I am a senior research scientist at RISE Research institutes of Sweden heading The Deep Learning Research Group. I have a PhD from Chalmers University of Technology, and I am the organizer of RISE Learning Machines Seminars.
I work on problems within applied AI where privacy, fairness, and efficiency is central. This includes work on federated learning, privacy-preserving representation learning, and generative adversarial networks. I work with many data modalities, including natural language, vision, and speech.
Some of our ongoing projects include The Federated Learning Testbed, The Swedish Medical Language Data Lab, AI Driven Financial Risk Assessment of Circular Business Models, and Smart Fire Detection.
For more info, visit my research webpage: mogren.one
- Adversarial representation learning for private speech generation
- Automatic blood glucose prediction with confidence using recurrent neural netwo…
- Blood Glucose Prediction with Variance Estimation Using Recurrent Neural Networ…
- Generative Modelling of Semantic Segmentation Data in the Fashion Domain
- Grammatical gender in Swedish is predictable using recurrent neural networks
- Representation learning for natural language
- Semantic Segmentation of Fashion Images Using Feature Pyramid Networks