A journey through academia, research, and innovation
Dr. Addi Ait-Mlouk is an Associate Professor (Docent) of Artificial Intelligence and Data Science at the School of Informatics, University of Skövde, Sweden. He served as the Director of the Data Science Master's Programme (2023–2024), showcasing his leadership in shaping AI education and mentoring the next generation of data scientists.
Before joining the University of Skövde, Addi was a Postdoctoral Researcher at Uppsala University (2020–2022), where he worked on a Scalable Federated Learning project at the Department of Information Technology, Division of Scientific Computing. Prior to that, he was a Postdoctoral Researcher at Umeå University (2018–2020), contributing to a WASP project on Privacy-Aware Data Federation for heterogeneous registry data. His research focuses on solving real-world challenges through cutting-edge AI and data science techniques. His expertise spans Federated Learning, Machine Learning, Data Mining, Information Retrieval, Conversational AI, Open Linked Data, Knowledge Graphs, and Natural Language Processing.
Addi holds a Master's degree in Information Systems Engineering and a PhD in Data Mining from Cadi Ayyad University, Marrakech, earned in 2011 and 2018, respectively. Beyond academia, he is a highly sought-after consultant and trainer specializing in CRM, ERP, Data Science, and AI. Passionate about emerging technologies, he collaborates with businesses to drive digital transformation and innovation. He actively contributes to the scientific community as a reviewer and program committee member for leading conferences and journals, including ICDM, ECIR, and ECML-PKDD.
Born in Imi N'Ouakka, Morocco, in 1990, Addi is always open to new collaboration opportunities in academia and industry. Feel free to connect with him at aitmlouk@gmail.com.
Professional experience and educational background
Artificial Intelligence and Data Science
Leading research in Federated Learning, served as Director of the Data Science Master's Programme (2023–2024).
Scalable Federated Machine Learning
Department of Information Technology, Division of Scientific Computing.
Privacy-aware Federated Database Infrastructure (2018-2020)
WASP project focusing on privacy-preserving data integration and analysis.
Teaching and research in computer science, focusing on data science and machine learning applications.
Supporting teaching activities and conducting research in data mining and machine learning.
Doctoral research focused on data mining and association rules mining with applications to road safety. Developed innovative approaches for extracting relevant patterns from large datasets.
Master's degree specializing in information systems engineering with focus on data management, software engineering, and enterprise systems.
Bachelor's degree in software engineering with focus on programming, algorithms, and software development methodologies.
Professional consulting and training services
Helping businesses design and integrate tailored AI and machine learning solutions to optimize operations and drive innovation.
Implementing privacy-preserving, collaborative AI systems using federated learning and decentralized architectures for sensitive and distributed data environments.
Delivering actionable insights through advanced data analytics, predictive modeling, and decision support systems.
Building and customizing ERP systems enhanced with AI capabilities to streamline business processes and improve efficiency.
Providing hands-on training and workshops in AI, Data Science, Federated Learning, and ERP systems for individuals, teams, and organizations.
The principles that guide my academic and research endeavors
Constantly seeking new approaches and breakthrough solutions to complex problems.
Fostering meaningful partnerships and knowledge sharing across disciplines.
Maintaining the highest standards in research, teaching, and academic integrity.
Creating research that makes a meaningful difference in society and industry.