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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 netorks. 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.

Read more about me, or about my research group.

Selected publications

 
Adversarial representation learning for synthetic replacement of private attributes

IEEE BigData 2021

 
Decentralized federated learning of deep neural networks on non-iid data

FL-ICML 2021

 
Adversarial representation learning for private speech generation

ICML-SAS 2020

 
Blood glucose prediction with variance estimation using recurrent neural networks

JHIR

 
Semantic segmentation of fashion images using feature pyramid networks

CVCREATIVE 2019

 
Character-based recurrent neural networks for morphological relational reasoning

JLM 2019

 
C-RNN-GAN: Continuous recurrent neural networks with adversarial training

CML 2016

List all publications.

Recent talks in selection

 
Learned representations and what they encode

2021-01-20

 
Social bias and fairness in NLP

2020-11-27

 
Uncertainty in deep learning

2020-11-05

Olof Mogren, PhD, RISE Research institutes of Sweden