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Explainable AI-enabled predictive analytics for decision making support

Modern predictive analytics, underpinned by AI-enabled learning (such as machine learning, deep learning) techniques, has become a key enabler to the automation of data-driven decision making.In the context of business process management, predictive analytics makes predictions about the future state of a running business process instance. These predictions can include:which task will be carried out nextwhen the task be carried outwho will perform the taskwhen an ongoing process instance will be completewhat the outcome will be upon completion.Machine learning models …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Information Systems
Research centre(s)
Centre for Data Science

Guided process analytics for actionable improvement recommendations

Process analytics, often known as process mining, mainly involves data-driven analysis of business processes using the large amount of event log data captured by information systems in organisations.Various analytical techniques help extract insights about the actual business processes with the ultimate goal of process improvement.Within a broad domain of data analytics, process analytics is faced with a main challenge of information overloading. In the ‘big data’ era we often encounter with the problem that there is too much data to …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Information Systems
Research centre(s)
Centre for Data Science

Human-in-the-loop techniques to debug machine learning models

Machine learning models are being deployed in critical domains such as healthcare, education and fintech. The current approach to deploying machine learning models is based on considering a data-centric approach where the models are evaluated using performance measures on a test set. However, the high performance of the model on test data is not indicative of its reliability,An important aspect of reliability is in the understanding of what exactly a machine learning model encodes, and to verify if it learns …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Information Systems
Research centre(s)

Privacy-preserving process analytics

Modern organisations consider data to be their lifeblood. While the importance of data science and the potential benefits of data analytics are widely acknowledged, many people have grave concerns about irresponsible use of their data.Negative publicity surrounding large collections of personal data (including location, videos, pictures and emails) by corporations such as Google and Facebook and subsequent breaches have resulted in people losing trust in, and becoming more suspicious about, how their personal data is being used.Process mining is a …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Information Systems
Research centre(s)
Centre for Data Science

Responsible analytics of process data

Technological advances in the field of data science empowers organisations to become data driven by applying new techniques to analyse large amounts of data. The potential benefits include a better understanding of business performance and more informed decision making for business growth. However, a key roadblock to this vision is the lack of transparency surrounding the quality of data.Process mining is a specialised form of data-driven process analytics where process data, collated from the different IT systems typically available in …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Information Systems
Research centre(s)
Centre for Data Science

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