Olof Mogren
Principal Researcher
I am a Principal Researcher heading the RIDR group, responsible for the deep learning research area, and Docent at the Faculty of Engineering (LTH), Lund University. I hold a PhD in machine learning from Chalmers University of Technology (2018).
My research spans both foundational machine learning and applied AI with an emphasis on sustainability. My work focuses applying machine learning and applied AI techniques to climate change adaptation, environmental monitoring, biodiversity tracking, and machine listening.
In addition to research, I co-founded Climate AI Nordics, serve as co-PI for CLIMES (Swedish Centre for Impacts of Climate Extremes), and host the RISE Learning Machines Seminars.
Key Research Topics
- AI for climate adaptation and environmental monitoring
- Soundscape analysis and biodiversity monitoring
- Machine learning for remote sensing
- Robust machine learning
- Efficient and distributed machine learning
Personal website: mogren.ml
- The Accuracy Cost of Weakness : A Theoretical Analysis of Fixed-Segment Weak La…
- Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Usi…
- From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection an…
- Efficient Node Selection in Private Personalized Decentralized Learning
- Fully Convolutional Networks for Dense Water Flow Intensity Prediction in Swedi…
- Financing solutions for circular business models : Exploring the role of busine…
- FEW-SHOT BIOACOUSTIC EVENT DETECTION USING AN EVENT-LENGTH ADAPTED ENSEMBLE OF …
- Decentralized adaptive clustering of deep nets is beneficial for client collabo…
- Few-shot bioacoustic event detection using a prototypical network ensemble with…
- EFFGAN: Ensembles of fine-tuned federated GANs