Research Projects


  • RSSI Based Navigation in Indoor Positioning for Critical Applications, University of Dubai (budget 300,000 DHS): Innovative Location Based Critical Applications (LBCAs) are critical applications that are providing services in various categories including tracking people, navigation to emergency exits, locations of healthcare aid equipment’s etc. By using the already deployed University of Dubai IT infrastructure along with the university occupants' smartphones, the goal of the proposed project is to determine and track in real time the location of smartphone users inside the university buildings. Duration of the project: November 2022-October 2023.
  • Analysing digital advice journeys: In partnership with Ignition Advice, the aim of the project is to investigate the design of a range of digital services that help people at all stages managing and growing their wealth at the fraction of the cost of traditional Financial Services. Designing such new services will be based on analysing historical advice data using machine learning methods. Duration of the project: May 2021 to December 2024.


  • Investigating the state-of-the-art of applying machine learning to electronic trading: In partnership with the University of Manchester and with the support of Plato Consulting, the aim of the project is to conduct a survey of the state-of-the-art regarding the application of machine learning techniques in the area of electronic trading. Duration of the project: February 2020 to January 2021.
  • Categorising and enriching data collected from merchant electronic receipts: In partnership with Cognitivo Consulting and with the support of a TechVoucher grant, this project investigates methods to categorise and enrich data collected from merchant electronic receipts. This will include natural language interpretation of SKU (stock-keeping-unit) level data, matching and mastering SKU’s and categorizing items based on GS1 codes. Duration of the project: February 2019 to December 2019.
  • Creating a database of intraday financial market measures: to assist researchers in the School of Finance at UNSW by creating a database containing financial market measures related to Australian equity markets. This will make it easier for researchers to translate research to the Australian setting, and engage with industry in Australia. Duration of the project: February 2019 to January 2021.
  • Credit Risk Prediction: In partnership with Cognitivo Consulting , the purpose of the project is to investigate intelligent credit risk management techniques that leverage advanced data capture and modelling techniques as well as adaptive credit risk models that make use of the latest machine learning and AI techniques. Duration of the project: March 2017 to December 2018.
  • Performance of Islamic Funds, B. Saadouni (The University of Manchester), F. Rabhi (University of New South Wales), A. Rao and W. Mansoor (University of Dubai) and A. Hoque (University of Murdoch). The project aims to examine whether Islamic funds revise their portfolios around major Islamic Indices (e.g. Dow Jones World Market Islamic Index) revisions. The project also aims to test if Islamic funds portfolio revisions are driven by the sentiment (could be positive or negative) of the companies that are added (deleted) from the major Islamic Indices.
  • Information Services for Smart Cities : Our research group is partnering with UTS and the University of Wollongong in an initiative funded by Landcom. Our role is to look at designing services for accessing and processing a number of data sources (including Open data) and developing a variety of Apps to facilitate analysis property-related data. Period: 1 January 2018 - 30 June 2019.
  • Software Platform for Automatic Financial Advice, F.A. Rabhi and Ali Behnaz: Designing software services for a range of activities such as risk profiling, asset selection and portfolio construction. Includes the design of novel user interfaces for providing financial advice in an innovative way and big data analysis. Period: 1 January to 31 December 2017. Funding: Ignition Wealth Pty Ltd .
  • Enhancing Highlighter to Support External Data Interactions; F.A. Rabhi and L. Yao : This projects aims at using the Highlighter tool to derive simple Web interfaces from Excel spreadsheets. The case study is a simple spreadsheet that gives an investment manager control over asset selection and allocation. Period: 1 August to 31 December 2015. Funding: YTML Consulting Pty Ltd.
  • Instantaneous Exchange of Information (IEOI) between tax juridictions. E. Mercuri: historically, these exchanges have been restricted by manual transfers or by depth of information provided. IEOI promises the capability to transfer information globally and efficiently and is designed to address the needs of the OECD Base Erosion and Profit Shifting (BEPS) initiative,Period: April 2011 to December 2018.
  • Evaluation of News Sentiment Datasets, F.A. Rabhi and I. Al Qudah: Designing a methodology and a platform for evaluating the impact of sentiment scores on a stock market. This project uses an event study methodology supported by ADAGE services to evaluate the impact of sentiment score goals using Thomson Reuters market data. Period: 1 July 2014 tol 31 December 2017.
  • MBS Big Data Forum; N. Mehandjiev et al.: this is an initiative to promote big data research in several business areas. Our role is focusing on "Fraud and Manipulation Detection in Finance" area. Period: 15 September to 31 December 2015. Funding: Manchester Business School
  • Topic-based Weighted Sentiment Analysis; Asso Hamzehei and Raymond Wong: This project aims to measure a topic’s sentiment on social network by discriminating texts and their authors through social influence analysis. Period: 1 Sep 2014 to 31 Aug 2017.
  • Tabular Data Extraction and Understanding, Roya Rastan and Helen Paik: Design and Implement a system to detect, extract and interpret tabular data in corporate announcements. This project provides a reusable and repeatable solution to process vast amount of unstructured documents without relying on any specific external knowledge base. Period: 1 October 2012 to 1 March 2016

Collaboration between team members is enabled via a number of Shared Resources