Data Engineering promotes an engineering approach to analyze big “imperfect” data by creating and using new algorithms such as Unsupervised Deep Learning, appropriate electronic hardware platforms and mechanical/mechatronic/chemical-based approaches to interrogate and acquire missing data or information. My research engages a comprehensive data engineering approach from sensors for data collection to data analytics and optimization. This research has significant applications in Energy, Health, Agriculture and Business sectors and IoT applications.
 K. Abdulla, K. Steer, A. Wirth, and S.K. Halgamuge, “Improving the On-line Control of Energy Storage via Forecast Error Metric Customization”, Journal of Energy Storage, accepted in Sep 2016, Elsevier.
 K. Abdulla, J. d. Hoog, V. Muenzel, F. Suits, K. Steer, A. Wirth, and S. K. Halgamuge, “Optimal Operation of Energy Storage Systems Considering Forecasts and Battery Degradation", IEEE Transactions on Smart Grids, accepted in September 2016.
 A. Habibi, T. Scherer and S.K. Halgamuge, “Energy, Environmental and Economical Saving Potential of Data Centers with Various Economizers across Australia”, Applied Energy, accepted in 2016, Elsevier.