29.07.2020
©master1305 - stock.adobe.com
MCML - Virtual Workshop
Over 20 Presentations by Our PhD Students on Current Research Topics
The workshop includes presentations on Spatial and Temporal Machine Learning & Computer Vision, Learning on Graphs and Networks & Representation Learning, and Automatic and Explainable Modeling & Computational Models for Large-Sclae ML.
Agenda
Welcome
2:00 pm – 2:20 pm
Welcome Greeting
Prof. Dr. Thomas Seidl, Prof. Dr. Daniel Cremers
Track 1: Spatial and Temporal Machine Learning & Computer Vision
2:20 pm – 2:40 pm
Machine Learning at CAMP: Interpretability and Spatio-temporal Learning for Medical Imaging
Ashkan Khakzar, Azade Farshad / Prof. Dr. Nassir Navab
2:40 pm – 3:00 pm
Bayesian image segmentation with hierarchical Potts models
Christopher Küster / Prof. Dr. Volker Schmid
3:00 pm – 3:20 pm
Resource Search in Data Driven Environments
Prof. Dr. Matthias Schubert
3:20 pm – 3:40 pm
Learning a neural solver for multi-object tracking
Guillem Brasó / Prof. Dr. Laura Leal-Taixé
3:20 pm – 3:40 pm
Learning a neural solver for multi-object tracking
Guillem Brasó / Prof. Dr. Laura Leal-Taixé
3:40 pm – 4:00 pm
Deep learning: a non-alchemical view
Yuesong Shen / Prof. Dr. Daniel Cremers
4:00 pm – 4:20 pm
Equivariant Deep Learning
Vladimir Golkov, / Prof. Dr. Daniel Cremers
4:20 pm – 4:40 pm
Learning to Optimize for Human Reconstructions
Andrei Burov / Prof. Dr. Matthias Niessner
Track 2: Learning on Graphs and Networks & Representation Learning
2:20 pm – 2:40 pm
Robust deep learning on graphs
Daniel Zügner / Prof. Dr. Stephan Günnemann
2:40 pm – 3:00 pm
Applied Network Science
Cornelius Fritz, Marc Schneble, Sevag Kevork / Prof. Dr. Göran Kauermann
3:00 pm – 3:20 pm
Knowledge Graph Matching
Max Berrendorf / Prof. Dr. Volker Tresp
3:20 pm – 3:40 pm
Measurement Dependence Inducing Latent Causal Models
Alex Markham / Prof. Dr. Moritz Grosse-Wentrup
3:40 pm – 4:00 pm
Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly
Nora Kassner / Prof. Dr. Hinrich Schütze
4:00 pm – 4:20 pm
Query to reference single-cell integration with transfer learning
Mohammad Lotfollahi / Prof. Dr. Dr. Fabian Theis
4:20 pm – 4:40 pm
Mapping the fate of single cells with RNA velocity using CellRank
Marius Lange / Prof. Dr. Dr. Fabian Theis
Track 3: Automatic and Explainable Modeling & Computational Models for Large-Scale ML
2:20 pm – 2:40 pm
Semi-Structured Deep Distributional Regression
Dr. David Rügamer / Prof. Dr. Bernd Bischl
2:40 pm – 3:00 pm
Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles
Julia Moosbauer, Martin Binder / Prof. Dr. Bernd Bischl
3:00 pm – 3:20 pm
Cluster Analysis and Feature Rankings: Validation, benchmarking and over-optimism concerns
Theresa Ullmann, Christina Nießl / Prof. Dr. Anne-Laure Boulesteix
3:20 pm – 3:40 pm
Finding and evaluating embeddings for functional data
Moritz Herrmann / PD Dr. Fabian Scheipl
3:40 pm – 4:00 pm
Clustering Large-Scaled Datasets using Deep Learning
Li Qian / Prof. Dr. Christian Böhm
4:00 pm – 4:20 pm
Evaluation of Results from Unsupervised Learning Processes
Anna Beer / Prof. Dr. Peer Kröger
4:20 pm – 4:40 pm
Recent Advances in Correlation Clustering
Daniyal Kazempour / Prof. Dr. Thomas Seidl
4:40 pm – 5:00 pm
Active Learning - Diversity vs. Uncertainty Sampling
Sandra Obermeier / Prof. Dr. Thomas Seidl
Closing
5:00 pm – 5:30 pm
Closing Remarks
Prof. Dr. Bernd Bischl
Related
09.10.2025
Rethinking AI in Public Institutions - Balancing Prediction and Capacity
Unai Fischer Abaigar explores how AI can make public decisions fairer, smarter, and more effective.
08.10.2025
MCML-LAMARR Workshop at University of Bonn
MCML and Lamarr researchers met in Bonn to exchange ideas on NLP, LLM finetuning, and AI ethics.
08.10.2025
Three MCML Members Win Best Paper Award at AutoML 2025
MCML PI Matthias Feurer and Director Bernd Bischl’s paper on overtuning won Best Paper at AutoML 2025, offering insights for robust HPO.
29.09.2025
Machine Learning for Climate Action - With Researcher Kerstin Forster
Kerstin Forster researches how AI can cut emissions, boost renewable energy, and drive corporate sustainability.
