Professor Richi Nayak
Faculty of Science,
School of Computer Science
Biography
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. Her 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 is highly cited in her research field. She has received a number of awards and nominations for teaching, research and service activities. She is the Applied Data Science Program Leader of the University Centre for Data Science (CDS).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 classification, TExt Clustering and Information Extraction methods based on the concepts of deep learning, ranking-centered, hubs, density-based and matrix/tensor factorization. These innovative methods are suitable for big and complex data to provide accurate and scalable solutions. We have applied these methods to several applications such as robotic marketer, 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; Energy Bills; Intestine Bowel Disease; 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 navigation recommendations. We have also developed deep learning algorithms to automatically generate marketing reports based on past reports and extensive online information scrapped from the related data sources.
Personal details
Positions
- Professor
Faculty of Science,
School of Computer Science
Keywords
Artificial intelligence, Data mining & knowledge discovery in databases, Machine learning, Applications of data mining, Web mining and Web Intelligence, Social Network Mining, Machine Learning for Information Retrieval
Research field
Artificial intelligence, Library and information studies, Theory of computation
Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2020
Qualifications
- PhD (Queensland University of Technology)
Professional memberships and associations
She is a Steering Committee member of the Australasian Data Mining community since 2012. She was appointed the 2017 IT ambassador of the Queensland Women in Technology (WiT) association.
Teaching
Teaching discipline: Data Science.
I am engaged in teaching Data Science related subjects.
IFN509: Data Exploration and Mining
IFN645: Data Mining Techniques and Applications
CAB330: Data and Web Analytics
IFN701 & IFN702 - Project Students Supervisor
Experience
She has an excellent track record of working with industries to address their data science-related problems using machine learning. Her research expertise spans multiple domains. She is actively engaged in and leading transdisciplinary research. Examples of end-user adoption of her research are given below.
Using her data and text mining expertise, she developed (i) a “world-first data-driven marketing strategy automation technology” that was commercialised by Marketing Eye, a top 10 marketing strategy consultancy in the USA and Australia, and (ii) a deep learning-based bias detection approach that was commercialised by iShield.ai to assist Fortune 500 companies in content management and as a slack Dost app for detecting bias in the text as users post it on social media.
Through her CRC Smart Services project, She developed several web personalisation methods for a product recommendation, as well as a people-to-people recommendation system, for online social networks. One of these methods was successfully trialled with the online social network RSVP, hosted by Fairfax Digital, one of the project’s industry partners, to identify the most suitable life partners.
Her research conducted as part of the Cotton RDC project resulted in a sustainability repository system that automatically integrated data from multiple online sources. This system provided a new capability for sustainability managers and farmers to extract insights on cotton production and agriculture farm data. The industry labelled Nayak’s research as a “game changer” and “ahead of [its] time”. In a similar project with the Queensland Department of Natural Resources, Mines and Energy, Nayak’s research resulted in a queriable database based on transforming past reports (including texts, PDFs, images and tables) to the well-card data. This enabled end-users to know more about past mining operations in Queensland intelligently and intuitively.
Through her Rural Industry RDC and AgriFuture research projects, her team developed a novel horizon scan method to analyse tweets, open Facebook pages, new publications, and patent data to identify emerging trends in agriculture. Six horizon reports detailing these trends and topics, combined with infographics, were delivered to AgriFutures, a statutory authority of the federal government. AgriFutures noted that our unique data mining approach placed them at the forefront of thought leadership in AgriTech.
The financial indicators proposed in her CRC Food Agility project were used by the Australian Wine Research Institute (AWRI) to collect operational and economic data from vineyards. These outcomes opened up the possibility of including environmental benchmarks in financial modelling.
In partnership with the Queensland Department of Transport and Main Roads (QDTMR), Nayak led a research team to develop risk-based decision support models to manage skid-resistance and pavement deterioration using data mining. The outcomes were used to refine the departments’ skid-resistance investigation and data collection policies. This decision support system is currently used to identify roads that are risk-prone and in need of maintenance.
Research conducted with the Queensland Department of Public Works resulted in a computer software package that predicts service life outcomes for a range of metal building components in different locations which are susceptible to corrosion. This software is a valuable tool for building designers, owners and maintainers.
Publications
- Agarwal, B., Nayak, R., Mittal, N. & Patnaik, S. (2020). Deep Learning-Based Approaches for Sentiment Analysis. Springer. https://eprints.qut.edu.au/198259
- Balasubramaniam, T., Nayak, R., Yuen, C. & Tian, Y. (2021). Column-wise element selection for computationally efficient nonnegative coupled matrix tensor factorization. IEEE Transactions on Knowledge and Data Engineering, 33(9), 3173–3186. https://eprints.qut.edu.au/197434
- Tennakoon Mudiyanselage, G. & Nayak, R. (2019). FCMiner: mining functional communities in social networks. Social Network Analysis and Mining, 9(1), 1–19. https://eprints.qut.edu.au/129061
- Noor Ifada, N. & Nayak, R. (2016). How relevant is the irrelevant data: Leveraging the tagging data for a learning-to-rank model. Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, 23–32. https://eprints.qut.edu.au/97531
- Kutty, S., Nayak, R. & Chen, L. (2014). A people-to-people matching system using graph mining techniques. World Wide Web, 17(3), 311–349. https://eprints.qut.edu.au/63801
- Emerson, D., Weligamage, J. & Nayak, R. (2013). A data mining driven risk profiling method for road asset management. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1267–1275. https://eprints.qut.edu.au/62100
- Nayak, R., Senellart, P., Suchanek, F. & Varde, A. (2012). Discovering interesting information with advances in web technology. ACM SIGKDD Explorations, 14(2), 63–81. https://eprints.qut.edu.au/62117
- Nayak, R., (2009). Generating rules with predicates, terms and variables from the pruned neural networks. Neural Networks, 22(4), 405–414. https://eprints.qut.edu.au/30070
- Nayak, R., (2008). Fast and effective clustering of XML data using structural information. Knowledge and Information Systems, 14(2), 197–215. https://eprints.qut.edu.au/224842
QUT ePrints
For more publications by Richi, explore their research in QUT ePrints (our digital repository).
Filter publications:
A complete list of publications is available at: https://www.qut.edu.au/about/our-people/academic-profiles/r.nayak
Awards
- Type
- Editorial Role for an Academic Journal
- Reference year
- 2020
- Details
- The Journal of Data Mining & Digital Humanities is concerned with the intersection of computing and the disciplines of the humanities, with tools provided by computing such as data visualization, information retrieval, statistics, text mining by publishing scholarly work beyond the traditional humanities.
- Type
- Committee Role/Editor or Chair of an Academic Conference
- Reference year
- 2018
- Details
- Steering Committee Member of the Australasian Data Mining Conference - the only leading association in this region - since 2012.General Co-chair of the International Conference on Data Science, Intelligent Computing and Cyber Security (ICDIC 2020) and the Australasian Conference on Data Mining (AusDM 2020, 2015, 2014).Program Committee Member of prestigious conferences, e.g., KDD 2016-19, CIKM 2018-19, WSDM 2017; PAKDD 07-19, AusDM12-08, ICDM14, ACM SAC 2014-20, EDBT11, AI20-09 and many others. She was program chair of the 2014 AusDM (Australian conference on Data Mining) to be held in Brisbane, December 2014. She was publicity chair of the 2013 ACM SIGIR to be held in GodCoast, July 2014. She was track chair of Database and Data Mining in the 3rd International Conference on Computer Science and its Applications 2011 (CSA-11). She was local chair of the 2012 joint WCCI World Conference on Computational Intelligence. She was local chair of the 2013 PAKDD 13.
- Type
- Advisor/Consultant for Community
- Reference year
- 2018
- Details
- 2018-2021: Board of Studies and Advisory Committee: International expert member, SNS College of Technology, Anna University, Chennai, India.2016-2018: Board member of the Ascot Primary State School Council. Advised the school on their data management, analytics, and digitalization practices.
- Type
- Editorial Role for an Academic Journal
- Reference year
- 2017
- Details
- Founder and Editor-in-Chief of the International Journal of Knowledge and Web Intelligence (2008-10) and now serve on the Editorial Board (2011-). Associate Editor of International Journal of Knowledge-Based and Intelligent Engineering Systems (2010-14) and International Journal of Data Mining, Modelling and Management (2015-18).Editorial advisory reviewer board member of the International Journal of Knowledge-Based & Intelligent Engineering Systems (KES).
- Type
- Academic Honours, Prestigious Awards or Prizes
- Reference year
- 2016
- Details
- Dr Nayak was the recipient of the 2016 WiT Infotech Outstanding Achievement Award for her exemplary services to the field of Data Analytics in the Queensland state and outside. Dr Nayak was the ICT ambassador appointed by the WiT on 2017.
Selected research projects
- Title
- Profit and Loss: The Commercial Trade in Indigenous Human Remains
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- DP200101814
- Start year
- 2020
- Keywords
- Title
- Improving the ability of the Australian cotton industry to report its sustainability performance
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- QUT1705
- Start year
- 2016
- Keywords
- Agriculture; Agroecosystem Health; Natural Resource Management
- Title
- Human Cues for Robot Navigation
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- DP140103216
- Start year
- 2014
- Keywords
- Autonomous Robots; Mapping and Navigation; Spatial Cognition
- Title
- The Neglected Dimension Of Community Liveability: Impact On Social Connectedness And Active Ageing
- Primary fund type
- CAT 1 - Australian Competitive Grant
- Project ID
- LP0883447
- Start year
- 2009
- Keywords
- Community Liveability; Social Engagement; Community Well Being; Social Isolation; Population Ageing
Projects listed above are funded by Australian Competitive Grants. Projects funded from other sources are not listed due to confidentiality agreements.
Supervision
Current supervisions
- Multi-View Data Learning with Deep Matrix Factorization
PhD, Principal Supervisor
Other supervisors: Associate Professor Yue Xu
Completed supervisions (Doctorate)
- Knowledge Discovery from Social Networks Using Interaction Frequency and User Hierarchy (2020)
- Matrix/Tensor Factorization with Selective Coordinate Descent: Algorithms and Output Usage (2020)
- Unsupervised Text Mining: Effective Similarity Calculation with Ranking and Matrix Factorization (2020)
- Clustering Methods For Multi-Aspect Data (2019)
- A Personalised Ontology Framework for Interpreting Discovered Knowledge in Text Information (2017)
- Scalable Fine-Grained Document Clustering via Ranking (2017)
- Personalized Ranking for Tag-based Item Recommendation System Using Tensor Model (2016)
- Anomaly Detection in Online Social Networks: Using Data-Mining Techniques and Fuzzy Logic (2014)
- Text Mining with Semantic Annotation: Using Enriched Text Representation for Entity-Oriented Retrieval, Semantic Relation Identification and Text Clustering (2014)
- Recommending People in Social Networks Using Data Mining (2013)
Supervision topics
The supervisions listed above are only a selection.