Skip to main content

Learn

Learn involves how to extract information from data that makes systems smart and adaptive or even autonomous. Since the generation and storage of data will often be distributed, there is a strong need for efficient distributed data analytics. A fundamental understanding of how machine learning algorithms extract information from data is still missing, and the impact of the data on the learning process and the resulting bias is hardly understood. This gives rise to questions concerning legal safeguards and the rule of law.

If you are interested in joining the working group, please feel free to contact the chair.

Ozan Öktem

Associate Professor at KTH, Co-PI of project Spatiotemporal reconstruction with learned deformations for earlier cancer detection via PET imaging, Chair working group Learn, Member of the Strategic Research Committee, Digital Futures Faculty

ozan@kth.se
Picture of Martina Scolamiero

Martina Scolamiero

Assistant Professor, Department of Mathematics at KTH, Vice Chair Working group Learn, Digital Futures Faculty

+46 8 790 81 19
scola@kth.se
Picture of Stefan Bauer

Stefan Bauer

Former Assistant Professor at KTH EECS, Former Working group Learn, Former Co-PI: Data-driven cardiovascular assist devices, Former Co-PI: Seed funding for large grant proposals, Former Digital Futures Faculty

Jonas Beskow

Professor and Dep. Head of Division at Division of Speech, Music and Hearing at KTH, Working group Learn, Co-PI: Advanced Adaptive Intelligent Systems (AAIS), Co-PI: Adaptive Intelligent Homes (AIH), Digital Futures fellow, Digital Futures Faculty

+46 8 790 89 65
beskow@kth.se
Picture of Henrik Boström

Henrik Boström

Professor, Division of Software and computer Systems at KTH, Working group Learn, Digital Futures fellow, Digital Futures Faculty

+46 8 790 43 06
bostromh@kth.se

Aristides Gionis

Professor, Division of Theoretical Computer Science at KTH, Former Member of the Strategic Research Committee, Working group Learn, Main Supervisor: Dragons - Data-Driven Algorithms and Governance for Networked Societies, Digital Futures Faculty

argioni@kth.se

Pawel Herman

Associate Professor, Computer Science, Division of Computational Science and Technology at KTH EECS, Co-PI: Humanizing the Sustainable Smart City eXtended (HiSSx), Former Co-PI: Humanizing the Sustainable Smart City (HiSS), Former Co-supervisor: Intelligence through reasoning, Digital Futures Faculty

+46 8 790 65 13
paherman@kth.se
Picture of Jaakko Hollmén

Jaakko Hollmén

Senior lecturer, Department of Computer and Systems Sciences at Stockholm University, Working group Learn, Digital Futures Faculty

+46 8 16 16 91
jaakko.hollmen@dsv.su.se
Picture of Anders Holst

Anders Holst

Senior Research Scientist, Swedish Institute of Computer Science at RISE, Adjunct professor at KTH Royal Institute of Technology, Working group Learn, Digital Futures Faculty

+46 10 228 43 13
anders.holst@ri.se
Picture of Iolanda Leite

Iolanda Leite

Associate Professor, Department of Robotics, Perception and Learning at KTH, Working group Learn, PI: Advanced Adaptive Intelligent Systems (AAIS), PI: Adaptive Intelligent Homes (AIH), Former Main supervisor: On The Feminist Design of Social Robots and Designing Robots For Young People, With Young People, Former Main supervisor: Designing Gamified Robot-Enhanced Interventions for Children with Neurodevelopmental Disorders, Digital Futures Faculty

iolanda@kth.se
Picture of Alexandre Proutiere

Alexandre Proutiere

Professor, Division of Decision and Control Systems at KTH, Working group Learn, Co-PI: Data-Limited Learning of Complex Dynamical Systems - Impact and Demonstrators, Former Co-PI: Data-Limited Learning of Complex Dynamical Systems, Digital Futures fellow, Digital Futures Faculty

+46 8 790 63 51
alepro@kth.se
Picture of Kevin Smith

Kevin Smith

Associate Professor, Division of Computational Science and Technology at KTH, Working group Learn, Digital Futures Faculty

+46 8 790 64 37
ksmith@kth.se

Liam Solus

Assistant Professor at KTH SCI, Working group Learn, Co-PI of project cAIMBER: Causal Artificial Intelligence for Human Mobility Behavior Analysis Using Trajectory Data, Digital Futures Faculty

+46 8 790 65 85
solus@kth.se

Ming Xiao

Associate Professor, Division of ISE at KTH EECS, Working group Learn, Co-supervisor: SMART – Smart Predictive Maintenance for the Pharmaceutical Industry, Co-supervisor: Fast Distributed Learning based on Adaptive Gradient Coding with Convergence Guarantees, Former Main supervisor: Intelligent wireless communications and high-accuracy positioning systems, Digital Futures Faculty

+46 8 790 65 77
mingx@kth.se