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Learning machines seminars – previous seminars

Here you will find the previous seminars of 2023. Full archive can be found in the menu to the right.



2024-05-23: Shruti Nath, University of Oxford and Climate Analytics
Monthly climate model emulators: lightweight tools for agile exploration of future climate uncertainties. 

2024-05-16: Mikolaj Czerkawski, European Space Agency
Geographical Guidance in the Era of Large-Scale Earth Observation Data and AI


2024-04-18: Tobias Andermann, Uppsala University
Spatial biodiversity modeling with remote sensing and AI

2024-04-11: Alouette van Hove, University of Oslo
Guiding drones by information gain

2024-04-04: Karsten Kreis, NVIDIA Toronto AI Lab (16:00)
Visual Generative AI with Diffusion Models – From Static Pixels to Video, 3D and 4D Synthesis


2024-03-28: Yonghao Xu, Linköping University
Machine learning for remote sensing

2024-03-21: Fredrik Gustafsson, Karolinska Institutet
How reliable is your regression model’s uncertainty under real-world distribution shifts?

2024-03-14: Santiago Martinez Balvanera, University College London
Data-driven bat monitoring: leveraging machine learning for effective solutions


2024-02-22: Alexander Mathis, EPFL
Measuring behavior and modeling the brain with machine learning


2024-01-25: Joakim Lindblad, Uppsala University
Trustworthy AI-based decision support in cancer diagnostics

2024-01-18: Serge Belongie, University of Copenhagen
Challenges in Fine-Grained Image Analysis



2023-12-07: Stefan Bauer, TU Munich
Neural causal models


2023-11-30: Ben Weinstein, University of Florida
General Models for Airborne Wildlife Detection

2023-11-23: Alisa Devlic, Sony AI
Superhuman racing AI through deep reinforcement learning

2023-11-16: Jonas Hellgren, RISE
Reinforcement learning - theory and applications

2023-11-09: Nataša Sladoje, Uppsala University
Automated alignment of multimodal images: To (deep) learn – or not?

2023-11-02: Priya L. Donti, MIT and Climate Change AI
Optimization-in-the-loop ML for energy and climate


2023-10-19: Valentin De Bortoli, Google Deepmind
Diffusion Schrödinger Bridge Matching

2023-10-05: Klaus-Robert Müller, TU Berlin
Machine Learning and AI for the Sciences — Towards Understanding


2023-09-28: Johan Östman, AI Sweden
The reality of federated learning: from life sciences to finance and beyond

2023-09-21: Nico Lang, University of Copenhagen
Global vegetation monitoring with probabilistic deep learning

2023-09-14: Virginia Smith, CMU
Evaluating Large-Scale Learning Systems

2023-09-07: Adam Breitholtz, Chalmers University of Technology
Unsupervised domain adaptation by learning using privileged information


2023-08-31: Zahra Taghiyar Renan, Halmstad University
From domain adaptation to federated learning


2023-05-25: Puzhao Zhang, KTH
Remote Sensing for Wildfire using Deep Learning

2023-05-11: Jonathan Sauder, École polytechnique fédérale de Lausanne (EPFL)
Unsupervised 3D Mapping from Video

2023-05-04: Ida Arvidsson, Lund University
Applications of AI in Medical Image Analysis


2023-04-27: Pontus Stenetorp, University College London (UCL)
LLMs and Future of NLP

2023-04-20: Rico Sennrich, University of Zurich
Knowledge Transfer Across Languages and Modalities

2023-04-13: Elijah Cole, Caltech
Learning from real-world data


2023-03-23: Gabrielle Flood, Lund University
Motion Maps with Statistical Deformations

2023-03-16: Stefanos Georganos, Karlstad University/KTH
Filling the gaps: AI and Earth Observation

2023-03-09: Shay Cohen, University of Edinburgh
Summarization with Latent Structure, Context Factors and Quantitative Precision


2023-02-23: Alexander Ilin, Aalto University
Hierarchical Imitation Learning with Vector Quantized Models

2023-02-09: Joakim Nivre, Erik Ylipää, and Olof Mogren, RISE
ChatGPT and other large language models 

2023-02-02: Vincent Szolnoky, Chalmers
Model Gradient Similarity


2023-01-26: Lena Voita, FAIR
Interpretability in NLP

2023-01-19: Giulia Fanti, Carnegie Mellon University
Communication Complexity of Federated Learning

2023-01-12: Mark D. Plumbley, University of Surrey: AI for Sound
AI for Sound

Olof Mogren

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