Senior lecturer in Computer Science and Marine Engineering
I started my career in the Royal Swedish Navy in 2001, and have sailed as a Marine Engineering officer on fast attack crafts, corvettes and submarines. During these years I have worked with gas turbines, diesel engines as well as battery electric propulsion. I live in Kalmar and have a wife and two kids. I have a keen interest in new technology and is also an amateur runner. In my spare time, I am always busy with hobby projects and reading books. I have also been active as a chairman of the Kalmar Secular Humanists, promoting a secular and scientific worldview.
I have always had a very keen interest in computers and new technology, after and during my PhD I started the transition from mechanical/marine engineering to computer science, with the focus on applying machine learning to solve problems. I am now heavily invested in the area of IoT, and are teaching several courses in IoT, mostly with an applied focus.
Since 2020 I am a part of the Computer Science department, teaching courses in Internet of Things, Web Intelligence and Python programming.
In 2020 I was living in Vancouver and teaching marine engineering and data analysis for M.Sc students at University of British Columbia, NAME (Naval Architecture and Marine Engineering). I was also involved in the Marine Engineering course at UBC during the last part of 2020 at distance from Sweden.
I have been the course administrator for the summer course Applied Internet of Things both the summer of 2020 and 2021, comprising of hundreds of students from all over the world. I also teach M.Sc courses in Applied IoT, as well as introductory programming in Python, machine learning (web intelligence). I am living by the motto that it should be fun - and what I don't know about is compensated by my enthusiasm for the subject.
My previous academic carreer, I have been teaching the pumping technology course, hydromechanics and machinery systems for the marine engineer programme.
Currently I am researching in the area of Internet of Things, I am currently running a larger IoT-lab project aimed for strengthen the competitiveness for small and medium sized companies in the Linnaeus region (Kalmar and Växjö).
In my research I have studied energy efficiency in shipping from the data perspective.
The studies are about Organic Rankine cycles (ORC) and energy and exergy analysis, as well as machine learning (ML) methods for energy predictions. The research is an applied research with a marine engineerings approach. This means that I am focused on research based on operational data and results that can be applied on board.
Programme Director of Software Engineering
Project leader of IoT SME
My research groups
Engineering Resilient Systems (EReS) The Engineering Resilient Systems (EReS) Research Lab conducts research in the area of system resilience. It focuses on investigating (and experimenting with)…
Maritime science The research in maritime science includes at present: 1) working life, organisation and risk management, 2) the environmental effects of shipping and marine spatial planning, 3)…
Smart Industry Group Smart Industry Group (SIG) is an interdisciplinary research group featuring expertise from computer science and mechanical engineering. SIG's focus is making production and…
My ongoing research projects
Project: DigIT Hub Sweden DigIT Hub Sweden is one of four digital innovation hubs in Sweden with focus on bringing businesses into the future through a one-stop-shop with a wide range of services,…
Project: Digital acceleration for medium size sustainable cities (DIACCESS) The aim of the project is to solve real societal challenges by combining municipal administrations and municipal companies…
Project: Energy Efficiency in Shipping The research in this project is conducted with an operational point of view, which means that using measured data from ships in operation is in focus. So far the…
Project: IoT lab for SME 2.0 The aim of this project is to develop a well-established network of companies in the Linnaeus region that can benefit from each other's expertise and products within…
Seed project: Biosensor Testbed at the IoT Lab The objective of this project is to explore the potential applications of biosensors for the continuous monitoring and management of various health…
Seed project: Development of an intelligent wearable – the DIWAH study The overall goal of the research in this seeding project within the Linnaeus University Center for Data Intensive Sciences and…
My completed research projects
Article in journal (Refereed)
Xie, X., Sun, B., Li, X., Olsson, T., Maleki, N., et al. (2023). Fuel Consumption Prediction Models Based on Machine Learning and Mathematical Methods. Journal of Marine Science and Engineering. 11 (4).
Hedayati, S., Maleki, N., Olsson, T., Ahlgren, F., Seyednezhad, M., et al. (2023). MapReduce scheduling algorithms in Hadoop : a systematic study. Journal of Cloud Computing : Advances, Systems and Applications. 12.
Musaddiq, A., Olsson, T., Ahlgren, F. (2023). Reinforcement-Learning-Based Routing and Resource Management for Internet of Things Environments : Theoretical Perspective and Challenges. Sensors. 23 (19).
Baldi, F., Ahlgren, F., Nguyen, T., Thern, M., Andersson, K. (2018). Energy and exergy analysis of a cruise ship. Energies. 11 (10). 1-41.
Mondejar, M.E., Ahlgren, F., Thern, M., Genrup, M. (2017). Quasi-steady state simulation of an organic Rankine cycle for waste heat recovery in a passenger vessel. Applied Energy. 185 (Special Issue Part 2). 1324-1335.
Ahlgren, F., Mondejar, M.E., Genrup, M., Thern, M. (2016). Waste Heat Recovery in a Cruise Vessel in the Baltic Sea by Using an Organic Rankine Cycle : A Case Study. Journal of engineering for gas turbines and power. 138 (1).
Baldi, F., Ahlgren, F., Melino, F., Gabrielii, C., Andersson, K. (2016). Optimal load allocation of complex ship power plants. Energy Conversion and Management. 124. 344-356.
Conference paper (Refereed)
- Maleki, N., Musaddiq, A., Mozart, D., Olsson, T., Omareen, M., et al. (2023). DeltaBin : An Efficient Binary Data Format for Low Power IoT Devices. 2023 International Conference on Computer, Information and Telecommunication Systems (CITS), Genoa, Italy, 2023.
- Musaddiq, A., Maleki, N., Palma, F., Olsson, T., Toll, D., et al. (2023). Industry-Academia Cooperation : Applied IoT Research for SMEs in South-East Sweden. Internet of Things. GIoTS 2022. 397-410.
- Maleki, N., Musaddiq, A., Toll, D., Palma, F., Olsson, T., et al. (2022). DynaSens : Dynamic Scheduling for IoT Devices Sustainability. 2022 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, CoBCom 20222022.
- Zapico, J.L., Ahlgren, F., Zennaro, M. (2022). Insect biodiversity in agriculture using IoT : opportunities and needs for further research. IEEE Global Communications Conference, 7-11 December 2021, Madrid, Spain : Connecting Cultures around the Globe. 1-5.
- Palma, F., Olsson, T., Wingkvist, A., Ahlgren, F. (2022). Investigating the Linguistic Design Quality of Public, Partner, and Private REST APIs. Proceedings - 2022 IEEE International Conference on Services Computing, SCC 2022. 20-30.
- Mohammadian, M., Maleki, N., Olsson, T., Ahlgren, F. (2022). Persis : A Persian Font Recognition Pipeline Using Convolutional Neural Networks. 2022 12th International Conference on Computer and Knowledge Engineering (ICCKE), Mashhad, Iran, Islamic Republic of. 196-204.
- Musaddiq, A., Maleki, N., Palma, F., Mozart, D., Olsson, T., et al. (2022). Internet of Things for Wetland Conservation using Helium Network : Experience and Analysis. 12th International Conference on the Internet of Things, IoT 2022, Delft 7 - 10 November 2022. 143-146.
- Pena, B., Luofeng, H., Ahlgren, F. (2020). A Review on Applications of Machine Learning in Shipping Sustainability. SNAME Maritime Convention 2020 – A Virtual Event 29 September- 2 October.
- Ahlgren, F., Mondejar, M.E., Thern, M. (2019). Predicting dynamic fuel oil consumption on ships with automated machine learning. Innovative Solutions for Energy Transitions : Proceedings of the 10th International Conference on Applied Energy (ICAE2018). 6126-6131.
- Ahlgren, F., Thern, M. (2018). Auto Machine Learning for predicting Ship Fuel Consumption. Proceedings of ECOS 2018 - the 31st International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems.
- Baldi, F., Nguyen, T., Ahlgren, F. (2016). The application of process integration to the optimisation of cruise ship energy systems : a case study. ECOS 2016 : 29th International Conference on Efficiency, Cost, Optimization, Simulation and Envirionmental Impact of Energy Systems. June 19-23 2016.
- Baldi, F., Ahlgren, F., Nguyen, T., Gabrielii, C., Andersson, K. (2015). Energy and exergy analysis of a cruise ship. Proceedings of ECOS 2015 - the 28th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems.
- Ahlgren, F., Mondejar, M.E., Genrup, M., Thern, M. (2015). Waste Heat Recovery in a Cruise Vessel in the Baltic Sea by Using an Organic Rankine Cycle : A Case Study. ASME Turbo Expo 2015: Turbine Technical Conference and Exposition : Volume 3: Coal, Biomass and Alternative Fuels; Cycle Innovations; Electric Power; Industrial and Cogeneration, Montreal, Quebec, Canada, June 15–19, 2015. 43392-43416.
- Ahlgren, F., Österman, C. (2015). A social sustainability perspective on an environmental intervention to reduce ship emissions. Creating Sustainable Work-environments : Proceedings of NES2015, Nordic Ergonomics Society 47th Annual Conference, 01-04 November 2015, Lillehammer, Norway. A4-12-A4-15.
- Mondejar, M.E., Ahlgren, F., Thern, M., Genrup, M. (2015). Study of the on-route operation of a waste heat recoverysystem in a passenger vessel. Clean, Efficient and Affordable Energy for a Sustainable Future. 1646-1653.
Chapter in book (Refereed)
- Ahlgren, F., Thern, M., Genrup, M., Mondejar, M.E. (2018). Energy integration of organic rankine cycle, exhaust gas recirculation and scrubber. Trends and challenges in maritime energy management. Cham, Switzerland, Springer. 157-168.
Article, review/survey (Refereed)
Dalipi, F., Zdravkova, K., Ahlgren, F. (2021). Sentiment Analysis of Students’ Feedback in MOOCs : A Systematic Literature Review. Frontiers in Artificial Intelligence. Frontiers Media S.A.. 4.
Chapter in book (Other academic)
- Manzoni, P., Zennaro, M., Ahlgren, F., Olsson, T., Prandi, C. (2023). Crowdsourcing Through TinyML as aWay to Engage End-Users in IoT Solutions. Mobile Crowdsourcing : From Theory to Practice. Switzerland, Springer. 359-387.
Doctoral thesis, comprehensive summary (Other academic)
- Ahlgren, F. (2018). Reducing ships' fuel consumption and emissions by learning from data. Doctoral Thesis. Växjö, Linnaeus University Press. 204.