Joined March 2018
- Tweets 58
- Following 415
- Followers 216
- Likes 788
21 Photos and videos
Maximilian Pichler retweeted
13 Nov 2025
We are hiring: 4 PhD Positions in Ecology / Data Science and one Ecological Data Scientist. For more information, see uni-regensburg.de/universita…
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11 Nov 2025
#PhD position!
I’m looking for a PhD Student (1 3 years) to model insect populations using statistical models deep learning at #TheoreticalEcology, Regensburg (Germany).
Join our team - please RT!
Details: karriere.uni-regensburg.de/t…
#Insects #DeepLearning #Ecology #Monitoring
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11 Aug 2025
In our new preprint, “Inferring processes within dynamic forest models using hybrid modeling” @KaberYannek I present a new hybrid modeling approach for jointly calibrating a DVM with embedded #deepneuralnetwork arxiv.org/abs/2508.01228 1/4
#deeplearning #forestdynamics
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11 Aug 2025
We introduce forest-informed neural networks (FINNs), a new DVM in which processes can be replaced by #DNN and the entire model is calibrated jointly. FINN can approximate the functional shapes of otherwise misspecified processes and achieve better predictive accuracy 3/4
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11 Aug 2025
Moreover, we can use explainable AI to understand the learned functional form of the replaced process. We demonstrated this using the BCI plot by replacing the growth process with a #DNN. We found plausible dbh-growth and light-growth functions learned by FINN 4/4
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Maximilian Pichler retweeted
25 Nov 2024
In @nature, Delavaux et al., 2024 suggest that species richness on oceanic islands is reduced by mutualism rates on the mainland. In arxiv.org/abs/2411.15105, @_Max_Pichler and I had another look at this question and conclude that this effect is likely a statistical artefact.
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7 May 2024
In our @EcographyJourna software note ‘cito': an R package for training neural networks using ‘torch' (#CRAN #rstats) led by @amesoeder and together with @florianhartig we present a new package for fitting and interpreting #DNN (#DL) doi.org/10.1111/ecog.07143 @TheoEcolUR 1/5
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7 May 2024
Moreover, cito is tailored for ecological data, beside typical loss functions we support likelihoods such as Poisson, negative binomial, or multivariate probit link for joint species distribution models #JSDM. 4/5
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7 May 2024
We have prepared an extensive overview and documentation (especially for beginners) with examples in our vignettes (citoverse.github.io/cito/und… Articles) 5/5:
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Maximilian Pichler retweeted
22 Apr 2024
📊 Exciting news! Prepare to boost your skills and expertise in statistics with our stellar lineup of instructors:
@vdVeenB @MatthewBJane @nj_clark @ucfagls @PhilipLeftwich @florianhartig @_Max_Pichler
Don't miss out on this opportunity 🚀physalia-courses.org/courses…
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11 Apr 2024
cito v1.1 #rstats package for deep neural networks (#DL #DNN) (with formula syntax) is now available on #CRAN. New features include likelihoods such as the negative binomial distribution and easy hyperparameter tuning: cran.r-project.org/web/packa… 1/4
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11 Apr 2024
For more details and explanations on how to train and interpret DNNs, see our extensive documentation (including #SDM and #MSDM examples) that also covers advanced topics such as custom loss functions and residual checks (under articles on citoverse.github.io/cito/)!
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Maximilian Pichler retweeted
24 Nov 2023
Come work with us - I am looking to fill a 3-yr position for a statistical postdoc / scientific programmer to continue the development of the DHARMa #Rstats #CRAN package for #glmm residual diagnostics. Full job advertisement is here uni-regensburg.de/assets/bio…
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6 Oct 2023
cito v1.0.2 #rstats package for deep neural networks(#DL #DNN) is now available on #CRAN. New features include confidence intervals and p-values for #xAI metrics: cran.r-project.org/web/packa… @florianhartig @amesoeder
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6 Oct 2023
An important new feature is the baseline loss, which helps in training the #DNN. It allows, for example, to identify inappropriate learning rates:
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6 Oct 2023
To learn more about example workflows such as #SDM, multi-species distribution models, or advanced stuff like quantile regression (e.g. for #conformalPrediction), check out the vignettes: cran.r-project.org/web/packa…
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