Assoc Prof Richi Nayak

    Associate Professor, Science and Engineering Faculty

    Richi is head of the Data Science Discipline in EECS. She is an internationally recognised expert in data mining, text mining and web intelligence. She has combined knowledge in these areas very successfully with diverse disciplines such as Social Science, Science, and Engineering for technology transfer to real-world problems to change their practices and methodologies. Richi’s particular research interests are machine learning and in recent years she has concentrated her work on text mining, personalization, automation, and social network analysis. She has published high-quality conference and journal articles and highly cited in her research field. Richi has received a number of awards and nominations for teaching, research and service activities.

    Research areas Text Mining for data organization and understanding With the advancements in computing resources and digitalization, an increasing amount of data is generated in text format. In this research stream, my research group is engaged in developing sophisticated and novel text clustering methods based on the concepts of ranking-centered, hubs, density-based and matrix/tensor factorization. These innovative methods are suitable for big data to provide accurate and scalable solutions. We have applied these methods to several applications such as social media mining, community discovery, information harvesting, robot navigation, trend detection, concept mining, abuse detection, spam review detection, recommendation, and personalization.

    Applications of data mining and machine learning into solving real-world problems Developing real-time data mining systems by utilising the hidden patterns and rules behind the complex sets of data sets such as School education; Road environmental and accidents data set; Anaesthetic time series data set; Active aging survey dataset and Structural health monitoring. In this research stream, my research group is engaged in developing machine learning and data mining algorithms and systems that can be deployed in practice by various industries and used in data-driven intelligent decision making. In an industry-funded project, we are using these techniques to extract useful information and build an information repository so the client can quiz the related information easily.

    Algorithms for Automation, Personalisation and Pattern Mining With the Internet of Things and Digital Twinning, algorithms are required to mine patterns and trends from the complex data and develop applications based on these patterns and trends. In this project stream, we have developed algorithms to understand the spatiotemporal context for anomaly detection and location-based navaigation recommendations. We have also developed Deepnet algorithms to automatically generate marketing reports based on past reports and extensive online information scrapped from the related data sources.