Dr Sefki Kolozali
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Email
sefki.kolozali@essex.ac.uk -
Telephone
+44 (0) 1206 873302
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Location
5A.523, Colchester Campus
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Academic support hours
Tuesday between 10:30AM (zoom: 91390146531)
Profile
Biography
I am a Lecturer in Embedded and Intelligent Systems at the University of Essex, and I am part of the Robotics and Embedded Systems Research Group. My interests include the fields of Internet of Things, Edge Computing, Machine Learning, Time Series Analysis, Signal Processing, Semantic Web, Federated Learning and their applications to various healthcare scenarios. Additionally, I have a particular interest in high-order tensor decompositions, multi-aspect learning, multimodal learning, explainable AI, and energy-efficient AI technologies. I am a member of the EPSRC Peer Review College and a registered expert with the European Commission. Additionally, I serve as an editor at PLOS ONE and as a reviewer for both the EPSRC and the European Commission. Furthermore, I act as a reviewer for several prominent 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 leading the Internet of Things and Machine Intelligence Lab as part of the Future Health Technologies cluster. 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. I worked on exploring 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 worked 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 received an M.Sc. degree from the University of Essex and a PhD degree from Queen Mary University of London (QMUL). My thesis was titled Automatic Ontology Generation Based on Semantic Audio Analysis. I received full scholarship for BSc from the NEU and TRNC, and full scholarship for PhD from the NEMA and EPSRC projects at QMUL.
Qualifications
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PhD on Automatic Ontology Generation Based on Semantic Audio Analysis Queen Mary University of London, (2013)
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MSc in E-Commerce Technologies University of Essex, (2008)
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BSc in Computer Engineering Near East University, (2005)
Appointments
University of Essex
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Postdoctoral Research Fellow, Institute for Analytics and Data Science, University of Essex (2/7/2018 - 14/1/2020)
Other academic
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Lecturer in Embedded and Intelligent Systems, School of Computer Science and Electronic Engineering, University of Essex (15/1/2020 - present)
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Research Associate in Applied Big Data Analysis, MRC-PHE Centre for Environment and Health, Faculty of Life Sciences, King's College London (1/9/2016 - 30/4/2018)
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Research Fellow in Large-scale data analytics for the Internet of Things, 5G Innovation Centre, Institute for Communication Systems (ICS), University of Surrey (4/2/2014 - 31/8/2016)
Research and professional activities
Research interests
Internet of Things
Machine Learning
Semantic Web
Signal Processing
Time Series Analysis
Internet of Things in Healthcare
Multi-aspect Learning
Multimodal Learning
Explainable AI
Energy Efficient AI Models
High-order Tensor Decompositions
Edge Computing
Federated Learning
Teaching and supervision
Current teaching responsibilities
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Team Project Challenge (CE201)
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Digital Signal Processing (CE335)
Current supervision
Publications
Journal articles (12)
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., Barnaghi, P., Tandy, J., Atemezing, GA., Atkinson, R., Cochrane, B., Fathy, Y., García-Castro, R., Haller, A., Harth, A., Janowicz, K., Kolozali, S., van Leeuwen, B., Lefrançois, M., Lieberman, J., Perego, A., Le Phuoc, D., Roberts, B., Taylor, K. and Troncy, R., (2019). Best Practices for Publishing, Retrieving, and Using Spatial Data on the Web. Semantic Web. 10 (1), 95-114
Kolozali, S., Bermudez-Edo, M., FarajiDavar, N., Barnaghi, P., Gao, F., Intizar Ali, M., Mileo, A., Fischer, M., Iggena, T., Kuemper, D. and Tonjes, R., (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., Moore, E., Lewis, A., Hashmi, M., Sultana, K., Wright, M., Smeeth, L., Chatzidiakou, L., Jones, R., Beevers, S., Kolozali, S., Kelly, F. and Barratt, B., (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., Wright, M., Sultana, K., Barratt, B., Chatzidiakou, L., Moore, E., Kolozali, Ş., Jones, RL., Beevers, S., Smeeth, L., Kelly, FJ. and Quint, JK., (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., Puschmann, D., Bermudez-Edo, M. and Barnaghi, P., (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., Barnaghi, P., Tonjes, R., Kumper, D., Ali, MI., Mileo, A., Xavier Parreira, J., Fischer, M., Kolozali, S., Farajidavar, N., Gao, F., Iggena, T., Pham, T-L., Nechifor, C-S., Puschmann, D. and Fernandes, J., (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
Conferences (15)
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., Phan, H., Tran, M., Jarchi, D. and Kolozali, S., (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 (1)
Farajidavar, N., Kolozali, S. and Barnaghi, P., (2017). A Deep Multi-View Learning Framework for City Event Extraction from Twitter Data Streams
Grants and funding
2021
Above Surveying contract research application
Above Surveying Ltd
Above Surveying Ltd KTP3 Application
Innovate UK (formerly Technology Strategy Board)
2020
Stephenson Harwood KTP Application
Innovate UK (formerly Technology Strategy Board)
Stephenson Harwood KTP Application
Stephenson Harwood
2019
AI-Assisted Decision-Making System for Cancer Pathways of the Colchester Hospital
East Suffolk and North Essex NHS Foundation Trust
A Remote Monitoring System for the Early Detection of Gestational Diabetes
University of Essex (GCRF)
Contact
Academic support hours:
Tuesday between 10:30AM (zoom: 91390146531)
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