I am on my new role in School of Computer Science, UTS, Sydney. You can reach me via this address:
Dr Morteza Saberi is currently a Lecturer (Assistant Professor) at the School of Business, UNSW Canberra. He has an outstanding research record and significant capabilities in the area of business intelligence, data mining and applied machine learning. He has published more than 150 papers in reputable academic journals and conference proceedings such as Computers & Industrial Engineering, Future Generation Computer Systems, Knowledge-Based Systems, Applied Soft Computing, Enterprise Information Systems, Business Process Management Journal, Journal of Manufacturing Systems, Journal of Loss Prevention in the Process Industries, Technological Forecasting and Social Change, Process Safety and Environmental Protection, Neurocomputing, Safety science, Quality & Quantity, Energy Policy, Applied Energy, AAA-I, WSDM & IJCAI. His Google Scholar citations and h-index are 3300 and 27, respectively. He was a Lecturer at the University of Tafresh’s Department of Industrial Engineering. He has won over 20 national and international research awards and recognitions till date from his research. He was awarded the first Place Dissertation Competition from Sixth International Conference on Industrial Engineering and Operations Management (IEOM) as an output of his PhD. He visited two world-class research institutes in Germany: the Department of Information Systems (under Professor Martin Theobald), Max Planck Institute, Saarbrucken, Germany and Information Systems Group (under Professor Felix Naumann), Hasso Plattner Institute, Germany. Currently, he is the co-supervisor of 4 PhD students.
Data is everywhere, generated by everything around us on a daily basis at incredible speeds and touching every aspect of our life. This has led to the emergence of a new discipline, Data Science, which involves finding new patterns within the huge amount of data. My research focus is to develop new data science models that not only find new patterns but also make it understandable for individuals to help their informed decision making.
In my research projects, I have verified the suitability of new data science models in Australian industries ranging from agriculture to mining, pharmaceutical and retail. For example, we developed a low-cost network of sensors, coupled with data analytic techniques in order to predict the abnormal behaviour of milk temperature that assists the milk farmer in taking proactive steps to maximise the milk’s quality. This has enabled them to grow their business, produce better quality milk, and get a better price in return. In the retail industry, we developed a new model that decides how to allocate stock to customers based on their preferences, and values when stock is limited. This strategy is beneficial for customers as their preferences are taken into consideration in the modelling.