Maximilian Pichler

PhD student Group for Theoretical Ecology Faculty of Biology and Pre-Clinical Medicine University of Regensburg Universitätsstraße 31 93053 Regensburg email: maximilian.pichler@biologie.uni-regensburg.de room: E4._2.103

Research Interests

• Machine Learning and Deep Learning in Ecology
• Inference with Machine Learning and Deep learning
• Joint Species Distribution Models (jSDM)
• Trait-Matching
• Automatic Species Recognition

See also

• twitter
• github
• google scholar

Software:

• cito - Building and training neural networks in R
• s-jSDM (fast and scalable JSDM based on PyTorch)
• TraitMatching (Using Machine Learning to predict species interactions)

Curriculum Vitae

Publications

• Pichler, M., & Hartig, F. (2023). Can predictive models be used for causal inference?. arXiv preprint arXiv:2306.10551. [preprint]
• Pichler, M., & Hartig, F. (2023). Machine learning and deep learning—A review for ecologists. Methods in Ecology and Evolution, 14(4), 994-1016. [journal]
• Pichler, M., & Hartig, F. (2021). A new joint species distribution model for faster and more accurate inference of species associations from big community data. Methods in Ecology and Evolution.[journal]
• Oberpriller, J., de Souza Leite, M., & Pichler, M. (2021). Fixed or random? On the reliability of mixed-effect models for a small number of levels in grouping variables. bioRxiv.[journal]
• Pichler, M., Boreux, V., Klein, A. M., Schleuning, M., & Hartig, F. (2020). Machine learning algorithms to infer trait‐matching and predict species interactions in ecological networks. Methods in Ecology and Evolution, 11(2), 281-293. [journal]