People_

Professor Irena Koprinska

PhD and MSc (Computer Science), MEd (Higher Education)

School of Computer Science

Phone

+61 2 9351 3764

Fax

+61 2 9351 3838

Email

irena.koprinska@sydney.edu.au

Address

J12 - Computer Science Building

The University of Sydney

Websites

Computer Human Adapted Interaction Research Group

Personal Page

School of Computer Science

Biographical details

I am Professor at the School of Computer Science, University of Sydney. My research is in Machine Learning, Data Mining and Neural Networks. I also teach courses in these areas -Introduction to Artificial Intelligence, and Machine Learning and Data Mining. I have received several best paper and other research awards and two times the Dean’s Award for Outstanding Teaching. I have supervised to completion 11 PhD and about 60 Honours thesis students, all in Machine Learning. My students have received many prizes and awards including the Google Fellowship in Machine Learning which recognises exceptional research and the prestigious ZONTA International Women in Technology Scholarship. I have been co-organising the Data Science for Social Good workshop at ECML PKDD since 2017. I am Associate Editor of the International Journal of Artificial Intelligence in Education (IJAIED) and the International Journal of Data Science and Analytics (IJDSA), and an editorial board member of the Journal of Educational Data Mining (JEDM) and Intelligent Data Analysis (IDA); I have taken leadership roles in leading conferences on Artificial Intelligence and Machine Learning and regularly serve on their program committees. I am currently the Associate Head Research Education for the School of Computer Science, and previously was the Sub-Dean Teaching and Learning for the Faculty of Engineering.

Research interests

Machine Learning, Data Mining, Neural Networks

I am motivated by the many practical applications of machine learning, and their potential to benefit society. I develop algorithms that extract patterns from data and build models to help humans make better decisions. My work is multidisciplinary, involving experts from different areas. I am interested in both algorithms and applications, especially applications in education, health and energy.

For example, I have worked on machine learning for: (i) education - for understanding student behaviour and how it changes over time and for early prediction of at-risk students for timely intervention; (ii) health - for predicting medical conditions (sleep apnea, insomnia) based on health data recordings, for understanding immune response to diseases based on cytometry data, for predicting hospital admissions and length of stay; (III) enery - for predicting renewable energy generation (e.g. solar) and electricity demand from multiple time series data sources.

“I’m honoured to have worked with many wonderful students over the years – Honours, Dalyell/Talented Student/Special Projects, MPhil, PhD, visiting students - from Germany, Italy, Slovakia, France, Netherlands etc. This has been an inspirational and very rewarding experience.”

Teaching and supervision

COMP3308 Introduction to Artificial Intelligence

COMP3608 Introduction to Artificial Intelligence (Advanced)

COMP5318 Machine Learning and Data Mining

COMP4318 Machine Learning and Data Mining

PhD and Master’s project opportunities

I am offering projects in Machine Learning, Data Mining and Neural Networks.

Awards and honours

2017 and 2008, Dean’s Award for Outstanding Teaching (I received the Faculty Award)

2019, Best Paper Award at CHI 2019

2019, Winner of Dolby Paper Competition for our journal paper in Knowledge-Based Systems

2018, Thompson Research Fellowship, The University of Sydney

2019, Best Student Paper Award Finalist at ICONIP 2019

2018, Best Student Paper Award at ICONIP 2018

2017, JCAI 2017 paper featured in the press opening of the conference

2009, Best Student Paper Award at ADCS 2009

Current research students

Project title	Research student
Deep Learning for Time Series Forecasting	Hanxue YAO

Publications

By Type

Expand all

Book Chapters

Lin, Y., Koprinska, I., Rana, M., Troncoso, A. (2020). Solar Power Forecasting Based on Pattern Sequence Similarity and Meta-learning. In Igor Farkas, Paolo Masulli, Stefan Wermter (Eds.), Artificial Neural Networks and Machine Learning - ICANN 2020, (pp. 271-283). Cham: Springer. [More Information]
Rana, M., Koprinska, I., Khosravi, A. (2015). Feature Selection for Interval Forecasting of Electricity Demand Time Series Data. In Petia Koprinkova-Hristova, Valeri Mladenov, Nikola K. Kasabov (Eds.), Artificial Neural Networks: Methods and Applications in Bio-/Neuroinformatics, (pp. 445-462). Heidelberg: Springer Science+Business Media. [More Information]
Koprinska, I., Yacef, K. (2015). People to People Reciprocal Reccomenders. In F. Ricci, L. Rokach, B. Shapira (Eds.), Recommender Systems Handbook, (pp. 545-567). New York: Springer Science+Business Media. [More Information]
Kay, J., Koprinska, I., Yacef, K. (2011). Educational Data Mining to Support Group Work in Software Development Projects. In Cristobal Romero, Sebastian Ventura, Mykola Pechenizkiy, Ryan S J d Baker (Eds.), Handbook of Educational Data Mining, (pp. 173-185). USA: CRC Press. [More Information]
Koprinska, I. (2010). Feature Selection for Brain-Computer Interfaces. In Theeramunkong, Nattee, Adeodato, Chawla, Christen, Lenca, Poon, Williams (Eds.), New Frontiers in Applied Data Mining: PAKDD 2009 International Workshops - Lecture Notes in Artificial Intelligence - LNAI Volume 5669, (pp. 106-117). Berlin Heidelberg: Springer. [More Information]
Koprinska, I., Carrato, S. (2003). Segmentation techniques for video sequences in the domain of MPEG-compressed data. In Tasic, Najim, Ansorge (Eds.), Intelligent Integrated Media Communication Techniques, (pp. 89-116). Netherlands: Springer.

show 3 more

Journals

Luo, L., Li, B., Fan, X., Wang, Y., Koprinska, I., Chen, F. (2023). Dynamic customer segmentation via hierarchical fragmentation-coagulation processes. Machine Learning, 112(1), 281-310. [More Information]
Wang, E., Koprinska, I., Jeffries, B. (2023). Sleep Apnea Prediction Using Deep Learning. IEEE Journal of Biomedical and Health Informatics, 27(11), 5644-5654. [More Information]
Putri, G., Chung, J., Edwards, D., Marsh-Wakefield, F., Koprinska, I., Dervish, S., King, N., Ashhurst, T., Read, M. (2023). TrackSOM: mapping immune response dynamics through clustering of time-course cytometry data. Cytometry Part A, 103(1), 54-70. [More Information]
McBroom, J., Koprinska, I., Yacef, K. (2022). A Survey of Automated Programming Hint Generation: The HINTS Framework. ACM Computing Surveys, 54(8), 172. [More Information]
Paassen, B., Koprinska, I., Yacef, K. (2022). Recursive tree grammar autoencoders. Machine Learning, 111(9), 3393-3423. [More Information]
Nassif, E., Davies, A., Bente, K., Wellard-Cole, L., Jung, J., Kay, J., Hughes, C., Koprinska, I., Watson, W., Yacef, K., Rangan, A., Bauman, A., Allman-Farinelli, M., et al (2022). The Contribution of Nutrients of Concern to the Diets of 18-to-30-Year-Old Australians from Food Prepared Outside Home Differs by Food Outlet Types: The MYMeals Cross-Sectional Study. Nutrients, 14(18). [More Information]
Paassen, B., McBroom, J., Jeffries, B., Yacef, K., Koprinska, I. (2021). Mapping Python Programs to Vectors using Recursive Neural Encodings. Journal of Educational Data Mining (JEDM), 13(3), 1-35. [More Information]
Wellard-Cole, L., Davies, A., Chen, J., Jung, J., Bente, K., Kay, J., Watson, W., Hughes, C., Rangan, A., Yacef, K., Koprinska, I., Wong, N., Gemming, L., Bauman, A., Allman-Farinelli, M., et al (2021). The Contribution of Foods Prepared Outside the Home to the Diets of 18- to 30- Year-Old Australians: The MYMeals Study. Nutrients, 13(6), 1761. [More Information]
Putri, G., Koprinska, I., Ashhurst, T., King, N., Read, M. (2021). Using single-cell cytometry to illustrate integrated multi-perspective evaluation of clustering algorithms using Pareto fronts. Bioinformatics, 37(14), 1972-1981. [More Information]
Jung, J., Wellard-Cole, L., Cai, C., Koprinska, I., Yacef, K., Allman-Farinelli, M., Kay, J. (2020). Foundations for Systematic Evaluation and Benchmarking of a Mobile Food Logger in a Large-scale Nutrition Study. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(2), 1-25. [More Information]
Taib, R., Berkovsky, S., Koprinska, I., Wang, E., Zeng, Y., Li, J. (2020). Personality Sensing: Detection of Personality Traits Using Physiological Responses to Image and Video Stimuli. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(3), 1-32. [More Information]
Torres, J., Troncoso, A., Koprinska, I., Wang, Z., Martinez-Alvarez, F. (2019). Big data solar power forecasting based on deep learning and multiple data sources. Expert Systems, 36(4), 1-14. [More Information]
Putri, G., Read, M., Koprinska, I., Singh, D., Roehm, U., Ashhurst, T., King, N. (2019). ChronoClust: Density-based clustering and cluster tracking in high-dimensional time-series data. Knowledge-Based Systems, 174, 9-26. [More Information]
McCloskey, S., Jeffries, B., Koprinska, I., Miller, C., Grunstein, R. (2019). Data-driven cluster analysis of insomnia disorder with physiology-based qEEG variables. Knowledge-Based Systems, 183, 1-11. [More Information]
Galicia, A., Talavera-Llames, R., Troncoso, A., Koprinska, I., Martinez-Alvarez, F. (2019). Multi-step Forecasting for Big Data Time Series Based on Ensemble Learning. Knowledge-Based Systems, 163, 830-841. [More Information]
Rendell, K., Koprinska, I., Kyme, A., Ebker-White, A., Dinh, M. (2019). The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision-making. EMA - Emergency Medicine Australasia, 31(3), 429-435. [More Information]
Wellard-Cole, L., Jung, J., Kay, J., Rangan, A., Chapman, K., Watson, W., Hughes, C., Ni Mhurchu, C., Bauman, A., Gemming, L., Yacef, K., Koprinska, I., Allman-Farinelli, M. (2018). Examining the Frequency and Contribution of Foods Eaten Away From Home in the Diets of 18- to 30-Year-Old Australians Using Smartphone Dietary Assessment (MYMeals): Protocol for a Cross-Sectional Study. Journal of Medical Internet Research, 7(1), 1-13. [More Information]
Al-Ani, A., Koprinska, I., Naik, G. (2017). Dynamically identifying relevant EEG channels by utilizing channels classification behaviour. Expert Systems with Applications, 83, 273-282. [More Information]
Driessens, K., Santos, O., Yacef, K., Koprinska, I., Smirnov, E., Zaiane, O. (2017). EdRecSys 2017 workshop organizers' welcome. #ISOJ (International Symposium on Online Journalism), , 125-126. [More Information]
Rana, M., Koprinska, I. (2016). Forecasting electricity load with advanced wavelet neural networks. Neurocomputing, 182, 118-132. [More Information]
Rana, M., Koprinska, I. (2016). Neural Network Ensemble Based Approach for 2D-Interval Prediction of Solar Photovoltaic Power. Energies, 9(10), 1-17. [More Information]
Rana, M., Koprinska, I., Agelidis, V. (2016). Univariate and multivariate methods for very short-term solar photovoltaic power forecasting. Energy Conversion and Management, 121, 380-390. [More Information]
Rana, M., Koprinska, I., Agelidis, V. (2015). 2D-interval forecasts for solar power production. Solar Energy, 122, 191-203. [More Information]
Koprinska, I., Rana, M., Agelidis, V. (2015). Correlation and instance based feature selection for electricity load forecasting. Knowledge-Based Systems, 82, 29-40. [More Information]
Pizzato, L., Rej, T., Akehurst, J., Koprinska, I., Yacef, K., Kay, J. (2013). Recommending people to people: the nature of reciprocal recommenders with a case study in online dating. User Modeling and User-Adapted Interaction, 23(5), 447-488. [More Information]
Pizzato, L., Akehurst, J., Silvestrini, C., Yacef, K., Koprinska, I., Kay, J. (2012). The effect of suspicious profiles on people recommenders. Lecture Notes in Computer Science (LNCS), 7379, 225-236. [More Information]
Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaiane, O. (2009). Clustering and Sequential Pattern Mining of Online Collaborative Learning Data. IEEE Transactions On Knowledge And Data Engineering, 21(6), 759-772. [More Information]
Chan, J., Koprinska, I., Poon, J. (2008). Semi-supervised classification using bridging. International Journal on Artificial Intelligence Tools, 17(3), 415-431. [More Information]
Koprinska, I., Poon, J., Clark, J., Chan, J. (2006). Learning to classify e-mail. Information Sciences, Online Article, 1-21. [More Information]
Koprinska, I., Carrato, S. (2002). Hybrid Rule-Based/Neural Approach for Segmentation of MPEG Compressed Video. Multimedia Tools and Applications, 18(3), 187-212. [More Information]
Koprinska, I., Carrato, S. (2002). Temporal Video Segmentation: A Survey. Signal Processing: Image Communication, 16(5), 477-500.

show 28 more

Conferences

McCloskey, S., Jeffries, B., Koprinska, I., Gordon, C., Grunstein, R. (2022). Insomnia Disorder Detection Using EEG Sleep Trajectories. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), : Springer Verlag.
Gao, Y., Kholghi, M., Koprinska, I., Zhang, Q. (2021). Association of Longitudinal Sleep and Next-day Indoor Mobility Measured via Passive Sensors among Community-dwelling Older Adults. 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021, Mexico: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Paaßen, B., McBroom, J., Jeffries, B., Koprinska, I., Yacef, K. (2021). Next Steps for Next-step Hints: Lessons Learned from Teacher Evaluations of Automatic Programming Hints. 2021 Joint Workshops at the International Conference on Educational Data Mining, EDM-WS 2021, Germany: CEUR-WS.
Paaßen, B., McBroom, J., Jeffries, B., Koprinska, I., Yacef, K. (2021). Next Steps for Next-step Hints: Lessons Learned from Tutor Evaluations of Automatic Programming Hints. 5th Educational Data Mining in Computer Science Education (CSEDM) Workshop, France: International Educational Data Mining Society.
McBroom, J., Paassen, B., Jeffries, B., Koprinska, I., Yacef, K. (2021). Progress Networks as a Tool for Analysing Student Programming Difficulties. 23rd Australasian Computing Education Conference (ACE'21), New York: Association for Computing Machinery (ACM). [More Information]
Lin, Y., Koprinska, I., Rana, M. (2021). SSDNet: State Space Decomposition Neural Network for Time Series Forecasting. 21st IEEE International Conference on Data Mining, ICDM 2021, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers Inc. [More Information]
Lin, Y., Koprinska, I., Rana, M. (2021). Temporal Convolutional Attention Neural Networks for Time Series Forecasting. 2021 International Joint Conference on Neural Networks, IJCNN 2021, Shenzhen: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
McBroom, J., Yacef, K., Koprinska, I. (2020). DETECT: A hierarchical clustering algorithm for behavioural trends in temporal educational data. 21st International Conference on Artificial Intelligence in Education (AIED 2020), Cham: Springer. [More Information]
Sharan, R., Berkovsky, S., Taib, R., Koprinska, I., Li, J. (2020). Detecting Personality Traits Using Inter-Hemispheric Asynchrony of the Brainwaves. 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society (EMBC 2020), : Springer Verlag. [More Information]
Chen, Z., Koprinska, I. (2020). Ensemble Methods for Solar Power Forecasting. 2020 International Joint Conference on Neural Networks (IJCNN), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
McBroom, J., Koprinska, I., Yacef, K. (2020). How Does Student Behaviour Change Approaching Dropout? A Study of Gender and School Year Differences. The 13th International Conference on Educational Data Mining (EDM 2020), Montreal: International Educational Data Mining Society.
McBroom, J., Yacef, K., Koprinska, I. (2020). Scalability in Online Computer Programming Education: Automated Techniques for Feedback, Evaluation and Equity. The 13th International Conference on Educational Data Mining (EDM 2020), Montreal: International Educational Data Mining Society.
Haidar, R., Koprinska, I., Jeffries, B. (2020). Sleep Apnea Event Prediction Using Convolutional Neural Networks and Markov Chains. 2020 International Joint Conference on Neural Networks (IJCNN), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Lin, Y., Koprinska, I., Rana, M. (2020). SpringNet: Transformer and Spring DTW for Time Series Forecasting. 27th International Conference on Neural Information Processing (ICONIP 2020), Cham: Springer. [More Information]
Lin, Y., Koprinska, I., Rana, M. (2020). Temporal Convolutional Neural Networks for Solar Power Forecasting. 2020 International Joint Conference on Neural Networks (IJCNN), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Paassen, B., Koprinska, I., Yacef, K. (2020). Tree Echo State Autoencoders with Grammars. 2020 International Joint Conference on Neural Networks (IJCNN), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
McBroom, J., Koprinska, I., Yacef, K. (2020). Understanding gender differences to improve equity in computer programming education. 22nd Australasian Computing Education Conference (ACE'20), New York: Association for Computing Machinery (ACM). [More Information]
Koprinska, I., Kamp, M., Appice, A., Loglisci, C., Antonie, L., Zimmermann, A., Guidotti, R., Özgöbek, Ö., et al (2020). Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases. Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases, Cham: Springer. [More Information]
Torres, J., Troncoso, A., Koprinska, I., Wang, Z., Martinez-Alvarez, F. (2019). Deep Learning for Big Data Time Series Forecasting Applied to Solar Power. 13th International Conference on Soft Computing Models (SOCO'18), 11th International Conference on Computational Intelligence in Security for Information Systems (CISIS'18) & 9th International Conference on European Transnational Education (ICEUTE'18), Cham: Springer. [More Information]
Berkovsky, S., Taib, R., Koprinska, I., Wang, X., Zeng, Y., Li, J., Kleitman, S. (2019). Detecting Personality Traits Using Eye-Tracking Data. 2019 CHI Conference on Human Factors in Computing Systems (CHI 2019), New York: Association for Computing Machinery (ACM). [More Information]
Putri, G., Read, M., Koprinska, I., Ashhurst, T., King, N. (2019). Dimensionality Reduction for Clustering and Cluster Tracking of Cytometry Data. 28th International Conference on Artificial Neural Networks (ICANN 2019), Cham: Springer Nature. [More Information]
Koprinska, I., Rana, M., Rahman, A. (2019). Dynamic Ensemble Using Previous and Predicted Future Performance for Multi-step-ahead Solar Power Forecasting. 28th International Conference on Artificial Neural Networks (ICANN 2019), Cham: Springer Nature. [More Information]
Haidar, R., Koprinska, I., Jeffries, B. (2019). Feature Learning and Data Compression of Biosignals Using Convolutional Autoencoders for Sleep Apnea Detection. 26th International Conference on Neural Information Processing (ICONIP 2019), Cham: Springer International Publishing. [More Information]
Lin, Y., Koprinska, I., Rana, M., Troncoso, A. (2019). Patter Sequence Neural Network for Solar Power Forecasting. 26th International Conference on Neural Information Processing (ICONIP 2019), Cham: Springer International Publishing. [More Information]
McBroom, J., Yacef, K., Koprinska, I., Curran, J. (2018). A data-driven method for helping teachers improve feedback in computer programming automated tutors. 19th International Conference on Artificial Intelligence in Education (AIED 2018), Cham: Springer. [More Information]
Koprinska, I., Wu, D., Wang, Z. (2018). Convolutional Neural Networks for Energy Time Series Forecasting. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Haidar, R., McCloskey, S., Koprinska, I., Jeffries, B. (2018). Convolutional Neural Networks on Multiple Respiratory Channels to Detect Hypopnea and Obstructive Apnea Events. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
McCloskey, S., Haidar, R., Koprinska, I., Jeffries, B. (2018). Detecting hypopnea and obstructive apnea events using convolutional neural networks on wavelet spectrograms of nasal airflow. 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2018), Cham: Springer. [More Information]
Alzate, C., Monreale, A., Assem, H., Bifet, A., Buda, T., Caglayan, B., Drury, B., García-Martín, E., Gavaldà, R., Koprinska, I., et al (2018). ECML PKDD 2018 Workshops. ECML PKDD: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Cham: Springer Link. [More Information]
Wang, Z., Koprinska, I. (2018). Solar power forecasting using dynamic meta-learning ensemble of neural networks. 27th International Conference on Artificial Neural Networks (ICANN 2018), Cham: Springer Nature. [More Information]
Wang, Z., Koprinska, I., Troncoso, A., Martinez-Alvarez, F. (2018). Static and Dynamic Ensembles of Neural Networks for Solar Power Forecasting. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Chow, S., Yacef, K., Koprinska, I., Curran, J. (2017). Automated data-driven hints for computer programming students. 25th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2017), New York: Association for Computing Machinery (ACM). [More Information]
Luo, L., Liu, W., Koprinska, I., Chen, F. (2017). DAAR: A discrimination-aware association rule classifier for decision support. 17th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2015), Cham: Springer. [More Information]
Luo, L., Li, B., Berkovsky, S., Koprinska, I., Chen, F. (2017). Online Engagement for a Healthier You: A Case Study of Web-based Supermarket Health Program. 26th International World Wide Web Conference (WWW 2017), Perth, WA: Association for Computing Machinery (ACM). [More Information]
Haidar, R., Koprinska, I., Jeffries, B. (2017). Sleep Apnea Event Detection from Nasal Airflow Using Convolutional Neural Networks. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part V), Cham: Springer. [More Information]
Wang, Z., Koprinska, I., Rana, M. (2017). Solar power forecasting using pattern sequences. 26th International Conference on Artificial Neural Networks, ICANN 2017, Cham: Springer. [More Information]
Wang, Z., Koprinska, I., Rana, M. (2017). Solar Power Prediction Using Weather Type Pair Patterns. The International Joint Conference on Neural Networks (IJCNN 2017), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Wang, Z., Koprinska, I. (2017). Solar Power Prediction with Data Source Weighted Nearest Neighbors. The International Joint Conference on Neural Networks (IJCNN 2017), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Luo, L., Li, B., Koprinska, I., Berkovsky, S., Chen, F. (2017). Tracking the evolution of customer purchase behavior segmentation via a fragmentation-coagulation process. 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne: International Joint Conferences on Artificial Intelligence. [More Information]
Al-Ani, A., Koprinska, I., Naik, G., Khushaba, R. (2016). A Dynamic Channel Selection Algorithm for the Classification of EEG and EMG data. 2016 International Joint Conference on Neural Networks (IJCNN 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Wang, Z., Koprinska, I., Rana, M. (2016). Clustering Based Methods for Solar Power Forecasting. 2016 International Joint Conference on Neural Networks (IJCNN 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Luo, L., Li, B., Koprinska, I., Berkonsky, S., Chen, F. (2016). Discovering Temporal Purchase Patterns with Different Responses to Promotions. 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), New York: Association for Computing Machinery (ACM). [More Information]
McBroom, J., Jeffries, B., Koprinska, I., Yacef, K. (2016). Exploring and following students' strategies when completing their weekly tasks. The 9th International Conference on Educational Data Mining (EDM 2016), Raleigh: International Educational Data Mining Society.
Rana, M., Koprinska, I., Troncoso, A., Agelidis, V. (2016). Extended Weighted Nearest Neighbor for Electricity Load Forecasting. 25th International Conference on Artificial Neural Networks and Machine Learning (ICANN 2016), Cham, Switzerland: Springer International Publishing Switzerland. [More Information]
Gramoli, V., Charleston, M., Jeffries, B., Koprinska, I., McGrane, M., Radu, A., Viglas, A., Yacef, K. (2016). Mining autograding data in Computer Science Education. Eighteenth Australasian Computing Education Conference (ACE 2016), New York: Association for Computing Machinery (ACM). [More Information]
McBroom, J., Jeffries, B., Koprinska, I., Yacef, K. (2016). Mining behaviors of students in autograding submission system logs. The 9th International Conference on Educational Data Mining (EDM 2016), Raleigh: International Educational Data Mining Society.
Rana, M., Koprinska, I., Agelidis, V. (2016). Solar Power Forecasting Using Weather Type Clustering and Ensembles of Neural Networks. 2016 International Joint Conference on Neural Networks (IJCNN 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Luo, L., Li, B., Berkovsky, S., Koprinska, I., Chen, F. (2016). Who will be Affected by Supermarket Health Programs? Tracking Customer Behavior Changes via Preference Modeling. The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2016), Switzerland: Springer. [More Information]
Gupta, J., Koprinska, I., Patrick, J. (2015). Automated Classification of Clinical Incident Types. 23rd Australian National Health Informatics Conference (HIC 2015), Amsterdam: IOS Press. [More Information]
Luo, L., Liu, W., Koprinska, I., Chen, F. (2015). Discovering Causal Structures from Time Series Data via Enhanced Granger Causality. 28th Australasian Joint Conference on Artificial Intelligence (AI 2015), Cham: Springer. [More Information]
Luo, L., Liu, W., Koprinska, I., Chen, F. (2015). Discrimination-Aware Association Rule Mining for Unbiased Data Analytics. 17th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2015), Cham: Springer. [More Information]
Luo, L., Koprinska, I., Liu, W. (2015). Discrimination-Aware Classifiers for Student Performance Prediction. The 8th International Conference on Educational Data Mining (EDM), Madrid: International Educational Data Mining Society.
Rana, M., Koprinska, I., Agelidis, V. (2015). Forecasting Solar Power Generated by Grid Connected PV System Using Ensembles of Neural Networks. 2015 International Joint Conference on Neural Networks (IJCNN 2015), Killarney: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Colombo, T., Koprinska, I., Panella, M. (2015). Maximum Length Weighted Nearest Neighbor approach for electricity load forecasting. 2015 International Joint Conference on Neural Networks (IJCNN 2015), Killarney: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Koprinska, I., Stretton, J., Yacef, K. (2015). Predicting student performance from multiple data sources. 17th International Conference on Artificial Intelligence in Education (AIED 2015), : Springer Verlag. [More Information]
Koprinska, I., Stretton, J., Yacef, K. (2015). Students at Risk: Detection and Remediation. The 8th International Conference on Educational Data Mining (EDM), Madrid: International Educational Data Mining Society.
Rana, M., Koprinska, I., Troncoso, A. (2014). Forecasting hourly electricity load profile using neural networks. The Annual International Joint Conference on Neural Networks (IJCNN 2014), Piscataway, USA: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
O'Keefe, T., Curran, J., Ashwell, P., Koprinska, I. (2013). An Annotated Corpus of Quoted Opinions in News Articles. 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Stroudsburg, PA USA: Association for Computational Linguistics (ACL).
Pareti, S., O'Keefe, T., Konstas, I., Curran, J., Koprinska, I. (2013). Automatically Detecting and Attributing Indirect Quotations. 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), Seattle, USA: Association for Computational Linguistics (ACL).
Xu, M., Berkovsky, S., Ardon, S., Triukose, S., Mahanti, A., Koprinska, I. (2013). Catch-up TV Recommendations: Show Old Favourites and Find New Ones. 7th ACM Conference on Recommender Systems (RecSys 2013), New York: Association for Computing Machinery (ACM). [More Information]
Koprinska, I., Rana, M., Troncoso, A., Martinez-Alvarez, F. (2013). Combining Pattern Sequence Similarity with Neural Networks for Forecasting Electricity Demand Time Series. 2013 International Joint Conference on Neural Networks (IJCNN 2013), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Rana, M., Koprinska, I., Khosravi, A. (2013). Feature Selection for Neural Network-Based Interval Forecasting of Electricity Demand Data. 23rd International Conference on Artificial Neural Networks (ICANN 2013), Berlin: Springer-Verlag. [More Information]
Rana, M., Koprinska, I., Khosravi, A., Agelidis, V. (2013). Prediction intervals for electricity load forecasting using neural networks. 2013 International Joint Conference on Neural Networks (IJCNN 2013), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Rana, M., Koprinska, I. (2013). Wavelet Neural Networks for Electricity Load Forecasting - Dealing with Border Distortion and Shift Invariance. 23rd International Conference on Artificial Neural Networks (ICANN 2013), Berlin: Springer-Verlag. [More Information]
O'Keefe, T., Pareti, S., Curran, J., Koprinska, I., Honnibal, M. (2012). A Sequence Labelling Approach to Quote Attribution. Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (emnlp-conll 2012), Stroudsburg, PA: Association for Computational Linguistics (ACL).
Rana, M., Koprinska, I. (2012). Electricity Load Forecasting Using Non-decimated Wavelet Prediction Methods With Two-Stage Feature Selection. 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Koprinska, I., Rana, M., Agelidis, V. (2012). Electricity Load Forecasting: A Weekday-Based Approach. 22nd ICANN International Conference on Artificial Neural Networks, Berlin, Germany: Springer. [More Information]
Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L., Kay, J., Rej, T. (2012). Explicit and Implicit User Preferences in Online Dating. 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011, Heidelberg, Germany: Springer. [More Information]
Rana, M., Koprinska, I., Agelidis, V. (2012). Feature Selection for Electricity Load Prediction. The 19th International Conference on Neural Information Processing (ICONIP2012), Heidelberg: Springer-Verlag. [More Information]
Kotillova, A., Koprinska, I., Rana, M. (2012). Statistical and Machine Learning Methods for Electricity Demand Prediction. The 19th International Conference on Neural Information Processing (ICONIP2012), Heidelberg: Springer-Verlag. [More Information]
Xu, M., Berkovsky, S., Koprinska, I., Ardon, S., Yacef, K. (2012). Time Dependency in TV Viewer Clustering. PATCH: 4th International Workshop on Personal Access to Cultural Heritage at the 20th Conference on User Modeling, Adaptation, and Personalization (UMAP), online: CEUR-WS.
Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L., Kay, J., Rej, T. (2011). CCR - A Content-Collaborative Reciprocal Recommender for Online Dating. 22nd International Joint Conference on Artificial Intelligence, Menlo Park, California, USA: AAAI Press.
Hawson, L., Koprinska, I., McLean, A., McGreevy, P. (2011). Deciphering the cues from riders' legs. 7th International Equitation Science Conference, the Netherlands: Wageningen Academic Publishers.
Pizzato, L., Rej, T., Yacef, K., Koprinska, I., Kay, J. (2011). Finding someone you will like and who won't reject you. 19th International Conference on User Modeling, Adaptation and Personalization, UMAP 2011, Berlin, Heidelberg: Springer. [More Information]
Koprinska, I. (2011). Mining Assessment and Teaching Evaluation Data of Regular and Advanced Stream Students. 4th International Conference on Educational Data Mining (EDM 2011), Eindhoven, Netherlands: Technische Universiteit Eindhoven.
Koprinska, I., Rana, M., Agelidis, V. (2011). Yearly and seasonal models for electricity load forecasting. 2011 International Joint Conference on Neural Networks (IJCNN 2011), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Sood, R., Koprinska, I., Agelidis, V. (2010). Electricity Load Forecasting Based on Autocorrelation Analysis. 2010 IEEE Congress on Evolutionary Computation (IEEE-CEC) - WCCI 2010, USA: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Pizzato, L., Chung, T., Rej, T., Koprinska, I., Yacef, K., Kay, J. (2010). Learning User Preferences in Online Dating. European conference on machine learning and principles and practice of knowledge discovery in databases (2012 ECML PKDD), Spain: ECML PKDD.
Pizzato, L., Rej, T., Chung, T., Koprinska, I., Yacef, K., Kay, J. (2010). Reciprocal Recommender System for Online Dating. Fourth ACM Conference on Recommender Systems (RecSys10), USA: Association for Computing Machinery (ACM). [More Information]
Pizzato, L., Rej, T., Chung, T., Yacef, K., Koprinska, I., Kay, J. (2010). Reciprocal Recommenders. 8th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP 2010), USA: Intelligent Techniques for Web Personalization and Recommender Systems.
Pizzato, L., Rej, T., Chung, T., Koprinska, I., Kay, J. (2010). RECON: A Reciprocal Recommender for Online Dating. Fourth ACM Conference on Recommender Systems (RecSys10), USA: Association for Computing Machinery (ACM). [More Information]
Koprinska, I., Sood, R., Agelidis, V. (2010). Variable Selection for Five-Minute Ahead Electricity Load Forecasting. 20th International Conference on Pattern Recognition (ICPR 2010), USA: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Koprinska, I. (2009). Comparison of Feature Selection Methods for Classification of Brain-Computer Interface Data. Workshop on Advances and Issues in Biomedical Data Mining AIBDM 2009 - In association with the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'09), Thailand: Printing House of Thammasat University, Rangsit Campus.
O'Keefe, T., Koprinska, I. (2009). Feature Selection and Weighting Methods in Sentiment Analysis. 14th Australasian Document Computing Symposium, Sydney, Australia: School of Information Technologies, University of Sydney.
Setiawan, A., Koprinska, I., Agelidis, V. (2009). Very Short-Term Electricity Load Demand Forecasting Using Support Vector Regression. International Joint Conference on Neural Networks IJCNN 2009, United States: Documentation LLC. [More Information]
Al-Zoubi, O., Koprinska, I., Calvo, R. (2008). Classification of Brain-Computer Interface Data. The Seventh Australasian Data Mining Conference (AusDM 2008), Sydney NSW, Australia: Australian Computer Society.
Chan, J., Poon, J., Koprinska, I. (2007). Enhancing the Performance of Semi-Supervised Classification Algorithms with Bridging. The Twentieth International Florida Artificial Intelligence Research Society Conference, Menlo Park, California: AAAI Press.
Perera, D., Kay, J., Yacef, K., Koprinska, I. (2007). Mining learners' traces from an online collaboration tool. Workshop on Educational Data Mining 2007, online: International Working Group on Educational Data Mining.
Cummins, D., Yacef, K., Koprinska, I. (2006). A Sequence Based Recommender System for Learning Resources. ADCS 2006 11th Australasian Document Computing Symposium, Queensland: Faculty of Information Technology, Queensland University of Technology.
Feger, F., Koprinska, I. (2006). Co-training Using RBF Nets and Different Feature Splits. 2006 IEEE World Congress on Computational Intelligence - A Joint Conference of the Int Joint Conf on Neural Networks (IJCNN), Fuzzy Systems (FUZZ-IEEE), and Evolutionary Computation (CEC), USA: Institute of Electrical and Electronics Engineers (IEEE).
Koprinska, I., Deng, D., Felix, F. (2006). Image Classification Using Labelled And Unlabelled Data. 14th European Signal Processing Conference (EUSIPCO 2006), Europe: EURASIP European Association for Signal, Speech and Image Processing.
Ler, D., Koprinska, I., Chawla, S. (2005). A Hill-climbing Landmarker Generation Algorithm Based on Efficiency and Correlativity Criteria. The Eighteenth International Florida Artificial Intelligence Research Society Conference - FLAIRS 2005, USA: AAAI Press.
Ler, D., Koprinska, I., Chawla, S. (2005). Comparisons between Heuristics Based on Correlativity and Efficiency for Landmarker Generation. Fourth International Conference on Hybrid Intelligent Systems - HIS 2004, Los Alamitos, California, USA: Institute of Electrical and Electronics Engineers (IEEE).
Saberi, M., Carrato, S., Koprinska, I., Clark, J. (2005). Estimation of the Hierachical Structures of a Video Sequence Using MPEG-7 Descriptors and GCS. 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, Germany: Springer.
Ler, D., Koprinska, I., Chawla, S. (2005). Utilising Regression-based Landmarkers within a Meta-learning Framework for Algorithm Selection. W10 - Workshop on Meta-Learning : associated with the 22nd International Conference on Machine Learning (ICML 2005), Germany: International Machine Learning Society.
Ler, D., Koprinska, I., Chawla, S. (2004). A Landmarker Selection Algorithm Based On Correlation And Efficiency Criteria. 17th Australian Joint Conference on Artificial Intelligence, Berlin: Springer.
Ler, D., Koprinska, I., Chawla, S. (2004). A New Landmarker Generation Algorithm Based On Correlativity. The 2004 International Conference on Machine Learning and Applications (ICMLA'04), USA: Institute of Electrical and Electronics Engineers (IEEE).
Chan, J., Koprinska, I., Poon, J. (2004). Co-Training On Textual Documents With A Single Natural Feature Set. The Ninth Australasian Document Computing Symposium (ADCS 2004), Victoria, Australia: Department of Computer Science and Software Engineering, University of Melbourne.
Chan, J., Koprinska, I., Poon, J. (2004). Co-Training With A Single Natural Feature Set Applied To Email Classification. 2004 IEEE/WIC ACM International Conference on Web Intelligence, Los Alamitos, CA, USA: Institute of Electrical and Electronics Engineers (IEEE).
Crawford, E., Koprinska, I., Patrick, J. (2004). Phrases And Feature Selection In E-Mail Classification. The Ninth Australasian Document Computing Symposium (ADCS 2004), Victoria, Australia: Department of Computer Science and Software Engineering, University of Melbourne.
Koprinska, I., Clark, J. (2004). Video Summarization And Browsing Using Growing Cell Structures. 2004 IEEE International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers (IEEE).
Koprinska, I., Clark, J., Carrato, S. (2004). Videogcs - A Clustering-Based System For Video Summarizatiion And Browsing. 6th COST 276 Workshop on Information and Knowledge Management for Integrated Media Communication, Greece: Informatics and Telematics Institute, Centre for Research and Technology.
Clark, J., Koprinska, I., Poon, J. (2003). A neural network based approach to automatee e-mail classification. 2003 IEEE/WIC Intenational Conference on Web Intelligence, United States: Institute of Electrical and Electronics Engineers (IEEE).
Verhein, F., Kay, J., Koprinska, I., McCreath, E. (2003). Classifying public announcements for user communities. Eighth Australasian Document Computing Symposium, Canberra: CSIRO ICT Centre.
Koprinska, I., Trieu, F., Poon, J., Clark, J. (2003). E-mail classification by decision forests. Eighth Australasian Document Computing Symposium, Canberra: CSIRO ICT Centre.
Mak, H., Koprinska, I., Poon, J. (2003). INTIMATE: A web-based movie recommender using text categorization. 2003 IEEE/WIC Intenational Conference on Web Intelligence, United States: Institute of Electrical and Electronics Engineers (IEEE).
Clark, J., Koprinska, I., Poon, J. (2003). Linger - a smart personal assistant for e-mail classification. 2003 IEEE/WIC Intenational Conference on Web Intelligence, United States: Institute of Electrical and Electronics Engineers (IEEE).
Crawford, E., Koprinska, I., Patrick, J. (2002). A Multi-Learner Approach to E-mail Classification. ADCS '02 The Seventh Australasian Document Computing Symposium, Sydney, Australia: School of Information Technologies, University of Sydney.
Ceguerra, A., Koprinska, I. (2002). Automatic Fingerprint Verification Using Neural Networks. Artificial Neural Networks ICANN 2002 International Conference, Germany: Springer.
Jackson, K., Koprinska, I. (2002). DNA Microarray Data Clustering Using Growing Self Organizing Networks. 9th International Conference on Neural Information Processing (ICONIP''02) 4th Asia Pacific Conference on Simulated Evolution and Learning (SEAL''02) 1st International Conference on Fuzzy Systems and Knowledge Discovery (FSKD''02), Singapore: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
Ceguerra, A., Koprinska, I. (2002). Integrating Local and Global Features in Automatic Fingerprint Verification. 16th International Conference on Pattern Recognition, Los Alamitos, California: Institute of Electrical and Electronics Engineers (IEEE).

show 108 more

By Year

Expand all

2023

Luo, L., Li, B., Fan, X., Wang, Y., Koprinska, I., Chen, F. (2023). Dynamic customer segmentation via hierarchical fragmentation-coagulation processes. Machine Learning, 112(1), 281-310. [More Information]
Wang, E., Koprinska, I., Jeffries, B. (2023). Sleep Apnea Prediction Using Deep Learning. IEEE Journal of Biomedical and Health Informatics, 27(11), 5644-5654. [More Information]
Putri, G., Chung, J., Edwards, D., Marsh-Wakefield, F., Koprinska, I., Dervish, S., King, N., Ashhurst, T., Read, M. (2023). TrackSOM: mapping immune response dynamics through clustering of time-course cytometry data. Cytometry Part A, 103(1), 54-70. [More Information]

2022

McBroom, J., Koprinska, I., Yacef, K. (2022). A Survey of Automated Programming Hint Generation: The HINTS Framework. ACM Computing Surveys, 54(8), 172. [More Information]
McCloskey, S., Jeffries, B., Koprinska, I., Gordon, C., Grunstein, R. (2022). Insomnia Disorder Detection Using EEG Sleep Trajectories. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), : Springer Verlag.
Paassen, B., Koprinska, I., Yacef, K. (2022). Recursive tree grammar autoencoders. Machine Learning, 111(9), 3393-3423. [More Information]
Nassif, E., Davies, A., Bente, K., Wellard-Cole, L., Jung, J., Kay, J., Hughes, C., Koprinska, I., Watson, W., Yacef, K., Rangan, A., Bauman, A., Allman-Farinelli, M., et al (2022). The Contribution of Nutrients of Concern to the Diets of 18-to-30-Year-Old Australians from Food Prepared Outside Home Differs by Food Outlet Types: The MYMeals Cross-Sectional Study. Nutrients, 14(18). [More Information]

show 1 more

2021

Gao, Y., Kholghi, M., Koprinska, I., Zhang, Q. (2021). Association of Longitudinal Sleep and Next-day Indoor Mobility Measured via Passive Sensors among Community-dwelling Older Adults. 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021, Mexico: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Paassen, B., McBroom, J., Jeffries, B., Yacef, K., Koprinska, I. (2021). Mapping Python Programs to Vectors using Recursive Neural Encodings. Journal of Educational Data Mining (JEDM), 13(3), 1-35. [More Information]
Paaßen, B., McBroom, J., Jeffries, B., Koprinska, I., Yacef, K. (2021). Next Steps for Next-step Hints: Lessons Learned from Teacher Evaluations of Automatic Programming Hints. 2021 Joint Workshops at the International Conference on Educational Data Mining, EDM-WS 2021, Germany: CEUR-WS.
Paaßen, B., McBroom, J., Jeffries, B., Koprinska, I., Yacef, K. (2021). Next Steps for Next-step Hints: Lessons Learned from Tutor Evaluations of Automatic Programming Hints. 5th Educational Data Mining in Computer Science Education (CSEDM) Workshop, France: International Educational Data Mining Society.
McBroom, J., Paassen, B., Jeffries, B., Koprinska, I., Yacef, K. (2021). Progress Networks as a Tool for Analysing Student Programming Difficulties. 23rd Australasian Computing Education Conference (ACE'21), New York: Association for Computing Machinery (ACM). [More Information]
Lin, Y., Koprinska, I., Rana, M. (2021). SSDNet: State Space Decomposition Neural Network for Time Series Forecasting. 21st IEEE International Conference on Data Mining, ICDM 2021, Piscataway, New Jersey: Institute of Electrical and Electronics Engineers Inc. [More Information]
Lin, Y., Koprinska, I., Rana, M. (2021). Temporal Convolutional Attention Neural Networks for Time Series Forecasting. 2021 International Joint Conference on Neural Networks, IJCNN 2021, Shenzhen: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Wellard-Cole, L., Davies, A., Chen, J., Jung, J., Bente, K., Kay, J., Watson, W., Hughes, C., Rangan, A., Yacef, K., Koprinska, I., Wong, N., Gemming, L., Bauman, A., Allman-Farinelli, M., et al (2021). The Contribution of Foods Prepared Outside the Home to the Diets of 18- to 30- Year-Old Australians: The MYMeals Study. Nutrients, 13(6), 1761. [More Information]
Putri, G., Koprinska, I., Ashhurst, T., King, N., Read, M. (2021). Using single-cell cytometry to illustrate integrated multi-perspective evaluation of clustering algorithms using Pareto fronts. Bioinformatics, 37(14), 1972-1981. [More Information]

show 6 more

2020

McBroom, J., Yacef, K., Koprinska, I. (2020). DETECT: A hierarchical clustering algorithm for behavioural trends in temporal educational data. 21st International Conference on Artificial Intelligence in Education (AIED 2020), Cham: Springer. [More Information]
Sharan, R., Berkovsky, S., Taib, R., Koprinska, I., Li, J. (2020). Detecting Personality Traits Using Inter-Hemispheric Asynchrony of the Brainwaves. 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society (EMBC 2020), : Springer Verlag. [More Information]
Chen, Z., Koprinska, I. (2020). Ensemble Methods for Solar Power Forecasting. 2020 International Joint Conference on Neural Networks (IJCNN), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Jung, J., Wellard-Cole, L., Cai, C., Koprinska, I., Yacef, K., Allman-Farinelli, M., Kay, J. (2020). Foundations for Systematic Evaluation and Benchmarking of a Mobile Food Logger in a Large-scale Nutrition Study. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(2), 1-25. [More Information]
McBroom, J., Koprinska, I., Yacef, K. (2020). How Does Student Behaviour Change Approaching Dropout? A Study of Gender and School Year Differences. The 13th International Conference on Educational Data Mining (EDM 2020), Montreal: International Educational Data Mining Society.
Taib, R., Berkovsky, S., Koprinska, I., Wang, E., Zeng, Y., Li, J. (2020). Personality Sensing: Detection of Personality Traits Using Physiological Responses to Image and Video Stimuli. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(3), 1-32. [More Information]
McBroom, J., Yacef, K., Koprinska, I. (2020). Scalability in Online Computer Programming Education: Automated Techniques for Feedback, Evaluation and Equity. The 13th International Conference on Educational Data Mining (EDM 2020), Montreal: International Educational Data Mining Society.
Haidar, R., Koprinska, I., Jeffries, B. (2020). Sleep Apnea Event Prediction Using Convolutional Neural Networks and Markov Chains. 2020 International Joint Conference on Neural Networks (IJCNN), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Lin, Y., Koprinska, I., Rana, M., Troncoso, A. (2020). Solar Power Forecasting Based on Pattern Sequence Similarity and Meta-learning. In Igor Farkas, Paolo Masulli, Stefan Wermter (Eds.), Artificial Neural Networks and Machine Learning - ICANN 2020, (pp. 271-283). Cham: Springer. [More Information]
Lin, Y., Koprinska, I., Rana, M. (2020). SpringNet: Transformer and Spring DTW for Time Series Forecasting. 27th International Conference on Neural Information Processing (ICONIP 2020), Cham: Springer. [More Information]
Lin, Y., Koprinska, I., Rana, M. (2020). Temporal Convolutional Neural Networks for Solar Power Forecasting. 2020 International Joint Conference on Neural Networks (IJCNN), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Paassen, B., Koprinska, I., Yacef, K. (2020). Tree Echo State Autoencoders with Grammars. 2020 International Joint Conference on Neural Networks (IJCNN), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
McBroom, J., Koprinska, I., Yacef, K. (2020). Understanding gender differences to improve equity in computer programming education. 22nd Australasian Computing Education Conference (ACE'20), New York: Association for Computing Machinery (ACM). [More Information]
Koprinska, I., Kamp, M., Appice, A., Loglisci, C., Antonie, L., Zimmermann, A., Guidotti, R., Özgöbek, Ö., et al (2020). Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases. Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases, Cham: Springer. [More Information]

show 11 more

2019

Torres, J., Troncoso, A., Koprinska, I., Wang, Z., Martinez-Alvarez, F. (2019). Big data solar power forecasting based on deep learning and multiple data sources. Expert Systems, 36(4), 1-14. [More Information]
Putri, G., Read, M., Koprinska, I., Singh, D., Roehm, U., Ashhurst, T., King, N. (2019). ChronoClust: Density-based clustering and cluster tracking in high-dimensional time-series data. Knowledge-Based Systems, 174, 9-26. [More Information]
McCloskey, S., Jeffries, B., Koprinska, I., Miller, C., Grunstein, R. (2019). Data-driven cluster analysis of insomnia disorder with physiology-based qEEG variables. Knowledge-Based Systems, 183, 1-11. [More Information]
Torres, J., Troncoso, A., Koprinska, I., Wang, Z., Martinez-Alvarez, F. (2019). Deep Learning for Big Data Time Series Forecasting Applied to Solar Power. 13th International Conference on Soft Computing Models (SOCO'18), 11th International Conference on Computational Intelligence in Security for Information Systems (CISIS'18) & 9th International Conference on European Transnational Education (ICEUTE'18), Cham: Springer. [More Information]
Berkovsky, S., Taib, R., Koprinska, I., Wang, X., Zeng, Y., Li, J., Kleitman, S. (2019). Detecting Personality Traits Using Eye-Tracking Data. 2019 CHI Conference on Human Factors in Computing Systems (CHI 2019), New York: Association for Computing Machinery (ACM). [More Information]
Putri, G., Read, M., Koprinska, I., Ashhurst, T., King, N. (2019). Dimensionality Reduction for Clustering and Cluster Tracking of Cytometry Data. 28th International Conference on Artificial Neural Networks (ICANN 2019), Cham: Springer Nature. [More Information]
Koprinska, I., Rana, M., Rahman, A. (2019). Dynamic Ensemble Using Previous and Predicted Future Performance for Multi-step-ahead Solar Power Forecasting. 28th International Conference on Artificial Neural Networks (ICANN 2019), Cham: Springer Nature. [More Information]
Haidar, R., Koprinska, I., Jeffries, B. (2019). Feature Learning and Data Compression of Biosignals Using Convolutional Autoencoders for Sleep Apnea Detection. 26th International Conference on Neural Information Processing (ICONIP 2019), Cham: Springer International Publishing. [More Information]
Galicia, A., Talavera-Llames, R., Troncoso, A., Koprinska, I., Martinez-Alvarez, F. (2019). Multi-step Forecasting for Big Data Time Series Based on Ensemble Learning. Knowledge-Based Systems, 163, 830-841. [More Information]
Lin, Y., Koprinska, I., Rana, M., Troncoso, A. (2019). Patter Sequence Neural Network for Solar Power Forecasting. 26th International Conference on Neural Information Processing (ICONIP 2019), Cham: Springer International Publishing. [More Information]
Rendell, K., Koprinska, I., Kyme, A., Ebker-White, A., Dinh, M. (2019). The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision-making. EMA - Emergency Medicine Australasia, 31(3), 429-435. [More Information]

show 8 more

2018

McBroom, J., Yacef, K., Koprinska, I., Curran, J. (2018). A data-driven method for helping teachers improve feedback in computer programming automated tutors. 19th International Conference on Artificial Intelligence in Education (AIED 2018), Cham: Springer. [More Information]
Koprinska, I., Wu, D., Wang, Z. (2018). Convolutional Neural Networks for Energy Time Series Forecasting. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Haidar, R., McCloskey, S., Koprinska, I., Jeffries, B. (2018). Convolutional Neural Networks on Multiple Respiratory Channels to Detect Hypopnea and Obstructive Apnea Events. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
McCloskey, S., Haidar, R., Koprinska, I., Jeffries, B. (2018). Detecting hypopnea and obstructive apnea events using convolutional neural networks on wavelet spectrograms of nasal airflow. 22nd Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2018), Cham: Springer. [More Information]
Alzate, C., Monreale, A., Assem, H., Bifet, A., Buda, T., Caglayan, B., Drury, B., García-Martín, E., Gavaldà, R., Koprinska, I., et al (2018). ECML PKDD 2018 Workshops. ECML PKDD: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Cham: Springer Link. [More Information]
Wellard-Cole, L., Jung, J., Kay, J., Rangan, A., Chapman, K., Watson, W., Hughes, C., Ni Mhurchu, C., Bauman, A., Gemming, L., Yacef, K., Koprinska, I., Allman-Farinelli, M. (2018). Examining the Frequency and Contribution of Foods Eaten Away From Home in the Diets of 18- to 30-Year-Old Australians Using Smartphone Dietary Assessment (MYMeals): Protocol for a Cross-Sectional Study. Journal of Medical Internet Research, 7(1), 1-13. [More Information]
Wang, Z., Koprinska, I. (2018). Solar power forecasting using dynamic meta-learning ensemble of neural networks. 27th International Conference on Artificial Neural Networks (ICANN 2018), Cham: Springer Nature. [More Information]
Wang, Z., Koprinska, I., Troncoso, A., Martinez-Alvarez, F. (2018). Static and Dynamic Ensembles of Neural Networks for Solar Power Forecasting. 2018 IEEE International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

show 5 more

2017

Chow, S., Yacef, K., Koprinska, I., Curran, J. (2017). Automated data-driven hints for computer programming students. 25th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2017), New York: Association for Computing Machinery (ACM). [More Information]
Luo, L., Liu, W., Koprinska, I., Chen, F. (2017). DAAR: A discrimination-aware association rule classifier for decision support. 17th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2015), Cham: Springer. [More Information]
Al-Ani, A., Koprinska, I., Naik, G. (2017). Dynamically identifying relevant EEG channels by utilizing channels classification behaviour. Expert Systems with Applications, 83, 273-282. [More Information]
Driessens, K., Santos, O., Yacef, K., Koprinska, I., Smirnov, E., Zaiane, O. (2017). EdRecSys 2017 workshop organizers' welcome. #ISOJ (International Symposium on Online Journalism), , 125-126. [More Information]
Luo, L., Li, B., Berkovsky, S., Koprinska, I., Chen, F. (2017). Online Engagement for a Healthier You: A Case Study of Web-based Supermarket Health Program. 26th International World Wide Web Conference (WWW 2017), Perth, WA: Association for Computing Machinery (ACM). [More Information]
Haidar, R., Koprinska, I., Jeffries, B. (2017). Sleep Apnea Event Detection from Nasal Airflow Using Convolutional Neural Networks. The 24th International Conference On Neural Information Processing (ICONIP 2017) (proceedings part V), Cham: Springer. [More Information]
Wang, Z., Koprinska, I., Rana, M. (2017). Solar power forecasting using pattern sequences. 26th International Conference on Artificial Neural Networks, ICANN 2017, Cham: Springer. [More Information]
Wang, Z., Koprinska, I., Rana, M. (2017). Solar Power Prediction Using Weather Type Pair Patterns. The International Joint Conference on Neural Networks (IJCNN 2017), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Wang, Z., Koprinska, I. (2017). Solar Power Prediction with Data Source Weighted Nearest Neighbors. The International Joint Conference on Neural Networks (IJCNN 2017), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Luo, L., Li, B., Koprinska, I., Berkovsky, S., Chen, F. (2017). Tracking the evolution of customer purchase behavior segmentation via a fragmentation-coagulation process. 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), Melbourne: International Joint Conferences on Artificial Intelligence. [More Information]

show 7 more

2016

Al-Ani, A., Koprinska, I., Naik, G., Khushaba, R. (2016). A Dynamic Channel Selection Algorithm for the Classification of EEG and EMG data. 2016 International Joint Conference on Neural Networks (IJCNN 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Wang, Z., Koprinska, I., Rana, M. (2016). Clustering Based Methods for Solar Power Forecasting. 2016 International Joint Conference on Neural Networks (IJCNN 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Luo, L., Li, B., Koprinska, I., Berkonsky, S., Chen, F. (2016). Discovering Temporal Purchase Patterns with Different Responses to Promotions. 25th ACM International Conference on Information and Knowledge Management (CIKM 2016), New York: Association for Computing Machinery (ACM). [More Information]
McBroom, J., Jeffries, B., Koprinska, I., Yacef, K. (2016). Exploring and following students' strategies when completing their weekly tasks. The 9th International Conference on Educational Data Mining (EDM 2016), Raleigh: International Educational Data Mining Society.
Rana, M., Koprinska, I., Troncoso, A., Agelidis, V. (2016). Extended Weighted Nearest Neighbor for Electricity Load Forecasting. 25th International Conference on Artificial Neural Networks and Machine Learning (ICANN 2016), Cham, Switzerland: Springer International Publishing Switzerland. [More Information]
Rana, M., Koprinska, I. (2016). Forecasting electricity load with advanced wavelet neural networks. Neurocomputing, 182, 118-132. [More Information]
Gramoli, V., Charleston, M., Jeffries, B., Koprinska, I., McGrane, M., Radu, A., Viglas, A., Yacef, K. (2016). Mining autograding data in Computer Science Education. Eighteenth Australasian Computing Education Conference (ACE 2016), New York: Association for Computing Machinery (ACM). [More Information]
McBroom, J., Jeffries, B., Koprinska, I., Yacef, K. (2016). Mining behaviors of students in autograding submission system logs. The 9th International Conference on Educational Data Mining (EDM 2016), Raleigh: International Educational Data Mining Society.
Rana, M., Koprinska, I. (2016). Neural Network Ensemble Based Approach for 2D-Interval Prediction of Solar Photovoltaic Power. Energies, 9(10), 1-17. [More Information]
Rana, M., Koprinska, I., Agelidis, V. (2016). Solar Power Forecasting Using Weather Type Clustering and Ensembles of Neural Networks. 2016 International Joint Conference on Neural Networks (IJCNN 2016), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Rana, M., Koprinska, I., Agelidis, V. (2016). Univariate and multivariate methods for very short-term solar photovoltaic power forecasting. Energy Conversion and Management, 121, 380-390. [More Information]
Luo, L., Li, B., Berkovsky, S., Koprinska, I., Chen, F. (2016). Who will be Affected by Supermarket Health Programs? Tracking Customer Behavior Changes via Preference Modeling. The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2016), Switzerland: Springer. [More Information]

show 9 more

2015

Rana, M., Koprinska, I., Agelidis, V. (2015). 2D-interval forecasts for solar power production. Solar Energy, 122, 191-203. [More Information]
Gupta, J., Koprinska, I., Patrick, J. (2015). Automated Classification of Clinical Incident Types. 23rd Australian National Health Informatics Conference (HIC 2015), Amsterdam: IOS Press. [More Information]
Koprinska, I., Rana, M., Agelidis, V. (2015). Correlation and instance based feature selection for electricity load forecasting. Knowledge-Based Systems, 82, 29-40. [More Information]
Luo, L., Liu, W., Koprinska, I., Chen, F. (2015). Discovering Causal Structures from Time Series Data via Enhanced Granger Causality. 28th Australasian Joint Conference on Artificial Intelligence (AI 2015), Cham: Springer. [More Information]
Luo, L., Liu, W., Koprinska, I., Chen, F. (2015). Discrimination-Aware Association Rule Mining for Unbiased Data Analytics. 17th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2015), Cham: Springer. [More Information]
Luo, L., Koprinska, I., Liu, W. (2015). Discrimination-Aware Classifiers for Student Performance Prediction. The 8th International Conference on Educational Data Mining (EDM), Madrid: International Educational Data Mining Society.
Rana, M., Koprinska, I., Khosravi, A. (2015). Feature Selection for Interval Forecasting of Electricity Demand Time Series Data. In Petia Koprinkova-Hristova, Valeri Mladenov, Nikola K. Kasabov (Eds.), Artificial Neural Networks: Methods and Applications in Bio-/Neuroinformatics, (pp. 445-462). Heidelberg: Springer Science+Business Media. [More Information]
Rana, M., Koprinska, I., Agelidis, V. (2015). Forecasting Solar Power Generated by Grid Connected PV System Using Ensembles of Neural Networks. 2015 International Joint Conference on Neural Networks (IJCNN 2015), Killarney: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Colombo, T., Koprinska, I., Panella, M. (2015). Maximum Length Weighted Nearest Neighbor approach for electricity load forecasting. 2015 International Joint Conference on Neural Networks (IJCNN 2015), Killarney: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Koprinska, I., Yacef, K. (2015). People to People Reciprocal Reccomenders. In F. Ricci, L. Rokach, B. Shapira (Eds.), Recommender Systems Handbook, (pp. 545-567). New York: Springer Science+Business Media. [More Information]
Koprinska, I., Stretton, J., Yacef, K. (2015). Predicting student performance from multiple data sources. 17th International Conference on Artificial Intelligence in Education (AIED 2015), : Springer Verlag. [More Information]
Koprinska, I., Stretton, J., Yacef, K. (2015). Students at Risk: Detection and Remediation. The 8th International Conference on Educational Data Mining (EDM), Madrid: International Educational Data Mining Society.

show 9 more

2014

Rana, M., Koprinska, I., Troncoso, A. (2014). Forecasting hourly electricity load profile using neural networks. The Annual International Joint Conference on Neural Networks (IJCNN 2014), Piscataway, USA: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

2013

O'Keefe, T., Curran, J., Ashwell, P., Koprinska, I. (2013). An Annotated Corpus of Quoted Opinions in News Articles. 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Stroudsburg, PA USA: Association for Computational Linguistics (ACL).
Pareti, S., O'Keefe, T., Konstas, I., Curran, J., Koprinska, I. (2013). Automatically Detecting and Attributing Indirect Quotations. 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), Seattle, USA: Association for Computational Linguistics (ACL).
Xu, M., Berkovsky, S., Ardon, S., Triukose, S., Mahanti, A., Koprinska, I. (2013). Catch-up TV Recommendations: Show Old Favourites and Find New Ones. 7th ACM Conference on Recommender Systems (RecSys 2013), New York: Association for Computing Machinery (ACM). [More Information]
Koprinska, I., Rana, M., Troncoso, A., Martinez-Alvarez, F. (2013). Combining Pattern Sequence Similarity with Neural Networks for Forecasting Electricity Demand Time Series. 2013 International Joint Conference on Neural Networks (IJCNN 2013), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Rana, M., Koprinska, I., Khosravi, A. (2013). Feature Selection for Neural Network-Based Interval Forecasting of Electricity Demand Data. 23rd International Conference on Artificial Neural Networks (ICANN 2013), Berlin: Springer-Verlag. [More Information]
Rana, M., Koprinska, I., Khosravi, A., Agelidis, V. (2013). Prediction intervals for electricity load forecasting using neural networks. 2013 International Joint Conference on Neural Networks (IJCNN 2013), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Pizzato, L., Rej, T., Akehurst, J., Koprinska, I., Yacef, K., Kay, J. (2013). Recommending people to people: the nature of reciprocal recommenders with a case study in online dating. User Modeling and User-Adapted Interaction, 23(5), 447-488. [More Information]
Rana, M., Koprinska, I. (2013). Wavelet Neural Networks for Electricity Load Forecasting - Dealing with Border Distortion and Shift Invariance. 23rd International Conference on Artificial Neural Networks (ICANN 2013), Berlin: Springer-Verlag. [More Information]

show 5 more

2012

O'Keefe, T., Pareti, S., Curran, J., Koprinska, I., Honnibal, M. (2012). A Sequence Labelling Approach to Quote Attribution. Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (emnlp-conll 2012), Stroudsburg, PA: Association for Computational Linguistics (ACL).
Rana, M., Koprinska, I. (2012). Electricity Load Forecasting Using Non-decimated Wavelet Prediction Methods With Two-Stage Feature Selection. 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Koprinska, I., Rana, M., Agelidis, V. (2012). Electricity Load Forecasting: A Weekday-Based Approach. 22nd ICANN International Conference on Artificial Neural Networks, Berlin, Germany: Springer. [More Information]
Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L., Kay, J., Rej, T. (2012). Explicit and Implicit User Preferences in Online Dating. 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011, Heidelberg, Germany: Springer. [More Information]
Rana, M., Koprinska, I., Agelidis, V. (2012). Feature Selection for Electricity Load Prediction. The 19th International Conference on Neural Information Processing (ICONIP2012), Heidelberg: Springer-Verlag. [More Information]
Kotillova, A., Koprinska, I., Rana, M. (2012). Statistical and Machine Learning Methods for Electricity Demand Prediction. The 19th International Conference on Neural Information Processing (ICONIP2012), Heidelberg: Springer-Verlag. [More Information]
Pizzato, L., Akehurst, J., Silvestrini, C., Yacef, K., Koprinska, I., Kay, J. (2012). The effect of suspicious profiles on people recommenders. Lecture Notes in Computer Science (LNCS), 7379, 225-236. [More Information]
Xu, M., Berkovsky, S., Koprinska, I., Ardon, S., Yacef, K. (2012). Time Dependency in TV Viewer Clustering. PATCH: 4th International Workshop on Personal Access to Cultural Heritage at the 20th Conference on User Modeling, Adaptation, and Personalization (UMAP), online: CEUR-WS.

show 5 more

2011

Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L., Kay, J., Rej, T. (2011). CCR - A Content-Collaborative Reciprocal Recommender for Online Dating. 22nd International Joint Conference on Artificial Intelligence, Menlo Park, California, USA: AAAI Press.
Hawson, L., Koprinska, I., McLean, A., McGreevy, P. (2011). Deciphering the cues from riders' legs. 7th International Equitation Science Conference, the Netherlands: Wageningen Academic Publishers.
Kay, J., Koprinska, I., Yacef, K. (2011). Educational Data Mining to Support Group Work in Software Development Projects. In Cristobal Romero, Sebastian Ventura, Mykola Pechenizkiy, Ryan S J d Baker (Eds.), Handbook of Educational Data Mining, (pp. 173-185). USA: CRC Press. [More Information]
Pizzato, L., Rej, T., Yacef, K., Koprinska, I., Kay, J. (2011). Finding someone you will like and who won't reject you. 19th International Conference on User Modeling, Adaptation and Personalization, UMAP 2011, Berlin, Heidelberg: Springer. [More Information]
Koprinska, I. (2011). Mining Assessment and Teaching Evaluation Data of Regular and Advanced Stream Students. 4th International Conference on Educational Data Mining (EDM 2011), Eindhoven, Netherlands: Technische Universiteit Eindhoven.
Koprinska, I., Rana, M., Agelidis, V. (2011). Yearly and seasonal models for electricity load forecasting. 2011 International Joint Conference on Neural Networks (IJCNN 2011), Piscataway: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

show 3 more

2010

Sood, R., Koprinska, I., Agelidis, V. (2010). Electricity Load Forecasting Based on Autocorrelation Analysis. 2010 IEEE Congress on Evolutionary Computation (IEEE-CEC) - WCCI 2010, USA: Institute of Electrical and Electronics Engineers (IEEE). [More Information]
Koprinska, I. (2010). Feature Selection for Brain-Computer Interfaces. In Theeramunkong, Nattee, Adeodato, Chawla, Christen, Lenca, Poon, Williams (Eds.), New Frontiers in Applied Data Mining: PAKDD 2009 International Workshops - Lecture Notes in Artificial Intelligence - LNAI Volume 5669, (pp. 106-117). Berlin Heidelberg: Springer. [More Information]
Pizzato, L., Chung, T., Rej, T., Koprinska, I., Yacef, K., Kay, J. (2010). Learning User Preferences in Online Dating. European conference on machine learning and principles and practice of knowledge discovery in databases (2012 ECML PKDD), Spain: ECML PKDD.
Pizzato, L., Rej, T., Chung, T., Koprinska, I., Yacef, K., Kay, J. (2010). Reciprocal Recommender System for Online Dating. Fourth ACM Conference on Recommender Systems (RecSys10), USA: Association for Computing Machinery (ACM). [More Information]
Pizzato, L., Rej, T., Chung, T., Yacef, K., Koprinska, I., Kay, J. (2010). Reciprocal Recommenders. 8th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP 2010), USA: Intelligent Techniques for Web Personalization and Recommender Systems.
Pizzato, L., Rej, T., Chung, T., Koprinska, I., Kay, J. (2010). RECON: A Reciprocal Recommender for Online Dating. Fourth ACM Conference on Recommender Systems (RecSys10), USA: Association for Computing Machinery (ACM). [More Information]
Koprinska, I., Sood, R., Agelidis, V. (2010). Variable Selection for Five-Minute Ahead Electricity Load Forecasting. 20th International Conference on Pattern Recognition (ICPR 2010), USA: Institute of Electrical and Electronics Engineers (IEEE). [More Information]

show 4 more

2009

Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaiane, O. (2009). Clustering and Sequential Pattern Mining of Online Collaborative Learning Data. IEEE Transactions On Knowledge And Data Engineering, 21(6), 759-772. [More Information]
Koprinska, I. (2009). Comparison of Feature Selection Methods for Classification of Brain-Computer Interface Data. Workshop on Advances and Issues in Biomedical Data Mining AIBDM 2009 - In association with the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'09), Thailand: Printing House of Thammasat University, Rangsit Campus.
O'Keefe, T., Koprinska, I. (2009). Feature Selection and Weighting Methods in Sentiment Analysis. 14th Australasian Document Computing Symposium, Sydney, Australia: School of Information Technologies, University of Sydney.
Setiawan, A., Koprinska, I., Agelidis, V. (2009). Very Short-Term Electricity Load Demand Forecasting Using Support Vector Regression. International Joint Conference on Neural Networks IJCNN 2009, United States: Documentation LLC. [More Information]

show 1 more

2008

Al-Zoubi, O., Koprinska, I., Calvo, R. (2008). Classification of Brain-Computer Interface Data. The Seventh Australasian Data Mining Conference (AusDM 2008), Sydney NSW, Australia: Australian Computer Society.
Chan, J., Koprinska, I., Poon, J. (2008). Semi-supervised classification using bridging. International Journal on Artificial Intelligence Tools, 17(3), 415-431. [More Information]

2007

Chan, J., Poon, J., Koprinska, I. (2007). Enhancing the Performance of Semi-Supervised Classification Algorithms with Bridging. The Twentieth International Florida Artificial Intelligence Research Society Conference, Menlo Park, California: AAAI Press.
Perera, D., Kay, J., Yacef, K., Koprinska, I. (2007). Mining learners' traces from an online collaboration tool. Workshop on Educational Data Mining 2007, online: International Working Group on Educational Data Mining.

2006

Cummins, D., Yacef, K., Koprinska, I. (2006). A Sequence Based Recommender System for Learning Resources. ADCS 2006 11th Australasian Document Computing Symposium, Queensland: Faculty of Information Technology, Queensland University of Technology.
Feger, F., Koprinska, I. (2006). Co-training Using RBF Nets and Different Feature Splits. 2006 IEEE World Congress on Computational Intelligence - A Joint Conference of the Int Joint Conf on Neural Networks (IJCNN), Fuzzy Systems (FUZZ-IEEE), and Evolutionary Computation (CEC), USA: Institute of Electrical and Electronics Engineers (IEEE).
Koprinska, I., Deng, D., Felix, F. (2006). Image Classification Using Labelled And Unlabelled Data. 14th European Signal Processing Conference (EUSIPCO 2006), Europe: EURASIP European Association for Signal, Speech and Image Processing.
Koprinska, I., Poon, J., Clark, J., Chan, J. (2006). Learning to classify e-mail. Information Sciences, Online Article, 1-21. [More Information]

show 1 more

2005

Ler, D., Koprinska, I., Chawla, S. (2005). A Hill-climbing Landmarker Generation Algorithm Based on Efficiency and Correlativity Criteria. The Eighteenth International Florida Artificial Intelligence Research Society Conference - FLAIRS 2005, USA: AAAI Press.
Ler, D., Koprinska, I., Chawla, S. (2005). Comparisons between Heuristics Based on Correlativity and Efficiency for Landmarker Generation. Fourth International Conference on Hybrid Intelligent Systems - HIS 2004, Los Alamitos, California, USA: Institute of Electrical and Electronics Engineers (IEEE).
Saberi, M., Carrato, S., Koprinska, I., Clark, J. (2005). Estimation of the Hierachical Structures of a Video Sequence Using MPEG-7 Descriptors and GCS. 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, Germany: Springer.
Ler, D., Koprinska, I., Chawla, S. (2005). Utilising Regression-based Landmarkers within a Meta-learning Framework for Algorithm Selection. W10 - Workshop on Meta-Learning : associated with the 22nd International Conference on Machine Learning (ICML 2005), Germany: International Machine Learning Society.

show 1 more

2004

Ler, D., Koprinska, I., Chawla, S. (2004). A Landmarker Selection Algorithm Based On Correlation And Efficiency Criteria. 17th Australian Joint Conference on Artificial Intelligence, Berlin: Springer.
Ler, D., Koprinska, I., Chawla, S. (2004). A New Landmarker Generation Algorithm Based On Correlativity. The 2004 International Conference on Machine Learning and Applications (ICMLA'04), USA: Institute of Electrical and Electronics Engineers (IEEE).
Chan, J., Koprinska, I., Poon, J. (2004). Co-Training On Textual Documents With A Single Natural Feature Set. The Ninth Australasian Document Computing Symposium (ADCS 2004), Victoria, Australia: Department of Computer Science and Software Engineering, University of Melbourne.
Chan, J., Koprinska, I., Poon, J. (2004). Co-Training With A Single Natural Feature Set Applied To Email Classification. 2004 IEEE/WIC ACM International Conference on Web Intelligence, Los Alamitos, CA, USA: Institute of Electrical and Electronics Engineers (IEEE).
Crawford, E., Koprinska, I., Patrick, J. (2004). Phrases And Feature Selection In E-Mail Classification. The Ninth Australasian Document Computing Symposium (ADCS 2004), Victoria, Australia: Department of Computer Science and Software Engineering, University of Melbourne.
Koprinska, I., Clark, J. (2004). Video Summarization And Browsing Using Growing Cell Structures. 2004 IEEE International Joint Conference on Neural Networks. Institute of Electrical and Electronics Engineers (IEEE).
Koprinska, I., Clark, J., Carrato, S. (2004). Videogcs - A Clustering-Based System For Video Summarizatiion And Browsing. 6th COST 276 Workshop on Information and Knowledge Management for Integrated Media Communication, Greece: Informatics and Telematics Institute, Centre for Research and Technology.

show 4 more

2003

Clark, J., Koprinska, I., Poon, J. (2003). A neural network based approach to automatee e-mail classification. 2003 IEEE/WIC Intenational Conference on Web Intelligence, United States: Institute of Electrical and Electronics Engineers (IEEE).
Verhein, F., Kay, J., Koprinska, I., McCreath, E. (2003). Classifying public announcements for user communities. Eighth Australasian Document Computing Symposium, Canberra: CSIRO ICT Centre.
Koprinska, I., Trieu, F., Poon, J., Clark, J. (2003). E-mail classification by decision forests. Eighth Australasian Document Computing Symposium, Canberra: CSIRO ICT Centre.
Mak, H., Koprinska, I., Poon, J. (2003). INTIMATE: A web-based movie recommender using text categorization. 2003 IEEE/WIC Intenational Conference on Web Intelligence, United States: Institute of Electrical and Electronics Engineers (IEEE).
Clark, J., Koprinska, I., Poon, J. (2003). Linger - a smart personal assistant for e-mail classification. 2003 IEEE/WIC Intenational Conference on Web Intelligence, United States: Institute of Electrical and Electronics Engineers (IEEE).
Koprinska, I., Carrato, S. (2003). Segmentation techniques for video sequences in the domain of MPEG-compressed data. In Tasic, Najim, Ansorge (Eds.), Intelligent Integrated Media Communication Techniques, (pp. 89-116). Netherlands: Springer.

show 3 more

2002

Crawford, E., Koprinska, I., Patrick, J. (2002). A Multi-Learner Approach to E-mail Classification. ADCS '02 The Seventh Australasian Document Computing Symposium, Sydney, Australia: School of Information Technologies, University of Sydney.
Ceguerra, A., Koprinska, I. (2002). Automatic Fingerprint Verification Using Neural Networks. Artificial Neural Networks ICANN 2002 International Conference, Germany: Springer.
Jackson, K., Koprinska, I. (2002). DNA Microarray Data Clustering Using Growing Self Organizing Networks. 9th International Conference on Neural Information Processing (ICONIP''02) 4th Asia Pacific Conference on Simulated Evolution and Learning (SEAL''02) 1st International Conference on Fuzzy Systems and Knowledge Discovery (FSKD''02), Singapore: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
Koprinska, I., Carrato, S. (2002). Hybrid Rule-Based/Neural Approach for Segmentation of MPEG Compressed Video. Multimedia Tools and Applications, 18(3), 187-212. [More Information]
Ceguerra, A., Koprinska, I. (2002). Integrating Local and Global Features in Automatic Fingerprint Verification. 16th International Conference on Pattern Recognition, Los Alamitos, California: Institute of Electrical and Electronics Engineers (IEEE).
Koprinska, I., Carrato, S. (2002). Temporal Video Segmentation: A Survey. Signal Processing: Image Communication, 16(5), 477-500.

show 3 more

Selected Grants

2020

ExploreCSR (2020), Han S, Fekete A, Koprinska I, Kay J, Poon J, Yacef K, Google LLC/Research Support

2018

Health Data Mining, Koprinska I, DVC Research/Thompson Fellowships

2017

Understanding and facilitating learning in emerging knowledge co-creation spaces, Markauskaite L, Reimann P, Goodyear P, Pardo A, Kay J, Shaw T, Poronnik P, Bobis J, Way J, Simpson A, Sutherland L, Curwood J, Jacobson M, Bartimote K, Yacef K, Koprinska I, Calvo R, DVC Research/Sydney Research Excellence Initiative 2020 (SREI)
Mapping dynamic immunity: next generation computational approaches to track the evolution of immune responses in West Nile virus & Zika virus encephalitis, Ashhurst T, Read M, King N, Roehm U, Koprinska I, Scalzo R, Marie Bashir Institute for Infectious Diseases and Biosecurity/Seed Funding Grants

2016

Quantifying intake of food prepared outside home during emerging adulthood, Allman-Farinelli M, Kay J, Chapman K, Hughes C, Watson W, Rangan A, Yacef K, Koprinska I, Mhurchu C, Bauman A, Australian Research Council (ARC)/Linkage Projects (LP)

2013

Enhancing feedback in online assessment of computer programming, Jeffries B, Koprinska I, Viglas A, Gramoli V, DVC Education/Large Educational Innovation Grant

2008

Personalisation, Curran J, Greenhalgh A, Nayak R, Wobcke W, Xu Y, Bain M, Mahidadia A, Compton P, Li Y, Spink A, Kay J, Koprinska I, Yacef K, Smart Services Cooperative Research Centre/Research Support

2006

Promoting interdisiplinary learning communities using interactive plasma displays, Calvo R, Koprinska I, Vande Moere A, Yacef K, University of Sydney/TIES Small Grant Scheme
Sequential pattern analysis in learning traces, Kay J, Koprinska I, Yacef K, University of Sydney/Bridging Support
Glass box data-mining to support scalable learner-centred systems, Kay J, Koprinska I, Yacef K, University of Sydney/Bridging Support

2005

Glass box data-mining to support scalable learner-centred systems, Kay J, Koprinska I, Yacef K, University of Sydney/Bridging Support
Talented Students Projects Computer Lab and Electronic Response Lecture Theatre, Kay J, Koprinska I, Kummerfeld R, Ryan G, Yacef K, University of Sydney/Teaching Innovation Grant - Faculty of Science

2004

Bridging the Gap: Smart Support for the Intergenerational Distributed Family, Kay J, Koprinska I, Kummerfeld R, Poon J, Yacef K, Smart Services Cooperative Research Centre./Cooperative Research Centres

2002

Video Segmentation and Summarization Using Neural Networks, Koprinska I, DVC Research/Research and Development Scheme: Newly Appointed Staff (NAS)

2001

Knowledge acquisition and Machine Learning, Smart Personal Assistant Project 01, Davis J, Kay J, Koprinska I, Poon J, Takatsuka M, Tobias J, Yacef K, Smart Services Cooperative Research Centre./Cooperative Research Centres
Machine Learning for SPA, Greenhalgh A, Koprinska I, Poon J, Smart Services Cooperative Research Centre./BLO Project

2000

Natural Adaptive User Interface Prototype, NAUI-Project 01, Davis J, Kay J, Koprinska I, Poon J, Takatsuka M, Tobias J, Yacef K, Smart Services Cooperative Research Centre./BLO Project

Page not found

Professor Irena Koprinska

Publications

By Type

By Year

Selected Grants

2020

2018

2017

2016

2013

2008

2006

2005

2004

2002

2001

2000