In a recent study, we explored the use of various machine learning techniques to model the radiation emitted by galactic objects called blazars. Blazars produce significant amounts of radiation, which make them a fascinating target for scientific exploration. This project serves as a perfect example of how data science and applied sciences can work hand-in-hand to tackle complex problems. While the methods were crucial, the true credit belongs to the incredible astrophysicists Anastasiia, Anna, and Frederike, who really did all of the heavy lifting! πͺπͺπͺ Here is the paper, and here is the wonderful astrophysics group!
Category Archives: New Paper
Challenges and Opportunities for Statistics in the Era of Data Science
Our opinion on the state of statistics and its future has now appeared here in Havard Data Science Review. Three conclusions are:
π Statistics is still very much alive!
π Statistics can contribute to modern data science in many ways, through formal modeling, inference, mathematical guarantees, and much more.
π However, statistics also needs to ensure that it stays relevant, joining forces with other data-related fields and participating in the education of the future data scientists.
The paper is also featured in the journals editorial, which I found a very good read more generally.
Organizing the workshop and the paper together with Claudia was a rewarding journey.
Sincere thanks to:
π VolkswagenStiftung, for hosting our workshop, where this paper originated. Everything was perfect: efficient and friendly organization, delicious food, wonderful scenery, practical seminar rooms, …
π Soumendra Lahiri, especially for supporting Claudia and myself in the publishing process.
π And all of the co-authors, for the inspiring discussions in Hannover and skillful contributions to the paper:
Harald Binder, Werner Brannath, Ivor Cribben, Holger Dette, Philipp Doebler, Oliver Feng, Axel Gandy, Sonja Greven, Barbara Hammer, Stefan Harmeling, Thomas Hotz, GΓΆran Kauermann, PD Dr. Joscha Krause, Georg Krempl, Alicia Nieto-Reyes, Ostap Okhrin, Hernando Ombao, Florian Pein, Michal PeΕ‘ta, Dimitris Politis, Li-Xuan Qin, Tom Rainforth, Holger Rauhut, Henry Reeve, David Salinas, Johannes Schmidt-Hieber, Clayton Scott, Johan Segers, Myra Spiliopoulou, Adalbert Wilhelm, Ines Wilms, and Yi Yu.
Anomaly detecion based on images
Anomaly detection attempts to identify “unusual” instances, that is, instances that deviate considerably from what is expected. Machine learning has become pretty good at detecting such anomalies—as long as abundant data are available. If data are scarce, however, the problem remains challenging. State-of-the-art approaches are multi-modal: they use several types of data simultaneously, usually images and text. But humans can often do well at anomaly detection using images alone. Our recent paper “AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2” has shown that the same is true also for machine learning. The reviewers agreed, and so our paper has been accepted for WACV 2025. π Congrats to Simon and Mike, and thanks to our collaborator Asja! π·π·π·π·
Prediction With Clusters
Our paper “VC-PCR: a prediction method based on variable selection and clustering” has been accepted at Statistica Neerlandica. Congratulations to our wonderful team from Belgium: Bernadette, Rainer, and especially our student/postdoc collaborator Rebecca! π§πͺπ§πͺπ§πͺ
Deep Learning in Geotechnics
We have a new paper titled “Deep learning-based analysis of true triaxial DEM simulations: Role of fabric and particle aspect ratio”, which will appear in Computers and Geotechnics. Thank you for Nazanin, Merita, Mohammad, and Torsten in Bochum and Pegah in Hamburg for this wonderful interdisciplinary collaboration! ππ’π
Layer Sparsity
Our paper “Layer sparsity in neural networks” has now been accepted at J. Stat. Plan. Inference. Congrats to Mahsa, and many thanks to our great collaborator Mohamed! πΌ
Anomaly detection
Our new paper “AnomalyDINO: boosting patch-based few-shot anomaly detection with DINOv2” is now on arXiv. Thanks for the great efforts, Simon, Mike, and Asja! π·ββοΈππ·ββοΈ
Fairness in deep generative models
Our new paper “Benchmarking the fairness of image upsampling methods” is now on arXiv. Awesome job, Mike—and thanks Imant, Julia, and Asja for the great collaboration! π©πͺπ¨ππ΄ββ οΈ
Change points in time series
Our discussion paper in JRSSB about “Probabilistic and Statistical Aspects of Machine Learning” is available online. Congratulations, Ayla! π¬π§
Affine Invariance in Neural Networks
Our new paper “Affine invariance in continuous-domain convolutional neural networks” is now on arXiv. Well done, Ali! π₯
Johannes Lederer 