# Talks

## Invited talks

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

## Conference contributions

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

## Tutorials

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