I am a senior research scientist at RISE Research institutes of Sweden and head of the deep learning research group. We run a number of applied research projects where state-of-the-art techniques from machine learning transform application areas, and let new application areas drive innovation to push the boundaries of machine learning research.
I have a PhD in machine learning from Chalmers university of technology, my thesis is called Representation learning for natural language (click for details). I have worked on summarization, dialogue systems, adversarial training (for sequences, data privacy, speech), character-based RNNs, and federated learning.
I believe in the potential for AI to make the world a better place for us all, which is why much of the research projects I run are motivated by environmental or medical issues. I like listenening to the Talking Machines podcast.
Also see my licentiate thesis, titled "Multi-document summarization and semantic relatedness", and my master's thesis Dynamics of geographical routing in small-world networks.
I am currently supervising two PhD candidates. I frequently supervise master's students in their thesis work. See my research group page and the list of finished master student projects for more info. I have taught many courses.
When not doing research or teaching, I am a long distance runner and the lucky father of two wonderful children.
Olof Mogren is the director of deep learning research at RISE, and the organizer of RISE Learning Machines Seminars. He holds a PhD degree from 2018 in Computer Science from Chalmers University of Technology (Sweden), with a thesis about deep representation learning. He has extensive experience in leading applied AI projects related to sustainability and environmental and health applications. His work includes federated learning, privacy-preserving representation learning, and generative adversarial networks for multiple data modalities, including images, sound, natural language, and other types of sensor inputs. Recent work includes applied research related to soundscape analysis and computer vision for biodiversity monitoring, efficient and distributed machine learning, uncertainty quantification, predictive maintenance of district heating networks, and smart fire detection using machine listening.