Dr. Maximilian Pichler

I am a computational ecologist. I am currently a PostDoc and Group Leader in the Theoretical Ecology Chair led by Florian Hartig. 

My research group and I study how we can use artificial intelligence, particularly algorithms from deep learning and machine learning, to study ecological systems such as bipartite networks and biodiversity patterns.

You can find my publications and research output on:

• My google scholar profile
• My ORCID profile

For more information, see also our my bAImo Project (bavarian artificial intelligence for insect monitoring) as well as my profiles on GitHub(externer Link, öffnet neues Fenster) (externer Link, öffnet neues Fenster) , arXiv(externer Link, öffnet neues Fenster) (externer Link, öffnet neues Fenster), 

You can contact me via email maximilian(at)ur.de or bluesky (externer Link, öffnet neues Fenster) or find me in my office WNW D4._1.309

I am developing R packages that combine statistical modeling with deep learning and machine learning:

• Statistical inference with Deep Neural Networks: cito - Building and training neural networks in R (CRAN)
• (deep)jSDM for big/novel community data: s-jSDM (fast and scalable JSDM based on PyTorch) (CRAN)
• ML and DL for trait-matching: TraitMatching (Using Machine Learning to predict species interactions) 

We use artificial intelligence, particularly algorithms from deep learning and machine learning, to study ecological systems such as bipartite networks and biodiversity patterns:

• Inference of complex ecological effects with Machine Learning and Deep learning
• Using Machine Learning and Deep Learning to infer trait-matching in ecological networks
• Machine Learning Deep Learning for inference
• (Deep) Joint Species Distribution Models (jSDM) (with AI)
• Automatic Species Recognition

Education

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]