We are a multi-disciplinary research group in computational and data-driven science. Our research interests are particularly oriented towards addressing challenges of scale and complexity. We are active in several areas including Decentralised AI/Federated Learning, Distributed and Cloud Computing, Cybersecurity, Machine Learning and Data-Driven Life Science. We are located at the Division of Scientific Computing, Department of Information Technology, Uppsala University.
We develop methods and software to address decentralised and privacy-preserving AI. We are core contributors to the FEDn open source framework for scalable federated machine learning. Recent projects in the federated setting include federated optimization and semi-supervised learning.
We address data-centric challenges in distributes systems and cybersecurity. Recent focus areas include scalable and efficient hierarchical storage and information management, image compression, and data-driven vulnerability analysis.
We study scientific computing, but with a firm focus on life science application data analysis, utilizing modern computing architectures (including GPU computations and massive parallelism in varying forms). The basic question is "how can we trade experiment result quality for more sophisticated computational methods", giving better results with worse original data.
The themes of learning, inference and optimization are intimately connected, and power AI and data-driven science. We build the foundations of data-efficient machine learning and optimisation. We also develop methods for performing analysis/inference with scientific data. Non-convex and global optimisation are strong focus areas in our lab.
We seek a Postdoctoral Researcher in Machine Learning Theory to join us at the Department of Information Technology, Science for Life Laboratory (SciLifeLab), Uppsala University, Sweden. You will be part of a collaborative research group Read more…
We are organizing a mini-symposium at Euro-Par 2024 on, Scalable Federated Machine Learning – Introducing the FEDn Framework for Cross-Device and Cross-Silo Environments Read more here!
We welcome applications for 2 PhD positions in our group: Please contact Prashant Singh with any questions.