• Environmental informatics
• Computational intelligence methods and tools for mechanical engineering and health (with applications including environment, energy, tribology, aeromechanics, exhaust aftertreatment modelling, allergy, aerobiology, etc.)
• Web-based urban environment management and information systems
• Quality of life - environment related - information services
• Air quality microsensors and the computational improvement of their performance

1. KASTOM: An Innovative sustem for air quality monitoring and forecasting. Main Role: responcible for AQ microsensor use and computational improvement, AQ data fusion.
2. Evolution of Computational Intelligence in Environmental Engineering - Generalization, Improvement, Optimal Combination of Methodologies in Air and Water Resources Quality Problems. Main role: scientific responsibility & coordination. Post-doc Researcher:  Dr. Ioannis Kontos
3. Creation of an external Machine Learning Service for the FMI-Enfuser modelling system. Role: Contractor of the Finnish Meteorological Institute.
4. INCENTIVE: Establishing Citizen Science Hubs in European Research Performing and Funding Organisations to drive institutional change and ground Responsible Research and Innovation in society (EU Horizon 2020). Main Role: support the development of a citizen science hub at Aristotle University of Thessaloniki, the first ever Greek university to move towards this direction.
5. Studying NO2 levels in Thessaloniki with the aid of diffusion tubes. Main role: setting up, managing and analysing measurements (collaboration with www.duh.de)

1. EU-citizen.science: Share, initiate and learn - citizen science in Europe EU Horizon 2020. Main Role: third party supporting community building and platform use. Contact: citizenscience [at symbol] meng.auth.gr
2. URwatair: a citizen science project for urban air quality and rain water management. Role: repsoncible for the AQ part of the project. Contact: citizenscience [at symbol] meng.auth.gr
3. FMI ENFUSER. Role: Contractor of the Finnish Meteorological Institute participating in the investigation, testing and extention of the fusion approach to include Computational Intelligence algorithms (indicatively Artificial Neural Networks) .
4. COST Action PortASAP: European network for the promotion of portable, affordable and simple analytical platforms. Role: Management Commitee member
5. Erasmus + KA2: Development of a technology transfer model at universities. Main role: Scientific coordination, contributor to the technology transfer model anong project partners (Dec. 2019-Nov. 2021).
6. Netmon: An International training cource (run every year), devoted to "Low-cost Environmental Monitoring – from sensor principles to novel services". Role: event co-organiser and co-lecturer

Selected Publications

1. Bagkis E., Kassandros Th., Karatzas K., (2022). Learning calibration functions on the fly: Hybrid batch-online stacking ensembles for the calibration of low-cost air quality sensor networks in the presence of concept drifts, Atmosphere, 13(3), 416. https://doi.org/10.3390/atmos13030416
2. Bagkis Ε., Kassandros T., Karteris Μ., Karteris Α., Karatzas Κ (2021), Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device. Atmosphere 12(2), 251. https://doi.org/10.3390/atmos12020251
3. Viana, M.; Karatzas, K.; Arvanitis, T.; Reche, C.; Escribano, M.; Ibarrola-Ulzurrun, E.; Adami, P.E.; Garrandes, F.; Bermon, S (2022), Air Quality Sensors Systems as Tools to Support Guidance in Athletics Stadia for Elite and Recreational Athletes. J. Environ. Res. Public Health 2022, 19(6), 3561; https://doi.org/10.3390/ijerph19063561
4. Kontos Y.N., Kassandros T., Katsifarakis K.L., Karatzas K. (2021), Deep Learning Modeling of Groundwater Pollution Sources. In: Iliadis L., Macintyre J., Jayne C., Pimenidis E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. pp. 165-177, https://doi.org/10.1007/978-3-030-80568-5_14
5. Furxhi I., Murphy F., Mullins M., Arvanitis A., Poland C. (2020), Practices and Trends of Machine Learning Application in Nanotoxicology, Nanomaterials 10(1):116; doi:10.3390/nano10010116
6. Vohland K., Sauermann H., Antoniou V., Balazs B., Göbel C., Karatzas K., Mooney P., Perelló J., Ponti M., Samson R. and Winter S. (2020). Citizen Science and Sustainability Transitions. Research Policy 49(5), https://doi.org/10.1016/j.respol.2020.103978
7. Borrego C., Costa A.M., Ginja J., Amorim M., Karatzas K., Sioumis Th., Katsifarakis N., Konstantinidis K., De Vito S., Esposito E., Smith P., André N., Gérard P., Francis L.A.,. Castell N., Viana M., Minguillón M.C., Reimringen W., Otjes R.P., v.Sicard O., Pohle R., Elen B., Suriano D., Pfister V., Prato M., Dipinto S., Penza M. (2018), Assessment of air quality microsensors versus reference methods: The EuNetAir joint exercise– part II, Atmospheric Environment 193, pp. 127-142, https://doi.org/10.1016/j.atmosenv.2018.08.028
8. Karatzas K., Papamanolis L., Katsifarakis N., Riga M.  B. Werchan, M. Werchan, U. Berger, and K.C. Bergmann (2018), Google Trends reflect allergic rhinitis symptoms related to birch and grass pollen seasons, Aerobiologia 34(4), pp. 437-444, https://doi.org/10.1007/s10453-018-9536-4
9. Karatzas K. and Katsifarakis N. (2018), Modelling of Household Electricity Consumption with the Aid of Computational Intelligence Methods.  Advances in Building Energy Research 12(1), pp. 84-96, https://doi.org/10.1080/17512549.2017.1314831
10. Katsifarakis N., Riga M., Voukantsis D. and Karatzas K. (2016), Computational Intelligence methods for rolling bearing fault detection, Journal of the Brazilian Society of Mechanical Sciences and Engineering 38 (6), pp. 1565-1574. doi:10.1007/s40430-015-0458-6 (First online: 08 December 2015)