About

I am a Lecturer in Embedded and Intelligent Systems at the University of Essex and part of the Robotics and Embedded Systems Research Group. I lead the Internet of Things and Machine Intelligence Lab, which is part of the Future Health Technologies cluster, and I co-lead the cluster with Dr Vito De Feo. You can also view my official University of Essex staff profile. Previously, I was a Postdoctoral Research Fellow at the Institute of Analytics and Data Science, University of Essex. Before that, I worked as a Research Associate in Applied Big Data Analysis at the MRC-PHE Centre for Environment and Health, King's College London, where I explored links between the environment and human health through the analysis of very large time series datasets. I was part of the COPE study team, which aimed to reduce the frequency of hospital and GP visits by patients with respiratory disease. I also served as a Research Fellow in large-scale data analytics for the Internet of Things at the 5G Innovation Centre, University of Surrey, where I was a work package leader in large-scale data analysis and seamless integration of data sources in the CityPulse project.

I received a B.Sc. degree in Computer Engineering from Near East University (NEU), Nicosia, Turkish Republic of Northern Cyprus (TRNC), in 2005. I earned an M.Sc. degree from the University of Essex and a PhD from Queen Mary University of London (QMUL). My thesis was titled Automatic Ontology Generation Based on Semantic Audio Analysis. I received a full scholarship for my BSc from NEU and TRNC, and a full scholarship for my PhD from the NEMA and EPSRC projects at QMUL.

I serve as an editor at PLOS ONE and act as a reviewer for the EPSRC, the European Commission, and numerous journals including the IEEE Internet of Things Journal, ACM Transactions on Internet of Things, IEEE Transactions on Emerging Topics in Computing, IEEE Transactions on Industrial Informatics, and Neural Computing and Applications. I am also a member of the EPSRC Peer Review College and a registered expert with the European Commission.

Research

My research interests include Internet of Things, Edge Computing, Machine Learning, Time Series Analysis, Signal Processing, Semantic Web, and Federated Learning. I am also focused on high-order tensor decompositions, multi-aspect learning, multimodal learning, explainable AI, and energy-efficient AI technologies for healthcare applications.

Teaching

  • CE335 - Digital Signal Processing
  • CE201 - Supervision of undergraduate group projects
  • CE301 - Supervision of final year undergraduate projects
  • CE903 - Supervision of postgraduate group projects
  • CE901 - Supervision of postgraduate projects

Supervision

I have supervised several MSc students and previously supervised PhD student Dat Ngo. I am always happy to hear from motivated prospective PhD students. If you are interested in working on intelligent systems, Internet of Things (IoT), multimodal learning, or IoT healthcare monitoring technologies, please do not hesitate to get in touch.

Grants

  • Co-Investigator - Chorus Intelligence KTP Project, Innovate UK, £207,952 (2025–present)
  • Principal Investigator - CRU Wine AI Project, CRU Wine Ltd, Innovate UK, £280,385 (Pending)
  • Principal Investigator - Above Surveying Ltd KTP3, Innovate UK, £236,319 (2021–2023)
  • Principal Investigator - Above Surveying contract research application, Above Surveying Ltd, £56,264 (2021)
  • Principal Investigator - Stephenson Harwood KTP, Innovate UK, £238,988 (2020–2022)
  • Principal Investigator - AI-Assisted Decision-Making for Cancer Pathways, NHS Trust, £41,172 (2019)
  • Principal Investigator - Remote Monitoring for Gestational Diabetes, GCRF, £8,000 (2019)

Past Projects

  • Remote Detection of Gestational Diabetes Mellitus (2019–2022) – A pilot study combining wearable and questionnaire data for early detection in South Africa. Collaborators: Dr. Alastair van Heerden, Prof. Lucilla Poston.
  • COPE Study (2016–2018) – Environmental health research on respiratory disease. Conducted at King's College London in collaboration with Imperial College London and the University of Cambridge.
  • EU CityPulse Project (2014–2016) – Large-scale data analytics framework for smart cities at the University of Surrey.

Publications

Journal Articles

  • Kolozali, S., Fasli, M., White, SL., Norris, S. and van Heerden, A., (2024). Explainable Early Prediction of Gestational Diabetes Biomarkers by Combining Medical Background and Wearable Devices: A Pilot Study with a Cohort Group in South Africa. IEEE Journal of Biomedical and Health Informatics. 28 (4), 1860-1871
  • Kolozali, S., Chatzidiakou, L., Rones, R., Quint, JK., Kelly, F. and Barratt, B., (2023). Early Detection of COPD Patients’ Symptoms with Personal Environmental Sensors: A Remote Sensing Framework using Probabilistic Latent Component Analysis with Linear Dynamic Systems. Neural Computing and Applications. 35 (23), 17247-17265
  • van Heerden, A., Kolozali, Ş. and Norris, SA., (2023). Feasibility and acceptability of continuous at-home glucose monitoring during pregnancy: a mixed-methods pilot study. South African Journal of Clinical Nutrition. 36 (3), 100-107
  • Wachter, EW., Kasap, S., Kolozali, S., Zhai, X., Ehsan, S. and McDonald-Maier, K., (2022). Using Machine Learning for Anomaly Detection on a System-on-Chip under Gamma Radiation. Nuclear Engineering and Technology. 54 (11), 3985-3995
  • Van Heerden, A., Comulada, WS., Kolozali, Ş. and Kohrt, B., (2021). Drawing open the curtain on home-based interventions.. mHealth. 7 (2), 18-18
  • van den Brink, L. et al., (2019). Best Practices for Publishing, Retrieving, and Using Spatial Data on the Web. Semantic Web. 10 (1), 95-114
  • Kolozali, S. et al., (2019). Observing the Pulse of a City: A Smart City Framework for Real-time Discovery, Federation, and Aggregation of Data Streams. IEEE Internet of Things Journal. 6 (2), 2651-2668
  • Quint, JK. et al., (2018). Recruitment of patients with Chronic Obstructive Pulmonary Disease (COPD) from the Clinical Practice Research Datalink (CPRD) for research. npj Primary Care Respiratory Medicine. 28 (1), 21-
  • Hashmi, M. et al., (2018). Preliminary results from the COPE study using primary-care electronic health records and environmental modelling to examine COPD exacerbations. British Journal of General Practice. 68 (suppl 1), bjgp18X696749-bjgp18X696749
  • Kolozali, S. et al., (2016). On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data. IEEE Internet of Things Journal. 3 (6), 1084-1098
  • Puiu, D. et al., (2016). CityPulse: Large Scale Data Analytics Framework for Smart Cities. IEEE Access. 4, 1086-1108
  • Kolozali, S., Barthet, M., Fazekas, G. and Sandler, M., (2013). Automatic Ontology Generation for Musical Instruments Based on Audio Analysis. IEEE Transactions on Audio, Speech, and Language Processing. 21 (10), 2207-2220

Conference Papers

  • Kolozali, S., Chatzidiakou, L., Jones, R., Quint, JK., Kelly, F. and Barratt, B., A probabilistic multi-aspect learning model for the early detection of COPD patients' symptoms
  • Turetta, C., Varasteh, M., Kolozali, Ş. and Pravadelli, G., (2024). Leveraging mmWave for Contactless Breath Rate Estimation of Moving Subjects
  • Ngo, D., Pham, L., Phan, H., Tran, M., Jarchi, D. and Kolozali, Ş., (2023). An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies
  • Ngo, D., Pham, L., Hoang, T., Kolozali, S. and Jarchi, D., (2022). Audio-Based Deep Learning Frameworks for Detecting COVID-19
  • Ahrabian, A., Kolozali, S., Enshaeifar, S., Cheong-Took, C. and Barnaghi, P., (2017). Data analysis as a web service: A case study using IoT sensor data
  • Barratt, B., Chatzidiakou, L., Moore, E., Quint, J., Beevers, S., Kolozali, S., Kelly, F., Jones, R. and Smeeth, L., (2017). Characterisation of COPD exacerbations using personal environmental exposure monitoring
  • Farajidavar, N., Kolozali, S. and Barnaghi, P., (2016). Physical-cyber-social similarity analysis in smart cities
  • Kolozali, S., Bermudez-Edo, M., Puschmann, D., Ganz, F. and Barnaghi, P., (2014). A Knowledge-Based Approach for Real-Time IoT Data Stream Annotation and Processing
  • Kolozali, Ş., Elsaleh, T. and Barnaghi, P., (2014). A validation tool for the W3C SSN ontology based sensory semantic knowledge
  • Kolozali, S., Fazekas, G., Barthet, M. and Sandler, MB., (2014). A framework for automatic ontology generation based on semantic audio analysis
  • Barthet, M., Anglade, A., Fazekas, G., Kolozali, S. and Macrae, R., (2011). Music recommendation for music learning: Hotttabs, a multimedia guitar tutor
  • Kolozali, S., Barthet, M., Fazekas, G. and Sandler, M., (2011). Knowledge representation issues in musical instrument ontology design
  • Kolozali, S., Barthet, M., Fazekas, G. and Sandler, M., (2011). Towards the Automatic Generation of a Semantic Web Ontology for Musical Instruments
  • Tidhar, D., Fazekas, G., Kolozali, S. and Sandler, M., (2009). Publishing music similarity features on the semantic web

Reports and Papers

  • Farajidavar, N., Kolozali, S. and Barnaghi, P., (2017). A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams

CV

Download My CV (PDF)

Contact

Email: sefki.kolozali@essex.ac.uk

Office: School of Computer Science and Electronic Engineering, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK

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