Olof Mogren

Principal Researcher

Tel

+46 73 023 56 09

E-post

olof.mogren@ri.se

Ort

Göteborg

ORCID

0000-0002-9567-2218

Jag är forskningsledare och leder RIDR-gruppen med ansvar för forskningsområdet djupinlärning, samt docent vid Lunds tekniska högskola (LTH), Lunds universitet. Jag har en doktorsexamen i maskininlärning från Chalmers tekniska högskola (2018).

Min forskning spänner över både grundläggande maskininlärning och tillämpad AI med fokus på hållbarhet. Mitt arbete är inriktat på att tillämpa maskininlärning och AI-metoder för klimatanpassning, miljöövervakning, biologisk mångfald och maskinlyssning.

Utöver forskningen är jag medgrundare av Climate AI Nordics, delansvarig forskningsledare (co-PI) för CLIMES (Swedish Centre for Impacts of Climate Extremes) och värd för seminarieserien RISE Learning Machines Seminars.

Olof Mogren

AI-stöd för omvandling av ytor till multifunktionella blågröna miljöer

FERTITEC - återvinning av gödsel från sekundära råmaterial, EU Horizon

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”

Rangel Daroya: Machine perception for scientific measurement from satellite imagery

Ana Lucic: Aurora – A foundation model of the Earth system

Jaime Caballer Revenga: Towards verified tree-level CO2 and H2O physiology, resolved at 30-min resolution

Ariel Flint Ashery: More is different – emergent social conventions and tipping points in AI populations

Markus Reichstein: Machine Learning for Earth System Science

Gustau Camps-Valls: A Critical Look at Explainable AI

Ariel Flint Ashery: More is different – emergent social conventions and tipping points in AI populations

Kelsey Doerksen: EarthShift: Benchmarking Robust Geospatial AI

Solmaz Khazaei: Flood detection using deep learning-based segmentation

Jeppe Rasmussen: AI in the wild – How Neural Networks Help Us Understand Our World Through Sound

Isabelle Tingzon: Geospatial AI for Climate Resilience and Education

Purnendu Sardar: Machine learning based classification of tree crops of Syrian Arab Republic

Sanja Karilanova: Bridging Spiking Neural Networks and Deep State-Space Models

Pablo Villanueva-Perez: Physics-Informed AI for X-ray Imaging

Nita Mulliqi: Towards clinically reliable artificial intelligence for prostate cancer pathology

Katarzyna Michalowska: Multi-Resolution Learning with Neural Operators

Felix Köhler: From numerical simulators of PDEs to neural emulators and back

Nora Gourmelon: Self-Supervised Learning for Glacier Monitoring from Space

Lena Stempfle: Interpretable machine learning for prediction with missing values at test time

Sebastian Hafner: Earth observation and deep learning for urban applications

Sigrid Passano Hellan: Hyperparameter tuning, Bayesian optimisation and applications related to climate change

Georges Le Bellier: Generative domain adaptation and foundation models for robust Earth observation

Markus Pettersson: Debiasing AI predictions for causal inference without fresh ground truth data

Lily Xu: High-stakes decisions from low-quality data: AI decision-making for conservation

John Martinsson: Efficient and precise annotation of local structures in data

Peter Dueben: The digital revolution of Earth system modelling

Ghjulia Sialelli: SatML for large-scale above-ground biomass estimation

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

Finansiell riskvärdering för cirkulära affärsmodeller med hjälp av AI