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Olof Mogren

Senior Researcher

Tel

+46 70 396 96 24

Email

olof.mogren@ri.se

Location

Göteborg

ORCID

0000-0002-9567-2218

Research director within AI.

Works on deep learning with applications to climate adaptation and environment. I have a PhD from Chalmers University of Technology, and I am the organizer of RISE Learning Machines Seminars.

Some of our ongoing projects include Soundscape analysis using AI methods, Predictive maintenance for district heating networks, and Smart fire detection.

For more info, visit my research webpage: mogren.one

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Publications

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Predicting Water Flow Intensity with Machine Learning

Climate change is affecting the hydrological conditions of the Earth, causing both draught and flooding in different regions. Sweden is facing the challenge of adapting to a warmer and wetter climate, which will lead to more extre…

How machine learning can help coffee farmers fight disease

Coffee is one of the most popular and valuable beverages in the world, with an estimated global market value of over 100 billion US dollars. However, coffee production faces many challenges, such as climate change, pests, diseases…

AI driven financial risk assessment for circular business models

When companies transition to circular business models (especially functional sales models), risk assessments of business cases and payback times, as well as new types of collateral and residual value, have been identified as impor…

Computer vision – a fast and reliable superpower

With the advancing development of technologies based on artificial intelligence, new areas of use are emerging. Computer vision is one such area. The technology can assist people in several ways, who can instead devote themselves …

“I want my AI research to benefit people”

There is increasing demand for research into artificial intelligence. Olof Mogren at RISE develops machine learning and deep learning for everything from predicting blood sugar levels in Type 1 diabetics to helping fashion compani…

Tobias Andermann: Spatial biodiversity modeling with remote sensing and AI

At RISE Learning Machines Seminar on April 18, 2024, we have the pleasure to listen to Tobias Andermann, Uppsala University, give his talk: Spatial biodiversity modeling with remote sensing and AI.

Alouette van Hove: Guiding drones by information gain

At RISE Learning Machines Seminar on April 11, 2024, we have the pleasure to listen to Alouette van Hove, University of Oslo, give her talk: Guiding drones by information gain. 

Karsten Kreis: Visual Generative AI with Diffusion Models – From Static Pixels to Video, 3D and 4D Synthesis

At RISE Learning Machines Seminar on April 4, 2024, we have the pleasure to listen to Karsten Kreis, NVIDIA Research, give his talk: Visual Generative AI with Diffusion Models: From Static Pixels to Video, 3D and 4D Synthesis…

Yonghao Xu: Machine learning for remote sensing

At RISE Learning Machines Seminar on March 28 2024, we have the pleasure to listen to Yonghao Xu, Linköping University, give his talk: Machine learning for remote sensing. 

Santiago Martinez Balvanera: Data-driven bat monitoring: leveraging machine learning

At RISE Learning Machines Seminar on 14 March 2024, we have the pleasure to listen to Santiago Martinez Balvanera, University College London, give his talk: Data-driven bat monitoring: leveraging machine learning for effectiv…

Joakim Lindblad: Trustworthy AI-based decision support in cancer diagnostics

At RISE Learning Machines Seminar on January 25, 2024, we have the pleasure to listen to Joakim Lindblad, Uppsala University, give his talk: Trustworthy AI-based decision support in cancer diagnostics.

Alisa Devlic: Superhuman racing AI through deep reinforcement learning

At RISE Learning Machines Seminar on November 23, 2023, we have the pleasure to listen to Alisa Devlic, Sony AI, give her talk: Superhuman racing AI through deep reinforcement learning.

Alexander Mathis: Measuring behavior and modeling the brain with machine learning

At RISE Learning Machines Seminar on February 22 2024, we have the pleasure to listen to Alexander Mathis, EPFL, give his talk: Measuring behavior and modeling the brain with machine learning. 

Serge Belongie: Challenges in Fine-Grained Image Analysis

At RISE Learning Machines Seminar on January 18 2024,  we have the pleasure to listen to Serge Belongie, University of Copenhagen, give his talk: Challenges in Fine-Grained Image Analysis.

Fredrik Gustafsson: Reliable regression model uncertainty under real-world distribution shifts

At RISE Learning Machines Seminar on March 21 2024, we have the pleasure to listen to Fredrik Gustafsson, Karolinska Institutet, give his talk: How reliable is your regression model’s uncertainty under real-world distribution…

Stefan Bauer: Neural causal models

At RISE Learning Machines Seminar on December 7 2023, we have the pleasure to listen to Stefan Bauer, TU Munich, give his talk: Neural causal models

Ben Weinstein: General Models for Airborne Wildlife Detection

At RISE Learning Machines Seminar on November 30 2023, we have the pleasure to listen to Ben Weinstein, University of Florida, give his talk: General Models for Airborne Wildlife Detection.

Jonas Hellgren: Reinforcement Learning – theory and applications

At RISE Learning Machines Seminar on November 16 2023, we have the pleasure to listen to Jonas Hellgren, RISE, give his talk: Reinforcement learning – theory and applications.

Nataša Sladoje: Automated alignment of multimodal images: To (deep) learn – or not?

At RISE Learning Machines Seminar on November 9 2023, we have the pleasure to listen to Nataša Sladoje, Uppsala University, give her talk: Automated alignment of multimodal images: To (deep) learn – or not?

Science of AI

How do we work with the science of AI? The Center for Applied AI at RISE is pushing the research frontier and ensuring scientific excellence along a select number of research directions.  

Priya Donti: Optimization-in-the-loop ML for energy and climate

At RISE Learning Machines Seminar on November 2, 2023, we have the pleasure to listen to Priya Donti, MIT and Climate Change AI, give her talk: Optimization-in-the-loop ML for energy and climate.

Valentin De Bortoli: Diffusion Schrödinger Bridge Matching

At RISE Learning Machines Seminar on October 19 2023,  we have the pleasure to listen to Valentin De Bortoli, Google Deepmind, give his talk: MDiffusion Schrödinger Bridge Matching.

Johan Östman: The reality of federated learning: from life sciences to finance and beyond

At RISE Learning Machines Seminar on September 28 2023, we have the pleasure to listen to Johan Östman, AI Sweden, give his talk: The reality of federated learning: from life sciences to finance and beyond. 

Virginia Smith: Evaluating Large-Scale Learning Systems

At RISE Learning Machines Seminar on 14 September 2023, we have the pleasure to listen to Virginia Smith, CMU, give her talk: Evaluating Large-Scale Learning Systems.

Adam Breitholz: Unsupervised domain adaptation by learning using privileged information

At RISE Learning Machines Seminar on 7 September 2023, we have the pleasure to listen to Adam Breitholz, Chalmers University of Technology, give his talk: Unsupervised domain adaptation by learning using privileged informatio…

Zahra Taghiyar Renan: From domain adaptation to federated learning

At RISE Learning Machines Seminar on August 31 2023. we had the pleasure to listen to Zahra Taghiyar Renan, Halmstad University, give her talk: From domain adaptation to federated learning.  – Theoretically, w…

Nico Lang: Global vegetation monitoring with probabilistic deep learning

At RISE Learning Machines Seminar on September 21 2023, we have the pleasure to listen to Nico Lang, University of Copenhagen give his talk: Global vegetation monitoring with probabilistic deep learning.  This talk will…

Klaus-Robert Müller: Machine Learning and AI for the Sciences

At RISE Learning Machines Seminar on October 5 2023, we have the pleasure to listen to Klaus-Robert Müller give his talk: Machine Learning and AI for the Sciences.   In his talk, Müller will touch upon th…

Lena Voita: A Journey on Interpretability Methods in NLP: Inside-Out and Back

At RISE Learning Machines Seminar on Jan 26 2023, we have the pleasure to listen to Lena Voita, FAIR, give her talk:  A Journey on Interpretability Methods in NLP: Inside-Out and Back. – In this talk, I will illustrate …

Contact for learning machine seminars

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.

Learning Machines Seminars – Upcoming Seminars

Here you find all the upcoming Learning Machines Seminars. Update your calendar with upcoming seminar dates of your interest!

Computer vision

Image analysis is a key technology for understanding and solving complex problems in various domains. At RISE, we conduct cutting-edge research in this field using artificial intelligence methods. We apply our knowledge and skills…

AI for climate change

We are facing the greatest challenge in modern time. Climate change is already giving rise to extreme weather events around the world. Artificial intelligence can be used both to mitigate and help us adapt to the disasters we face…

MLUP

Machine learning (ML) is an efficient tool to mimic complex processes. Numerical simulations, such as computational fluid dynamics (CFD) is often extremely time demanding and may occupy considerable computer resources for an exten…

Deep learning

Deep learning has taken the world by storm as the modern day work-horse powering many recent solutions in artificial intelligence, including Chat GPT, Dall-E, and autonomous driving. Deep neural networks are used in many applicati…

Natural Language Processing

Natural Language Processing (NLP) is the task of using computer programs to analyze, understand and generate natural language. Our research concerns both basic algorithms for language AI and the use of such algorithms to solve var…

Learning Machines Seminars

Learning Machines Seminars gathers experts in AI for an open weekly seminar! Seminars include presentations on a current topic on machine learning.

Publications

See all DIVA publications