6-8 September 2018 Weblink
  • 33rd International Workshop on Statistical Modelling (IWSM), Bristol, England, 15-20 July 2018 Weblink
  • COSTNET Networking Biostatistics meeting, Milano, Italy, 12-13 April 2018 Weblink
  • Conference on Statistical Network Science 2017, European Cooperation for Statistics of Network Data Science (COSTNET), Mallorca, Spain, 25-27 October 2017 Weblink
  • 32nd International Workshop on Statistical Modelling (IWSM), Groningen, Netherlands, 3-7 July 2017 Weblink
  • ICMS workshop: 'Learning graphical models in high dimensional settings', Edinburgh, Scotland, 4-7 April, 2017 Weblink
  • Workshop on Computational Models in Biology and Medicine, Hannover, Germany, 2-3 March 2017 Webpage
  • Statistics for Structures Seminar Series, Amsterdam, Netherlands, 4 November 2016 Weblink
  • Conference on Statistical Network Science 2016, European Cooperation for Statistics of Network Data Science (COSTNET), Ribno (Bled), Slovenia, 21-23 September 2016 Weblink
  • 13th Applied Statistics, International Conference, Ribno (Bled), Slovenia, 18-21 September 2016, Short Course on Bayesian networks Weblink
  • 31st International Workshop on Statistical Modelling (IWSM), Rennes, France, 4-8 July 2016 Weblink
  • 30th International Workshop on Statistical Modelling (IWSM), Linz, Austria, 6-10 July 2015 Weblink
  • Satelite meeting: 'Inferring dynamic genetic networks', International Biometric Society (IBS) Channel Network Conference, Nijmegen, Netherlands, 20-22 April 2015 Weblink
  • Van Dantzig Seminar Lecture Series, Amsterdam, Netherlands, 9 October 2014 Weblink
  • International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO), Granada, Spain, 7-9 April 2014 Weblink