Talks

Invited talks

• Wright, M. N. (2024). Generative machine learning in biostatistics. 70. Biometrisches Kolloquium. Lübeck, Germany.
• Wright, M. N. (2024). From explainable AI to generative modeling with tree-based machine learning. Institute of Medical Biometry and Statistics, University of Lübeck. Lübeck, Germany.
• Wright, M. N. (2023). From explainable AI to generative modeling with tree-based machine learning. Statistics and Econometrics Seminar, HU Berlin. Berlin, Germany.
• Wright, M. N. (2023). Interpretable machine learning. Begegnungszone: Statistical Physics and Machine Learning. Leipzig, Germany.
• Wright, M. N. (2022). Interpretable machine learning. School of Statistics and Actuarial Science, University of the Witwatersrand. Johannesburg, South Africa.
• Wright, M. N. (2022). Random forests: myths and facts. Statistical Computing 2022. Schloss Reisensburg, Günzburg, Germany.
• Wright, M. N. (2022). Random forests on high-dimensional data: From classification and survival analysis to generative modelling. Department of Statistics, TU Dortmund University. Dortmund, Germany.
• Wright, M. N. (2021). Genome-wide interpretable machine learning. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai. New York City, NY, USA (online presentation).
• Wright, M. N. (2021). Random forests: myths and facts. Interdisciplinary Colloquium “Statistical Methods in Empirical Research”. Berlin, Germany (online presentation).
• Wright, M. N. (2021). Machine learning for time to event data. ARTEMIS workshop: Artificial intelligence musculoskeletal disorders study. Kiel, Germany (online presentation).
• Wright, M. N. (2021). Interpretable machine learning in genetics. XXXIInd Conference of the Austro-Swiss Region (ROeS) of the International Biometric Society. Salzburg, Austria (hybrid conference).
• Wright, M. N. (2021). Machine learning for survival data. Institute for Medical Biostatistics, Epidemiology and Informatics, University Medical Center, Johannes Gutenberg University Mainz. Mainz, Germany (online presentation).
• Wright, M. N. (2020). Machine learning for survival data. Fraunhofer Institute for Digital Medicine MEVIS. Bremen, Germany.
• Wright, M. N. (2020). Interpretable machine learning for time to event data. Institute of Biometry and Clinical Epidemiology, Charité University Hospital. Berlin, Germany.
• Wright, M. N. (2019). Predicting the personalized need of care in an ageing society. Section of Epidemiology, Department of Public Health, University of Copenhagen. Copenhagen, Denmark.
• Wright, M. N. (2019). Benchmarking machine learning algorithms. Fraunhofer Institute for Digital Medicine MEVIS. Bremen, Germany.
• Wright, M. N. (2019). Keynote talk - Random forests: The first-choice method for every data analysis? Why R? 2019 Conference. Warsaw, Poland.
• Wright, M. N. (2017). Random forests: Fast implementations for high throughput omics data and survival endpoints. Department of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität München. München, Germany.
• Wright, M. N. (2015). Random forests: Fast implementations for high dimensional data and survival analysis. Section of Biostatistics, University of Copenhagen. Copenhagen, Denmark.

Conference contributions

• Wright, M. N., Blesch, K. & Watson, D. S. (2022). Testing conditional independence in supervised learning algorithms with the cpi package. useR! 2022. Virtual Conference.
• Wright, M. N. & Watson, D. S. (2021). Genome-wide conditional independence testing with machine learning. 67. Biometrisches Kolloquium. Münster, Germany (virtual conference).
• Wright, M. N., Mortensen, L. H., Kusumastuti, S., Westendorp, R. G. J. & Gerds, T. A. (2019). Recurrent neural networks for time to event predictions with competing risks. DAGStat 2019. München, Germany.
• Wright, M. N. & König, I. R. (2018). Splitting on categorical predictors in random forests (Poster). 64. Biometrisches Kolloquium. Frankfurt, Germany.
• Wright, M. N. & Nembrini, S. (2017). The revival of the Gini importance? Joint Conference on Biometrics & Biopharmaceutical Statistics. Vienna, Austria.
• Wright, M. N., Dankowski, T. & Ziegler, A. (2016). Random forests for survival analysis using maximally selected rank statistics (Poster). 2016 Joint Statistical Meeting. Chicago, IL, USA.
• Wright, M. N., Ziegler, A. & König, I. R. (2016). Do little interactions get lost in dark random forests? (Poster). 2016 International Biometric Conference. Victoria, BC, Canada.
• Wright, M. N. & Ziegler, A. (2016). ranger: A fast implementation of random forests for high dimensional data. useR! 2016. Stanford, CA, USA.
• Wright, M. N., Dankowski, T. & Ziegler, A. (2016). Random forests for survival analysis using maximally selected rank statistics. DAGStat 2016. Göttingen, Germany.
• Wright, M. N., Dankowski, T. & Ziegler, A. (2015). Random forests for survival analysis using maximally selected rank statistics. Herbstworkshop AG Statistische Methoden in der Epidemiologie. Mainz, Germany.
• Wright, M. N. & König, I. R. (2015). Do little interactions get lost in dark random forests? (Poster, presented by I.R. König). IGES 2015. Baltimore, MD, USA.
• Wright, M. N. & Ziegler, A. (2015). ranger: A fast implementation of random forests for high dimensional data. 61. Biometrisches Kolloquium. Dortmund, Germany.
• Wright, M. N. & Ziegler, A. (2014). A software package for genome-wide association studies with random survival forests (Poster). IGES 2014. Vienna, Austria.

Tutorials

• Wright, M. N. (2021). Model-agnostic interpretable machine learning. MOOD Science Webinars, OpenGeoHub Foundation. Virtual Webinar.
• Wright, M. N. & Gola, D. (2018). An introduction to machine learning in epidemiology. DGEpi 2018. Bremen, Germany.
• Wright, M. N. (2017). Hands-on introduction to Rcpp. 10th quarterly R Meetup. Hamburg, Germany.
• Wright, M. N. & Gola, D. (2017). An intuitive approach to machine learning: boosting, nearest neighbors, random forests and support vector machines. European Mathematical Genetics Meeting 2017. Tartu, Estonia.
• Ziegler, A. & Wright, M. N. (2016). An intuitive approach to machine learning: boosting, nearest neighbors, random forests and support vector machines. GMDS, DGEpi & IEA-EEF Annual Meeting. München, Germany.
• Ziegler, A. & Wright, M. N. (2016). A Statistical Approach to Machine Learning. 2016 Joint Statistical Meeting. Chicago, IL, USA.
• Ziegler, A. & Wright, M. N. (2016). A Statistical Approach to Machine Learning. 2016 International Biometric Conference. Victoria, BC, Canada.
• Wright, M. N. (2015). Implementation of random forests in the R package ranger. Statistical Computing 2015. Schloss Reisensburg, Günzburg, Germany.