Artificial intelligence (AI) in the Department of Computing Science’s research.
Artificial intelligence (AI) at the Department of Computing Science at Umeå University is shaped by a vibrant community of researchers who explore how intelligent systems can benefit society. Our professors work across a wide spectrum of AI, from understanding human decision‑making and building trustworthy autonomous systems to advancing machine learning, optimization, and natural language technologies.
They are commited to creating AI that is reliable, transparent, and grounded in real‑world needs. Their work helps illuminate how AI can support people in making better decisions, improve the tools we use every day, and open new possibilities in science, healthcare, and sustainability. Our faculty aims to inspire those who want to understand not only what AI can do, but also what it should do.
Feras M. Awaysheh : Collaborative and Privacy-Preserving Intelligent Systems
Research Description
My research focuses on the design and development of intelligent systems that can learn, scale, and collaborate across distributed environments while preserving privacy, security, and trust. I investigate federated learning, edge intelligence, and distributed AI techniques that enable parties to jointly build AI models without sharing sensitive data. A key aspect of my work is addressing challenges related to data heterogeneity, resource constraints, communication efficiency, and trustworthy AI deployment in real-world environments. The overall goal is to enable scalable, efficient, and responsible AI systems for applications ranging from healthcare and cybersecurity to smart cities and industrial automation.
Keywords
federated learning; edge intelligence; distributed and trustworthy AI; privacy-preserving analytics
My research interests centre on hybrid approaches to representing and processing multimodal data, combining symbolic and neural methods at both foundational and applied levels. I am equally engaged in principled, accountable evaluation of generative AI, and co-founded the Measuring What Matters initiative, which aims to raise the quality and impact of evaluation practices. Beyond research, I serve as program manager for WARA Media and Language and chair the Scientific Team of the SELLMA LLM project.
Keywords
LLMs; multimodal systems; evaluation; graph-structured data
Timotheus leads the Agent and Reasoning group. One of his focus areas lies in the intersection of processes and genAI agents that currently disrupt scientific workflows. Timotheus has substantial experience in data science and data-driven AI, most notably as Principal Scientist at SAP, Europe's largest software vendor. The Agents and Reasoning group frequently collaborates with researchers across disciplines on AI projects, as well as with industry. Notable past and current collaboration partners are at organizations such at IBM, SAP, Ercisson, Oxford University, the Technical University of Munich, and Imperial College London.
Vicenç Torra: Privacy-preserving machine learning / Approximate reasoning and decision making
Research Description
We study privacy technologies on the whole pipeline from data until machine learning models. This includes privacy models (as k-anonymity, differential and integral privacy), privacy methods, high quality privacy-preserving synthetic data generators, and disclosure risk measures (including different types of attacks).
Keywords
data privacy; privacy-preserving machine learning; disclosure risk; membership inference attacks; synthetic data generators
Research Description
Research focuses on mathematical formalisms to model reasoning and decision making under uncertainty. We focus on tools related to fuzzy sets and non-additive measures and integrals.
Keywords
fuzzy sets and systems; non-additive measures and integrals; multi-criteria decision making; game theory
