Kaveh Amouzgar

Short presentation

I am an Assistant Professor in Informatics at the Department of Civil and Industrial Engineering. My work centers on data analytics and advanced optimization techniques, including simulation-based, multi-objective, and surrogate-assisted approaches. I am also dedicated to advancing machine learning and extended reality (XR) applications to drive innovation in industrial environments.

Keywords

• machine learning
• statistical modeling and machine learning
• data analytics
• artifical intelligence
• c61 optimization techniques and programming models
• meta-modelling
• multi-objective optimization

Biography

I have an engineering background with a BSc in Mechanical Engineering (2003) from Iran. After graduation, I worked in the automotive and oil and gas industries for eight years before moving to Sweden in 2010 to pursue an MSc in Product Development and Materials Engineering.

During my master’s thesis, I became fascinated by the application of multi-objective optimization and simulation-based optimization using the finite element method (FEM) in real-world industrial cases. To further explore this interest, I continued researching and developing machine learning methods integrated with optimization algorithms as a PhD student.

After receiving my PhD in Informatics from the University of Skövde in 2018, I focused my research on data analytics while teaching courses in optimization and operations research as a senior lecturer. I was also responsible for the master’s program in Intelligent Automation at the University of Skövde for one year before joining Uppsala University in early 2021 as an Assistant Professor.

I am currently part of the Industrial Analytics Group within the Division of Industrial Engineering and Management.

Research

My research interests include simulation based optimization and the application of data analytics—particularly machine learning methods—in industrial settings. I am also engaged in research and education on extended reality (XR) applications for industry.

Research Projects:

MANUFACTOR – A Multi-Agent System for Cognitive and Physical Augmentation in Manufacturing is a Horizon Europe–funded research project focusing on human-centered manufacturing within the context of Industry 5.0. The project develops a multi-agent system that integrates extended reality (XR), artificial intelligence, human digital twins, and multimodal sensing to support cognitive and physical augmentation of industrial operators and managers. The project with a total budget of €6M is carried out by a consortium of 14 partners across Europe and aims to improve training, ergonomics, decision-making, and sustainability through adaptive XR-based guidance and explainable AI, validated in industrial pilots and learning factory environments.

ARTISAN – Agentic Reality Training in Skill Acquisition for Next-Generation Manufacturing is a Vinnova-funded project developing an AI-powered XR training system to capture and transfer expert knowledge in manufacturing. By combining real-time data collection, AI-based process analysis, and immersive XR guidance, ARTISAN enables new operators to learn complex tasks without requiring direct supervision. The project aims to improve training efficiency, reduce errors, and support knowledge retention across Swedish industry. ARTISAN is a collaboration between AugmentedRealm, Hitachi Energy, Solme, Ekets Group, and Uppsala University.

AI-COMPETE – Coordinated Multi-Agent AI-Powered Decision Support System for Sustainable Manufacturing is a Vinnova-funded project developing a multi-agent AI system to optimize production and energy use in manufacturing. The system integrates reinforcement learning, digital twins, and coordinated decision-making to improve productivity while reducing environmental impact. The project aims to make advanced AI solutions accessible to industry, especially SMEs, supporting Sweden’s transition toward sustainable and efficient manufacturing. AI-COMPETE is a collaboration between the University of Skövde (coordinator), Uppsala University, Volvo Penta, Scania, Daloc AB, and Evoma AB.

PreMoDIPS – Predictive Modelling for Data-Intensive Industrial Processes and Systems is a research project developing predictive modelling techniques to support data-driven decision-making in manufacturing. The project focuses on creating methods capable of handling both quantitative and qualitative inputs and outputs, as well as inherent uncertainty in industrial systems. By combining predictive analytics, diagnostic analysis, and simulation-based optimization, PreMoDIPS enables companies to automate planning and optimize processes and products more efficiently. The project is a collaboration between the University of Skövde and AB SKF, funded by the Knowledge Foundation, and contributes to sustainable industrial development and education through applied research and training.

Pedagogical Projects:

PUMA2025: Educating Machine Design with Extended Reality. This project develops an extended reality (XR) learning environment to teach machine design in the Mechanical Engineering program at Uppsala University. By integrating XR as a virtual lab, students can explore and interact with complex mechanical components—such as shafts, bearings, and gears—in realistic 3D contexts. The project aims to reduce cognitive load, improve understanding of design principles, and increase student engagement.

TUFF2024: GenAI in education: AI as tutor and student. This project explores how generative AI can be integrated into teaching and learning. In the master’s course Logistic Systems Modeling and Optimization, AI will be used both as a tutor to help students deepen their understanding and as a “student” that learners teach and critique. This dual approach aims to strengthen critical thinking, improve AI literacy, and support active engagement with course content.

PUMA2023: Immersive Technology for Educating Mechanical Engineers; HoloMech an Application in Mixed Reality. This project developed HoloMech, a mixed-reality application for teaching Solid Mechanics in engineering education. By combining interactive 3D visualization and hands-on experiences with HoloLens, the project aimed to reduce cognitive load, improve students’ understanding of complex concepts, and increase engagement. HoloMech was implemented as a virtual lab in the Solid Mechanics course at Uppsala University and evaluated through both quantitative and qualitative methods.