Associate Professor of Machine Learning / Artificial Intelligence

Mohamed-Rafik Bouguelia is an Associate Professor of Machine Learning and Docent at the School of Information Technology, Halmstad University, Sweden. He is a member of the Center for Applied Intelligent Systems Research (CAISR) and the Department of Intelligent Systems (ISDD).

He joined Halmstad University in 2015 as a Researcher in AI, became an Assistant Professor in 2018, and was promoted to Associate Professor in 2021. He obtained the Docent title (habilitation to direct research) in Machine Learning in 2021. Before that, he was a Teaching and Research Fellow at the University of Lorraine (France) and the INRIA research center, where he earned his Ph.D. in Computer Science, specializing in Artificial Intelligence and Machine Learning in 2015. He holds two M.Sc. degrees in Computer Science: one from USTHB University (Algeria, 2010) and another from the University of Lorraine (France, 2011). He also earned a Bachelor's degree in Mathematics and Computer Science from USTHB University (2008).

His research focuses on meta-learning, transfer learning, interactive machine learning, anomaly detection, deep representation learning, and related topics, with applications across various domains.

Throughout his career, he has contributed extensively to research, education, and program development. He led the creation of the Applied Artificial Intelligence BSc program in 2022, serving as its program manager (2022–2025), and has also managed the AI/ML track of professional education programs designed for industry professionals. Additionally, he is actively involved in supervising MSc and PhD students across various research projects.

Interested in collaboration ?

I am open to research collaborations in Machine Learning. If you're interested in working together with me on research papers or projects, feel free to reach out via email or connect with me on LinkedIn.

Main Fields

Computer Science
Artificial Intelligence
Machine Learning
Data Science

Research Topics

Meta-Learning, Transfer Learning, Deep Learning, Representation Learning, Self-supervised Learning, Continual Learning, Federated Learning, Domain Adaptation, Interactive Machine Learning, Multitask Learning, Deep Neural Networks, Anomaly & Novelty Detection, Few-Shot Learning, Streaming Data Analysis, and more.

Application Domains

Industrial Automation and Manufacturing (including Predictive Maintenance and Data-Driven Fault Detection), Energy and Sustainability, Human and Machine Activity Recognition, AI for Healthcare, Smart Homes, Cybersecurity, Smart Cities, and more.

Teaching - BSc courses

Introduction to Data Science
Python Programming
Linux Administration
Applied Data Mining
Algorithms and Problem Solving
Data Structures
Object-Oriented Programming
Programming for Human-Computer Interaction

Teaching - MSc courses

Learning Systems
Data Mining
Supervised ML
Machine Learning for industry professionals

Teaching - PhD courses

Advanced Transfer Learning with Deep Neural Networks.

Additional Education Assignments

Program Director for the Applied Artificial Intelligence BSc program, Halmstad University, Sweden.
- I developed and launched the program in 2022, and served as the program manager (2022-2025). Also, contributed to strategic decisions as a member of the Steering Group for Education at the School of IT.
Responsible of the AI / Machine Learning track within the MAISTR professional education program.
- It includes courses such as: Smart Healthcare with Applications, Bayesian Statistics, Applied Deep Learning with PyTorch, Causal Inference, Machine Learning for Predictive Maintenance, Computer Vision, Explainable AI, Data-Driven Healthcare.
Head of the Computer Science Subject Group, Halmstad University, Sweden.
- Includes 20+ courses at the School of IT. I lead meetings with the group to approve course plans, review evaluations, and engage in school management seminars for teaching assessments.

PhD Supervision

2021–2025 Anna Vettoruzzo (Main Supervisor): Advancing Meta-Learning for Enhanced Generalization Across Diverse Tasks.
2019–2023 Zahra Taghiyarrenani (Co-supervised): From Domain Adaptation to Federated Learning.
2018–2022 Shiraz Farouq (Co-supervised): Large-Scale Monitoring of District Heating Substations.
2021–2022 Ece Calikus (Co-supervised): Algorithms and Applications for User-Centric Anomaly Detection.

Master Supervision

2025 A. Saleh, Anomaly Detection in Industrial Manufacturing Processes.
2025 L. Babic & S. Falkman, Predicting Energy Consumption for Heavy-Duty Vehicles.
2024 L. Mathew & P. Yadav, Machine Learning for Radio Frequency Fingerprinting.
2022 F. Nilsson, Motifs Discovery in Streaming Data with Applications to Fault Detection.
2021 J. Lindskog, Representation Learning for Deviation Detection in District Heating.
2020 L. Cheng & S. Sunadresh, Interactive Anomaly Detection with Reduced Expert Effort.
2020 R.A. Hamad, Towards Reliable, Stable and Fast Learning for Smart Home Activity Recognition.
2018 D. Sweidan, A General Framework for Discovering Multiple Data Groupings.
2017 K.B. Girĳeswara, Recognition of Vehicle Operations.

Opponent Assignments for MSc Theses

2024 S. Martin & S. Mohan, Predicting Auxiliary Energy Consumption for Volvo Trucks.
2022 V. Jayaraman, Enhancing Failure Prediction from Timeseries Histogram Data.
2021 R. Gunnarsson, Approaching Sustainable Mobility Using Graph Neural Networks.
2019 M. Srihari & Z. Gholipour, Anomaly Detection on Truck Histograms.
2018 K. Chen, Recurrent Neural Networks for Fault Detection (Air Compressor Failures).
2016 A. Palmqvist, Exploratory Data Analysis of Volvo Trucks Repair History.
2015 M. Mazur, Behavior Trees Evolution by Means of Genetic Programming.

Research Projects

2024–Now KEEPER (Knowledge Creation for Efficient and Predictable Industrial Operations). Sub-project Leader.
2015–2020 BIDAF (Big Data Analytics Framework). Developing efficient algorithms for mining massive streaming data.
2015 In4Uptime: Predictive maintenance using both on-board and off-board vehicle data.
2017–2021 SeMI (Self-Monitoring for Innovation). In collaboration with five companies. Focused on anomaly and fault detection, predictive maintenance, and joint human-machine learning. Co-supervised two PhD students.
2018 REMIND: Situation awareness in smart homes. Collaboration with University of Jaén.
2019–2023 EVE: Predictive maintenance of electric-powered buses. Co-supervised a PhD student.
2015–2023 CAISR: Applied Intelligent Systems Research. Supervised a PhD student.

Grants

2024–2028 KEEPER: 22.5M SEK turn-over (10M SEK from the Knowledge Foundation). Sub-project leader.
2020–2024 Recruitment: 3.9M SEK (25% from Alfa Laval AB). Project leader and main applicant for the recruitment of a faculty member focused on research in self-monitoring of complex systems in collaboration with the Alfa Laval company.
2018–2024 Expert-Competence: Grant for developing a professional education program over two stages. Stage 1 (2018-2020): 4.7M SEK turn-over (3M SEK from the Knowledge Foundation). Stage 2 (2020-2024): 23.8M SEK turn-over (14.3M SEK from the Knowledge Foundation).
2020-2021 RELIFE: 2.9M SEK for online courses development for industry professionals during COVID-19.

Publications

I published research papers in various venues, including highly ranked journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and Data Mining and Knowledge Discovery (DAMI), and top quality conferences such as SIAM International Conference on Data Mining (SDM) and Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). My publications are accessible on my Google Scolar Profile.

Collaboration

Collaborated with international institutions like Massachusetts Institute of Technology (MIT, USA), University of South Dakota (USA), Norwegian University of Science and Technology (Norway), University of Jaén (Spain), KTH Royal Institute of Technology, and RISE (Research Institutes of Sweden). My industry collaborations include companies like Toyota Material Handling Europe, Volvo Group, Alfa Laval AB, EasyServ AB, HMS Networks, Öresundskraft, HEM (Halmstad Energy), and ITESOFT Digital Automation.

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