Personal details

Name
Associate Professor Richi Nayak
Position(s)
Associate Professor
Science and Engineering Faculty,
Electrical Engineering, Computer Science,
Data Science
Discipline *
Artificial Intelligence and Image Processing, Computation Theory and Mathematics, Library and Information Studies
Phone
+61 7 3138 1976
Email
Location
View location details (QUT staff and student access only)
Identifiers and profiles
ORCID iD
Qualifications

PhD (Queensland University of Technology)

Professional memberships
and associations

She is steering committee member of the Australasian Data Mining community. She was appointed the 2017 IT ambassador of the Queensland Women in Technology organization.

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

* Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2008

Biography

Research theme: Information Research discipline: Data Science She 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. 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 highly cited in her research field. She 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.

This information has been contributed by Associate Professor Richi Nayak.

Teaching

Teaching discipline: Data Science.

I am engaged in teaching Data Science related subjects – Data Mining and Applications; and Data and Web Analytics.

This information has been contributed by Associate Professor Richi Nayak.

Experience

Her research expertise spans multiple domains. She is actively engaged in and leading transdisciplinary research. Some of the past projects that she led are listed below with their impact on ICT industries, government and communities.

Applications of data mining 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 Road environmental and accidents data set; Anaesthetic time series data set; Active ageing survey dataset and Structural health monitoring. o Her research conducted with Queensland Dept of Transport and Main Roads (QDTMR) has developed the followings:

  • Risk-based decision support modelling for managing skid-resistance
  • Decision Support modelling based on data mining for skid resistance
  • Pavement Deterioration models with Data Mining

These projects have contributed new knowledge to Main Road domain and have influenced decision making processes at QDTMR. o Her research conducted with Queensland Dept of Public Works (QDPWD) resulted  into  a  computer  software  package  that  interactively  supports building designers, owners and maintainers by predicting service life outcomes for a range of metal building components, in different locations and susceptible to corrosions from a variety of sources. o Her research conducted with Anaesthetists at Royal Brisbane Hospital  (RBH) resulted  into  identifying  patterns  for  monitoring  and  assessing  patient’s health   during   surgery,   and   helping   anaesthetists   monitor   anaesthetic medicine admission with the aim to control cardiac arrest during surgery.

Personalisation  of  Web  Services  using  Link  and  Network  Mining  and  Product Recommendation – this research focused on the development of techniques and tools to better identify customer behaviour and need, and to apply these tools and techniques directly to improve products and services. It developed novel data mining techniques for user profiling and market segmentation, and also develops recommendations and information filtering techniques for improving the relevance of content in services such as personalised search and matching. o She has developed a number of innovative recommendation algorithms that have been trialed by a leading Australian Dating Network and Car seller website. She has developed a number of innovative algorithms that take the structural and semantic features embedded in data into consideration to identify patterns in the data and utilize them in decision-making.

Clustering algorithms: She has significantly contributed to the field of big data by focusing on the data problem of variety in which data appears in multiple formats, and the data analytics algorithms should take advantage of this added information.

  • She has proposed several robust, accurate and efficient multi-view clustering algorithms that can combine several types of features simultaneously in the clustering process.
  • Recently, she has proposed the novel concept of ranking and indexing infrastructure used in information retrieval in designing the clustering methods. In the MediaEval forum, one of these methods outperformed the other participants in the task of event detection. These algorithms are fast and efficient.
  • One of the techniques that she developed introduced a novel level structure; using this structure, she proposed an incremental clustering algorithm that could group a very large collection within a very short time. This work was ranked in the top 5 papers of the leading international conference – The 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006).

Tensor Space Modelling and Analysis: She has very successfully developed a number of methods based on Tensor Space (multi-dimensional) models.

  • Proposed a number of clustering methods that innovatively use Tensor Space model to include the structural and content features effectively and efficiently to find subgroups.
  • Proposed a number of learning-to-rank algorithms that are able to provide high-quality recommendations in social media systems.

She has successfully developed data mining solutions to real-world applications. In doing so, she proposed a number of customized data mining algorithms. She has developed methods for a number of application domains such as social networks, social media, information retrieval, Web services, e-commerce, and m-commerce. Some of them are:

  • A constrained clustering algorithm to deal with the nature and scale of the social networks’ users, utilising both the implicit and explicit information on these networks.
  • A bi-partite graph mining technique and a co-clustering method that are suitable for grouping the collections that involve two different types of entities or objects. This method takes advantage of both types of information and uses them efficiently in clustering.
This information has been contributed by Associate Professor Richi Nayak.

Publications

For publications by this staff member, visit QUT ePrints, the University's research repository.

Awards

Awards and recognition

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.
Type
Advisor/Consultant for Community
Reference year
2017
Details
She is a board member of the Ascot Primary State School and advises the school on their data management, analytics, and digitalization practices.
Type
Committee Role/Editor or Chair of an Academic Conference
Reference year
2018
Details
Program Committee Member of prestigious conferences. Some of them are KDD 2018, 2017, 2016; CIKM 2018; WSDM 2017; PAKDD18¿07, AusDM12-08, ICDM14, ACM SAC 2014-18, EDBT11, AI18-09 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
Editorial Role for an Academic Journal
Reference year
2017
Details
Chief Editor of International Journal of KNowledge and Web Intelligence (IJKWI). Editorial advisory reviewer board member of the International Journal of Knowledge-Based & Intelligent Engineering Systems (KES), International Journal of Cases on Electronic Commerce (IJCEC), and Information Resource Management Journal (IRMA).

Research projects

Grants and projects (Category 1: Australian Competitive Grants only)

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

Supervision

Current supervisions

  • Documents Clustering and Interpretation for Better Understanding
    PhD, Principal Supervisor
    Other supervisors: Associate Professor Shlomo Geva
  • Knowledge Discovery from Social Media Networks using Frequent Pattern Mining
    PhD, Principal Supervisor
    Other supervisors: Associate Professor Shlomo Geva
  • Clustering Methods on Multi-type Relational Data
    PhD, Principal Supervisor
    Other supervisors: Associate Professor Shlomo Geva
  • A Context-aware Spatial-temporal Anomaly Detection Framework for Data Streams
    PhD, Principal Supervisor
    Other supervisors: Associate Professor Shlomo Geva
  • Tensor based Multiple-Context Aware Recommendation Systems
    PhD, Principal Supervisor
    Other supervisors: Professor Glen Tian
  • A User Profile Approach to Review Spam Detection
    PhD, Principal Supervisor
    Other supervisors: Associate Professor Shlomo Geva
  • Controlling data mining driven risk profiles and applying them as triggers in service delivery in complex data environments.
    PhD, Principal Supervisor
    Other supervisors: Professor Alistair Barros
  • Research on big data for public electricity utility's smart meterings
    Professional Doctorate, Principal Supervisor
    Other supervisors: Dr Maolin Tang
  • New Approaches to Incompleteness Problems in Large-Scale Knowledge Graphs
    PhD, Associate Supervisor
    Other supervisors: Dr Maolin Tang
  • The Evolution of Digital Innovation Concepts in Online Social Networks
    PhD, Associate Supervisor
    Other supervisors: Dr Erwin Fielt, Professor Marek Kowalkiewicz

Completed supervisions (Doctorate)