Olof Mogren

Senior Researcher

Tel

+46 73 023 56 09

E-post

olof.mogren@ri.se

Ort

Göteborg

ORCID

0000-0002-9567-2218

Forskningsledare inom AI. Forskar inom maskininlärning och djupa neuronnät. Disputerade 2018 på Chalmers med avhandlingen "Representation learning for natural language". Intresserad av tillämpningar där datadrivna metoder gör nytta inom klimatanpassning och miljö. Organiserar RISE Learning Machines Seminars. Några av våra pågående projekt inkluderar Soundscape analysis using AI methods, Predictive maintenance for district heating networks och Smart fire detection mfl. Mer information finns på min forskarsida: mogren.one (engelska).

Olof Mogren

AI-stöd för omvandling av ytor till multifunktionella blågröna miljöer

EU Horizon CSA FERTITEC

Modellering av vattenflöden med maskininlärning

Finansiell riskvärdering för cirkulära affärs­modeller med hjälp av AI

Så kan energieffektiv AI minska klimatpåverkan

De vill bana väg för nykter trafik med AI-verktyg

Så kan AI bidra i kampen mot klimatförändringarna

Datorseende – en snabb och pålitlig superkraft

Vänder på algoritmen och skapar AI-konst

“Jag vill göra nytta för människor med min AI-forskning”

Beici Liang: Protecting music copyrights with MIR and AI

Abdul Shaamala: Optimising green infrastructure for climate-resilient cities

María J. Molina: Machine learning for Earth system prediction and predictability

Oriol Nieto: GenAI for sound design

Sherrie Wang: Reliable conclusions from remote sensing maps

Atakan Aral: Edge AI and IoT-Driven Water Quality Monitoring

Abdulhakim Abdi: Tech-informed nature conservation - are we there yet?

Benjamin Cretois: Leveraging AI for Large-Scale Acoustic Biodiversity Monitoring

Amal Nammouchi: Leveraging AI for Climate Resilience in Africa

Newton Mwai Kinyanjui: Improving treatment personalization with structures in sequential decision making

Oscar Täckström: From Enterprise Search to Intelligent Agents

Alireza Taheri Dehkordi: Estimation of water quality parameters using remote sensing data and machine learning mo…

Alp Kucukelbir: Artificial Intelligence for Climate Change Mitigation

Ankit Kariryaa: Deep Learning for digital twins of individual trees

Joel Oskarsson: Frontiers in machine learning for weather forecasting

Matthias Obst: Digital Twins & observation systems for monitoring marine biodiversity and change

Jakob Ambsdorf: Medical image analysis with limited labels

Charlie Fieseler: How to build a brain

Mélisande Teng: Species distribution modeling using remote sensing and citizen science data

Smita Chakraborty: AI-SAXS – Decoding Structural Complexity with Intelligent Scattering Analysis

Jonas Hentati Sundberg: Sensors and AI in seabird research and monitoring

Shruti Nath: Climate model emulators – tools for agile exploration of future climate uncertainties

Mikolaj Czerkawski: Geographical Guidance in the Era of Large-Scale Earth Observation Data and AI

Tobias Andermann: Spatial biodiversity modeling with remote sensing and AI

Alouette van Hove: Guiding drones by information gain

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

Yonghao Xu: Machine learning for remote sensing

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

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

Alisa Devlic: Superhuman racing AI through deep reinforcement learning

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

Serge Belongie: Challenges in Fine-Grained Image Analysis

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

Stefan Bauer: Neural causal models

Ben Weinstein: General Models for Airborne Wildlife Detection

Jonas Hellgren: Reinforcement Learning – theory and applications

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

AI-forskning

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

Valentin De Bortoli: Diffusion Schrödinger Bridge Matching

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

Virginia Smith: Evaluating Large-Scale Learning Systems

Adam Breitholz: Unsupervised domain adaptation by learning using privileged information

Zahra Taghiyar Renan: From domain adaptation to federated learning

Nico Lang: Global vegetation monitoring with probabilistic deep learning

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

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

Kontakt för learning machine seminars

Learning machines seminars – genomförda seminarier

Learning Machines Seminars – kommande seminarier

Datorseende

AI för klimatet

MLUP

Deep learning

Språkteknologi

Learning Machines Seminars

Finansiell riskvärdering för cirkulära affärsmodeller med hjälp av AI