- Dr Renuka Sindhgatta Rajan
- Lecturer Service Science
Science and Engineering Faculty,
- Discipline *
- Information Systems
- +61 7 3138 1725
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- Identifiers and profiles
PhD (University of Wollongong)
I started my academic career at QUT, School of Information Systems, after working in the Industry research labs for over 15 years. Before joining QUT, I was at IBM Research as a Senior Technical Staff Member and leading research in areas of empirical software engineering, services research, and AI solutions for education technology that included conversational agents or chatbots.
My research interest lies at the intersection of business process management for service delivery systems. I currently focus on analytics for evaluating outcomes in service-based applications. These include key characteristics of services, namely risk, performance, and resourcing. The focus will be on infusing trust through the interpretability of models, checking for bias, and considering the data lineage. Application areas include digital agents and process-aware information systems.
I have co-authored cover 40 papers in top peer-reviewed conferences such as OOPSLA, ASE, KDD, CIKM, ICSOC, BPM and CAiSE. I have filed over 15 patent applications and have 4 granted US patents.
Interpretable analytics for services delivery:
Analytics in service delivery systems uses machine learning or deep learning algorithms to predict and automate decision-making tasks. However, to help users trust the recommendations and make choices, it is essential to design interpretable models. This work explores the use of existing black-box models in service-based applications and the building of domain-specific interpretable models.
I am interested in students who would like to contribute in the area predictive analytics for services delivery.
I have over two decades of industry experience. I have worked in the area of software analytics by mining software development data and predicting defects or bugs.
I have analyzed the operational data of service teams and built a capacity planning solution that enabled the efficient allocation of work in a shared services delivery model. I was involved in developing simulation models for optimal staffing. Team staffing was validated based on data captured from over 500 teams across the organization. The implementation of these projects led to a cost saving of over 10 MUSD to the service delivery teams.