Research


My research interests are in Data Mining, Machine Learning and Neural Networks, and their applications for Pattern Recognition, text, image and video processing.  In particular, I am interested in algorithms for classification and prediction (supervised learning) and clustering (unsupervised learning) and their applications.

  • In a classification task, given a set of examples with their correct class (label), the goal is to build a classifier that can be used to predict the class of new, unseen, examples. Some of the classification tasks I have worked on are: classifcation of sleep stages in babies from EEG, EMG and other biological signals, decoding motor commands from EEG brain activity (brain-computer interfaces), recommending movies using collaborative filtering and content-based approaches, recognising fingerprints, filtering spam from non-spam e-mail. Prediction tasks are similar to classification tasks but the value to be predicted is numeric. I have worked on electricity load prediction from previous historical data and predicting sucessful interactions in online dating.
  • In clustering, given a set of unlabelled examples, the goal is to group data into several clusters according to their similarity. I have worked on clustering for educational data mining (extracting patterns distinguishing the stronger and weaker groups in software development projects from online interaction traces); keyframe extraction in video, video summarization and non-linear access to the relevant material and finding similar genes in microarray data.

I am a member of the Human-Centered Technology research clusterCHAI, Schwa Lab and the Clean Energy research cluster.


See also  publications, students and reviewing

Research Interests


  • Data Mining, Machine Learning and Neural Networks, in particular methods for classification and prediction. This includes:
    • Data mining of biomedical and health data (e.g. EEG, sleep, breathing, cytometry and diet data, and medical records)
    • Data mining of energy time series data (e.g. electricity load prediction, solar and wind power forecasting)
    • Data mining of educational data (e.g. mining student logs, assessment data and student evaluation of teaching)
    • Text mining and sentiment analysis
    • Recommender systems, in particular people to people recommenders (e.g. matching people in online dating systems) and recommender systems in education

Current and recent projects


  • Educational data mining (with Kalina Yacef, Joshua Stretton, Ling Luo and Judy Kay)
  • Solar power production forecasting (with Tony Wang, Mashud Rana, Alicia Troncoso and Vassilios Agelidis)
  • Electricity load forecasting (with Mashud Rana, Alicia Troncoso, Francisco Martinez-Alvarez, Vassilios Agelidis, Alexandra Kotillova, Tommaso Colombo and Massimo Panella)
  • Classification of breathing patterns (with Cecilia Li, Cindy Thamrin, Chinh Nguyen and Mark Read)
  • Revealing the development of the immune response through dynamic clustering (with Deeksha Singh, Mark Read, Uwe Roehm, Nick King and Thomas Ashhurst)
  • Classification of sleep data (with Stephen Mccloskey, Bryn Jeffries and Rim Haidar)
  • Predicting emergency events from health data (with Kathryn Rendell, Uwe Roehm, Aaron Schindeler, Craig Munns and Andre Kyme)
  • Predicting hospital admissions (with Tianyu Pu and Michael Dinh)
  • Temporal modelling of user behaviour (with Ling Luo, Bin Li, Shlomo Berkovsky and Fang Chen)
  • Recommender systems for online dating (with Joshua Akehurst, Luiz Pizzato, Kalina Yacef, Judy Kay and Tomasz Rej) [ chai intranet ]
  • Sentiment analysis and quote attribution (with Tim O'Keefe and James Curran)

Research grants


  • SREI 2020 grant (DVC Research/Sydney Research Excellence Initiative): Understanding and facilitating learning in emerging knowledge co-creation spaces, Lina Markauskaite, Peter Reimann, Peter Goodyear, Abelardo Pardo, Judy Kay, Tim Shaw, Philip Poronnik, Janette Bobis, Jennifer Way, Alyson Simpson, Louise Sutherland, Jen Curwood, Michael Jacobson, kathryn Bartimote-Aufflick, Kalina Yacef, Irena Koprinska, Rafael Calvo. $150K, 2017.
  • MBI Strategic grant (Marie Bashir Institute for Emerging Infectious Disease and Biosecurity): Mapping dynamic immunity: next-generation computational approaches to track the evolution of immune responses in West Nile virus and Zika virus encephalitis, Thomas Ashhurst, Mark Read, Nicholas King, Uwe Roehm, Irena Koprinska, Richard Scalzo, $10K, 2017.
  • ARC Linkage: Quantifying intake of food prepared outside home during emerging adulthood, Margaret Allman-Farinelli, Judy Kay, Kathryn Chapman, Clare Hughes, Wendy Watson, Anna Rangan, Kalina Yacef, Irena Koprinska, Cliona Ni Mhurchu, Adrian Bauman. $202K, 2015-2018.
  • WUN Research Development Fund: Feasibility Study of Recommender Systems in Academia, E. Smirnov, K. Driessens, I. Koprinska, K. Yacef, O. Zaiane, H. Drachsler and A. Surpatean (collaboration with the University of Maastricht, University of Alberta, Open University of the Netherlands and Data Science Consultancy, the Netherlands). GBP 35K, 2015.
  • Clear Energy and Intelligent Networks Research Cluster Award: Forecasting Solar Power Production Using Advanced Machine Learning Methods, Irena Koprinska. $5K, 2014-2015.
  • Smart Services CRC grant:  New Media Services - H5, Kalina Yacef, Judy Kay, Irena Koprinksa, Ying Zhou and Sanjay Chawla. $63K, 2012-2013.
  • Faculty of Engineering and Information Technologies grant:  Human Centred Technology Cluster Seed Grant, Judy Kay, Alex Blaszczynski, Rafael Calvo, James Curran, Andy Dong, Peter Goodyear, Craig Jin, Irena Koprinska, Bob Kummerfeld, Alistair McEwan, Fabio Ramos, David Rye, Masahiro Takatsuka, Martin Tomisch and Kalina Yacef, $25K, 2012.
  • Smart Services CRC grant: Personalisation H4, Judy Kay, Irena Koprinska and Kalina Yacef. $234K, 2011-2012.
  • Smart Services CRC grant: New Media Services NMS04, Judy Kay, Irena Koprinska, Kalina Yacef, Ying Zhou and Sanjay Chawla. $194K, 2011-201.
  • Smart Services CRC grant: Personalisation H3, Judy Kay, Irena Koprinska and Kalina Yacef, $142K, 2010-2011.
  • Smart Services CRC grant: Multi-channel Content Delivery and Mobile Personalisation H3, James Curran, Michael Fry, Judy Kay, Irena Koprinska, Bob Kummerfeld and Kalina Yacef, $379K, 2010-2011.
  • Smart Services CRC grant: Personalisation, Judy Kay, James Curran, Irena Koprinska, Kalina Yacef. $253K, 2009-2010.
  • University of Sydney Bridging support grant: Sequential Patttern Analysis of Learning Traces, Kalina Yacef, Judy Kay and Irena Koprinska, $15K, 2007.
  • University of Sydney bridging support grant: Data Mining of Learner Models, Kalina Yacef, Judy Kay and Irena Koprinska, $36K, 2006.
  • Smart Internet Technology CRC grant: Bridging the Gap: Smart Support for the Intergenerational Distributed Family, Bob Kummerfeld, Judy Kay, Kalina Yacef, Irena Koprinska and Josiah Poon, $270K, 2005-2007.
  • Smart Internet Technology CRC grant: Machine Learning for the Smart Personal Assistant, Irena Koprinska and Josiah Poon, $19K, 2003-2004. Collaboration with ANU and Griffith University.
  • SESQUI Early Career grant: Video Segmentation and Summarization Using Neural Networks, Irena Koprinska, $16K, 2002.
  • Smart Internet Technology CRC: Knowledge Aquisition and Machine Learning, Joseph Davis, Judy Kay, Irena Koprinska, Josiah Poon, Masahiro Takatsuka and Kalina Yacef, $46K, 2002.