Multi-aspect Learning

Richi Nayak is a Professor at the School of Computer Science and Leader of the Complex Data Analysis Program at the Centre of Data Science at Queensland University of Technology, Brisbane Australia. She is an internationally recognized expert in machine learning, data mining and text mining. She has made multiple advances in social media mining, deep neural networks, multi-view learning, matrix/tensor factorization, clustering and recommender systems. She has a driving passion to address pressing societal problems by innovating the Artificial Intelligence field underpinned by fundamental research in machine learning. She consults several private, public and government agencies in machine learning projects and many of her research projects have been commercialised. Her research has resulted in the development of novel solutions to address industry-specific problems in Digital Marketing, K-12 Education, Digital Agriculture and Digital Humanities. She has authored over 250 high-quality refereed publications that have attracted over 4300 citations and an h-index of 33.  Her research leadership is recognised by multiple best paper awards and nominations at international conferences, QUT Postgraduate Research Supervision awards, and the 2016 Women in Technology (WiT) Infotech Outstanding Achievement Award in Australia. She is a Steering committee member of the Australasian Data Mining and Machine Learning conference. She is the editorial chief of the International Journal of Data Mining and Digital Humanities. She holds a PhD in Computer Science from the Queensland University of Technology and a Masters in Engineering from the Indian Institute of Technology Roorkee, India.

Khanh Luong obtained her PhD in Computer Science specializing in Data Science from Queensland University of Technology (QUT) in 2019. Afterwards, she worked as a Postdoctoral Researcher in Data Science at the QUT Centre for Data Science, where her research focused on addressing the challenges of dealing with multiple aspect data. Her research has made significant contributions to the fields of machine learning and data mining by developing innovative methods ready to be deployed on real-world datasets, ranging from text, image, sound, video, and bioinformatics data. Her methods apply to diverse problems, such as clustering, classification, anomaly detection, community discovery, and collaborative filtering, with a novel multi-aspect outlook. She has an impressive track record as an active member of the Organizing Committee of the Australasian Data Mining Conference for several years. Additionally, she has established herself as a highly regarded reviewer for several top-tier journals, including IEEE Transactions on Knowledge and Data Engineering (TKDE), IEEE Transactions on Knowledge Discovery from Data (TKDD), IEEE Transactions on Neural Networks and Learning Systems (T-NNLS), IEEE Transactions on Audio, Speech and Language Processing (TASLP), and Information Sciences. Recently joining Charles Sturt University as a research fellow, she is currently working on Cyber Security projects and collaborating with Data61 to develop practical approaches for detecting and reacting to attacks using various data sources.

1 Multi-Aspect Data Learning: Overview, Challenges and Approaches.-  2 Non-negative Matrix Factorization-Based Multi-Aspect Data Clustering.- 3 NMF and Manifold Learning for Multi-Aspect Data.- 4 Subspace Learning for Multi-Aspect Data.-  5 Spectral Clustering on Multi-Aspect Data.- 6 Learning Consensus and Complementary Information for Multi-Aspect Data Clustering.- 7 Deep Learning-Based Methods for Multi-Aspect Data Clustering.