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Dr Teo Susnjak staff profile picture

Contact details +6492136146

Dr Teo Susnjak PhD

Senior Lecturer

Doctoral Supervisor
School of Mathematical and Computational Sciences

Prior to his academic career, Teo was a touring Tennis Professional, representing New Zealand in the Davis Cup.

Following his sporting career, he pursued studies in Computer Science, being awarded a PhD for his research focusing in machine learning. He worked in industry as a machine learning analyst and still continues to research applied and practical aspects of machine learning, and more broadly artificial intelligence.

His recent interest in the emerging field of Data Science has expended his research to include Big Data technologies for data processing, wrangling and visualisation as well as process mining. 

Roles and Responsibilities

  • Data Science Subject Lead
  • Master of Analytics Programme Coordinator

Teo is a researcher in Artificial Intelligence with recent work focusing on Large Language models and Generative AI. He has a long history of working with machine learning and applying to a wide range of applied contexts.

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Professional

Contact details

  • Ph: +64 9 414 0800 ext 43146
    Location: 3.25, Mathematical Sciences Building
    Campus: Albany

Qualifications

  • Doctor of Philosophy - 黑料网 (2013)

Certifications and Registrations

  • Licence, Supervisor, 黑料网

Prizes and Awards

  • Awarded the NZ Ministry of Science and Innovation Internship to investigate the feasability of implementing machine learning algorithms connected with my doctoral research, into the software owned by a NZ based company Compac Sorting Ltd. - NZ Ministry of Science and Innovation (2012)
  • Best Conference Paper Award for: Susnjak, T., Barczak, A., & Reyes, N. (2013). A Decomposition Machine-learning Strategy for Automated Fruit Grading. In Proceedings of the World Congress on Engineering and Computer Science (Vol. 2). - Organising committee for the World Congress on Engineering and Computer Science (2013)
  • Awarded the prize and the inclusion onto the "Dean's List of Outstanding Theses" for the PhD Thesis titled: "Efficient boosted ensemble-based machine learning in the context of cascaded frameworks" - 黑料网 (2013)

Research Expertise

Research Interests

Data science, machine learning, artificial intelligence, large language models and generative AI.

Area of Expertise

Field of research codes
Artificial Intelligence and Image Processing (080100): Decision Support and Group Support Systems (080605): Information And Computing Sciences (080000): Information Systems (080600): Natural Language Processing (080107): Pattern Recognition and Data Mining (080109)

Keywords

Artificial Intelligence

Machine Learning

Large Language Models

Generative AI

Research Outputs

Journal

Han, B., Susnjak, T., & Mathrani, A. (2024). Automating Systematic Literature Reviews with Retrieval-Augmented Generation: A Comprehensive Overview. Applied Sciences (Switzerland). 14(19)
[Journal article]Authored by: Han, B., Mathrani, A., Susnjak, T.
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McIntosh, TR., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., . . . Halgamuge, MN. (2024). From COBIT to ISO 42001: Evaluating cybersecurity frameworks for opportunities, risks, and regulatory compliance in commercializing large language models. Computers and Security. 144
[Journal article]Authored by: Liu, T., Susnjak, T.
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Susnjak, T., & McIntosh, TR. (2024). ChatGPT: The End of Online Exam Integrity?. Education Sciences. 14(6)
[Journal article]Authored by: Susnjak, T.
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McIntosh, TR., Liu, T., Susnjak, T., Watters, P., & Halgamuge, MN. (2024). A Reasoning and Value Alignment Test to Assess Advanced GPT Reasoning. ACM Transactions on Interactive Intelligent Systems. 14(3)
[Journal article]Authored by: Liu, T., Susnjak, T.
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McIntosh, TR., Susnjak, T., Liu, T., Watters, P., Ng, A., & Halgamuge, MN. (2024). A Game-Theoretic Approach to Containing Artificial General Intelligence: Insights from Highly Autonomous Aggressive Malware. IEEE Transactions on Artificial Intelligence.
[Journal article]Authored by: Liu, T., Susnjak, T.
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Mcintosh, TR., Susnjak, T., Liu, T., Watters, P., & Halgamuge, MN. (2024). The Inadequacy of Reinforcement Learning From Human Feedback - Radicalizing Large Language Models via Semantic Vulnerabilities. IEEE Transactions on Cognitive and Developmental Systems. 16(4), 1561-1574
[Journal article]Authored by: Liu, T., Susnjak, T.
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McIntosh, TR., Liu, T., Susnjak, T., Watters, P., Ng, A., & Halgamuge, MN. (2024). A Culturally Sensitive Test to Evaluate Nuanced GPT Hallucination. IEEE Transactions on Artificial Intelligence. 5(6), 2739-2751
[Journal article]Authored by: Liu, T., Susnjak, T.
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Susnjak, T. (2024). Beyond Predictive Learning Analytics Modelling and onto Explainable Artificial Intelligence with Prescriptive Analytics and ChatGPT. International Journal of Artificial Intelligence in Education. 34(2), 452-482
[Journal article]Authored by: Susnjak, T.
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12
Ramaswami, G., Susnjak, T., & Mathrani, A. (2023). Effectiveness of a Learning Analytics Dashboard for Increasing Student Engagement Levels. Journal of Learning Analytics. 10(3), 115-134
[Journal article]Authored by: Mathrani, A., Susnjak, T.
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Bunker, R., Yeung, C., Susnjak, T., Espie, C., & Fujii, K. (2023). A comparative evaluation of Elo ratings- and machine learning-based methods for tennis match result prediction. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology.
[Journal article]Authored by: Susnjak, T.
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McIntosh, T., Liu, T., Susnjak, T., Alavizadeh, H., Ng, A., Nowrozy, R., . . . Watters, P. (2023). Harnessing GPT-4 for generation of cybersecurity GRC policies: A focus on ransomware attack mitigation. Computers and Security. 134
[Journal article]Authored by: Liu, T., Susnjak, T.
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Brenner, M., Reyes, NH., Susnjak, T., & Barczak, ALC. (2023). RGB-D and Thermal Sensor Fusion: A Systematic Literature Review. IEEE Access. 11, 82410-82442
[Journal article]Authored by: Reyes, N., Susnjak, T.
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11
Susnjak, T., & Maddigan, P. (2023). Forecasting patient demand at urgent care clinics using explainable machine learning. CAAI Transactions on Intelligence Technology. 8(3), 712-733
[Journal article]Authored by: Susnjak, T.
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Maddigan, P., & Susnjak, T. (2023). Chat2VIS: Generating Data Visualizations via Natural Language Using ChatGPT, Codex and GPT-3 Large Language Models. IEEE Access. 11, 45181-45193
[Journal article]Authored by: Susnjak, T.
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Susnjak, T., & Maddigan, P. (2023). Forecasting patient flows with pandemic induced concept drift using explainable machine learning. EPJ Data Science. 12(1)
[Journal article]Authored by: Susnjak, T.
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Wanniarachchi, VU., Scogings, C., Susnjak, T., & Mathrani, A. (2023). Hate Speech Patterns in Social Media: A Methodological Framework and Fat Stigma Investigation Incorporating Sentiment Analysis, Topic Modelling and Discourse Analysis. Australasian Journal of Information Systems. 27
[Journal article]Authored by: Mathrani, A., Scogings, C., Susnjak, T.
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Ramaswami, G., Susnjak, T., Mathrani, A., & Umer, R. (2023). Use of Predictive Analytics within Learning Analytics Dashboards: A Review of Case Studies. Technology, Knowledge and Learning. 28(3), 959-980
[Journal article]Authored by: Mathrani, A., Susnjak, T.
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11
Umer, R., Susnjak, T., Mathrani, A., & Suriadi, L. (2023). Current stance on predictive analytics in higher education: opportunities, challenges and future directions. Interactive Learning Environments. 31(6), 3503-3528
[Journal article]Authored by: Mathrani, A., Susnjak, T.
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Ramaswami, G., Susnjak, T., & Mathrani, A. (2022). Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics. Big Data and Cognitive Computing. 6(4)
[Journal article]Authored by: Mathrani, A., Susnjak, T.
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Wanniarachchi, VU., Scogings, C., Susnjak, T., & Mathrani, A. (2022). Fat stigma and body objectification: A text analysis approach using social media content. Digital Health. 8
[Journal article]Authored by: Mathrani, A., Scogings, C., Susnjak, T.
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Ahmed, N., Barczak, ALC., Rashid, MA., & Susnjak, T. (2022). Runtime prediction of big data jobs: performance comparison of machine learning algorithms and analytical models. Journal of Big Data. 9(1)
[Journal article]Authored by: Susnjak, T.
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Wanniarachchi, VU., Mathrani, A., Susnjak, T., & Scogings, C. (2022). Methodological Aspects in Study of Fat Stigma in Social Media Contexts: A Systematic Literature Review. Applied Sciences (Switzerland). 12(10)
[Journal article]Authored by: Mathrani, A., Scogings, C., Susnjak, T.
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Umer, R., Susnjak, T., Mathrani, A., & Suriadi, S. (2022). Data quality challenges in educational process mining: building process-oriented event logs from process-unaware online learning systems. International Journal of Business Information Systems. 39(4), 569-592
[Journal article]Authored by: Mathrani, A., Susnjak, T.
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Susnjak, T., Ramaswami, GS., & Mathrani, A. (2022). Learning analytics dashboard: a tool for providing actionable insights to learners. International Journal of Educational Technology in Higher Education. 19(1)
[Journal article]Authored by: Mathrani, A., Susnjak, T.
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Ramaswami, G., Susnjak, T., & Mathrani, A. (2022). On Developing Generic Models for Predicting Student Outcomes in Educational Data Mining. Big Data and Cognitive Computing. 6(1)
[Journal article]Authored by: Mathrani, A., Susnjak, T.
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Bunker, R., & Susnjak, T. (2022). The Application of Machine Learning Techniques for Predicting Match Results in Team Sport: A Review. Journal of Artificial Intelligence Research. 73, 1285-1322
[Journal article]Authored by: Susnjak, T.
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Mathrani, A., Susnjak, T., Ramaswami, G., & Barczak, A. (2021). Perspectives on the challenges of generalizability, transparency and ethics in predictive learning analytics. Computers and Education Open. 2, Retrieved from https://www.sciencedirect.com/science/article/pii/S2666557321000318
[Journal article]Authored by: Mathrani, A., Susnjak, T.
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Ahmed, N., Barczak, ALC., Rashid, MA., & Susnjak, T. (2021). An enhanced parallelisation model for performance prediction of apache spark on a multinode hadoop cluster. Big Data and Cognitive Computing. 5(4)
[Journal article]Authored by: Susnjak, T.
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Ahmed, N., Barczak, ALC., Rashid, MA., & Susnjak, T. (2021). A parallelization model for performance characterization of Spark Big Data jobs on Hadoop clusters. Journal of Big Data. 8(1)
[Journal article]Authored by: Susnjak, T.
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Ahmed, N., Barczak, ALC., Susnjak, T., & Rashid, MA. (2020). A comprehensive performance analysis of Apache Hadoop and Apache Spark for large scale data sets using HiBench. Journal of Big Data. 7(1)
[Journal article]Authored by: Susnjak, T.
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Wanniarachchi, VU., Mathrani, A., Susnjak, T., & Scogings, C. (2020). A systematic literature review: What is the current stance towards weight stigmatization in social media platforms?. International Journal of Human Computer Studies. 135
[Journal article]Authored by: Mathrani, A., Scogings, C., Susnjak, T.
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McIntosh, T., Jang-Jaccard, J., Watters, P., & Susnjak, T. (2019). Masquerade Attacks Against Security Software Exclusion Lists. Australian Journal of Intelligent Information Processing Systems. 16(4), 1-8 Retrieved from http://ajiips.com.au/new/paper_page.php?volume=16&issue=4&first_page=1
[Journal article]Authored by: Susnjak, T.
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Ramaswami, G., Susnjak, T., Mathrani, A., Lim, J., & Garcia, P. (2019). Using educational data mining techniques to increase the prediction accuracy of student academic performance. Information and Learning Science. 120(7-8), 451-467
[Journal article]Authored by: Mathrani, A., Susnjak, T.
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Umer, R., Susnjak, T., Mathrani, AS., & Suriadi, S. (2017). On predicting academic performance with process mining in learning analytics. Journal of Research in Innovative Teaching & Learning. 10(2), 160-176
[Journal article]Authored by: Mathrani, A., Susnjak, T.
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Umer, R., Susnjak, T., Mathrani, A., & Suriadi, S. (2017). Prediction of Students’ Dropout in MOOC Environment. International Journal of Knowledge Engineering. 3(2), 43-47
[Journal article]Authored by: Mathrani, A., Susnjak, T.
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Suriadi, S., Susnjak, T., Ponder-Sutton, A., Watters, P., & Schumacher, CR. (2016). Using data-driven and process mining techniques for identifying and characterizing problem gamblers in New Zealand. Complex Systems Informatics and Modeling Quarterly. 9, 44-66
[Journal article]Authored by: Schumacher, C., Susnjak, T.
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Parsons, D., Susnjak, T., & Mathrani, A. (2016). Design from detail: Analyzing data from a global day of coderetreat. Information and Software Technology. 75, 39-55
[Journal article]Authored by: Mathrani, A., Susnjak, T.
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Parsons, D., Susnjak, T., & Lange, M. (2014). Influences on regression testing strategies in agile software development environments. Software Quality Journal. 22(4), 717-739
[Journal article]Authored by: Susnjak, T.
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Parsons, D., Mathrani, A., Susnjak, T., & Leist, A. (2014). Coderetreats: Reflective practice and the game of life. IEEE Software. 31(4), 58-64
[Journal article]Authored by: Mathrani, A., Susnjak, T.
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Parsons, D., Susnjak, T., & Lange, M. (2013). Influences on regression testing strategies in agile software development environments. Software Quality Journal. , 1-23
[Journal article]Authored by: Susnjak, T.
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Susnjak, T., Barczak, A., Reyes, N., & Hawick, K. (2013). Coarse-to-fine multiclass learning and classification for time-critical domains. Pattern Recognition Letters. 34(8), 884-894
[Journal article]Authored by: Reyes, N., Susnjak, T.
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Susnjak, T., Barczak, ALC., & Hawick, KA. (2012). Adaptive cascade of boosted ensembles for face detection in concept drift. Neural Computing and Applications. 21(4), 671-682
[Journal article]Authored by: Susnjak, T.
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Book

Susnjak, T. (2024). Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature. In Methods in Molecular Biology. (pp. 173 - 183).
[Chapter]Authored by: Susnjak, T.
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Garg, K., Fajardo-Yamamoto, LM., Rojas-Castro, FC., Susnjak, T., & Gilbert, L. (2024). Building a Binary Classification Machine-Learning Model: A Guide to Predicting Participation in a Lyme Disease Program at a Medical Institute. In Methods in Molecular Biology. (pp. 185 - 237).
[Chapter]Authored by: Susnjak, T.
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reyes, ., barczak, ., Susnjak, T., & Jordan, A. (2017). Fast and Smooth Replanning for Navigation in Partially Unknown Terrain. In Robot Intelligence Technology and Applications.
[Chapter]Authored by: Reyes, N., Susnjak, T.
Wang, W., Reyes, NH., Barczak, ALC., Susnjak, T., & Sincak, P. (2015). Multi-behaviour robot control using genetic network programming with fuzzy reinforcement learning. (pp. 151 - 158).
[Chapter]Authored by: Reyes, N., Susnjak, T.
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Reyes, NH., Barczak, ALC., Susnjak, T., Sincák, P., & Va拧膷ák, J. (2013). Real-time fuzzy logic-based hybrid robot path-planning strategies for a dynamic environment. In Robotics: Concepts, Methodologies, Tools, and Applications. (pp. 1545 - 1571).
[Chapter]Authored by: Reyes, N., Susnjak, T.
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Susnjak, T., & Barczak, A. (2014). On combining boosting with rule-induction for automated fruit grading. In Transactions on Engineering Technologies: Special Issue of the World Congress on Engineering and Computer Science 2013. (pp. 275 - 290).
[Chapter]Authored by: Reyes, N., Susnjak, T.
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Reyes, NH., Susnjak, T., Barczak, ALC., Sincák, P., & Va拧cák, J. (2013). Real-time fuzzy logic-based hybrid robot path-planning strategies for a dynamic environment. In Efficiency and Scalability Methods for Computational Intellect. (pp. 115 - 141).
[Chapter]Authored by: Reyes, N., Susnjak, T.
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Thesis

Susnjak, T. (2012). Efficient boosted ensemble-based machine learning in the context of cascaded frameworks. (Doctoral Thesis)
[Doctoral Thesis]Authored by: Susnjak, T.
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Susnjak, T. (2009). Accelerating classifier training using AdaBoost within cascades of boosted ensembles. (Master's Thesis)
[Masters Thesis]Authored by: Susnjak, T.

Report

Susnjak, T., & Schumacher, C.(2018). Towards Real-Time GDP Prediction. : Knowledge Exchange Hub, 黑料网
[Technical Report]Authored by: Schumacher, C., Susnjak, T.
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Barczak, T.(2011). AA new 2D static hand gesture colour image dataset for asl gestures. (Report No. Volume 15, pp.12 - 20, ISSN 1175-2777). Institute of Information and Mathematical Sciences, 黑料网 Albany
[Technical Report]Authored by: Susnjak, T.
Susnjak, T., Barczak, ALC., & Hawick, KA.(2010). A Novel Bootstrapping Method for Positive Datasets in Cascades of Boosted Ensembles. (Report No. Volume 14, pp.17-24, ISSN 1175-2777). Institute of Information and Mathematical Sciences, 黑料网 Albany
[Technical Report]Authored by: Susnjak, T.
Susnjak, T., Barczak, ALC., & Hawick, KA.(2009). Accelerated Face Detector Training using the PSL Framework. (Report No. Volume 13, pp.68 - 80, ISSN 1175-2777). Institute of Information and Mathematical Sciences, 黑料网 Albany
[Technical Report]Authored by: Susnjak, T.

Conference

Sadeghi, J., Jelodar, MB., Susnjak, T., Sutrisna, M., & Wilkinson, S.Introducing an Integrated Agent-Based and Reinforcement Learning Model of Contracting and Subcontracting in Construction Sector. Lecture Notes in Civil Engineering. (pp. 529 - 547). 2366-2557.
[Conference]Authored by: Babaeianjelodar, M., Susnjak, T., Sutrisna, M.
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Ahmed, N., Barczak, ALC., Bazai, SU., Susnjak, T., & Rashid, MA. (2020). Performance Analysis of Multi-Node Hadoop Cluster Based on Large Data Sets. 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020.
[Conference Paper in Published Proceedings]Authored by: Susnjak, T.
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Ramaswami, GS., Susnjak, T., Mathrani, A., & Umer, R.(2020). Predicting Students Final Academic Performance using Feature Selection Approaches. Paper presented at the meeting of 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2020
[Conference Paper]Authored by: Mathrani, A., Susnjak, T.
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McIntosh, T., Jang-Jaccard, J., Watters, P., & Susnjak, T.The Inadequacy of Entropy-Based Ransomware Detection. Communications in Computer and Information Science. (pp. 181 - 189). 1865-0929.
[Conference]Authored by: Susnjak, T.
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Wanniarachchi, VU., Mathrani, A., Susnjak, T., & Scogings, C. (2019). Gendered objectification of weight stigma in social media: a mixed method analysis. ACIS 2019 Proceedings - 30th Australasian Conference on Information Systems. (pp. 362 - 372).
[Conference Paper in Published Proceedings]Authored by: Mathrani, A., Scogings, C., Susnjak, T.
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Ramaswami, GS., Susnjak, T., & Mathrani, A. (2019). Capitalizing on Learning Analytics Dashboard for Maximizing Student Outcomes. 2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2019.
[Conference Paper in Published Proceedings]Authored by: Mathrani, A., Susnjak, T.
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Umer, R., Mathrani, A., Susnjak, T., & Lim, S.(2019). Mining activity log data to predict student's outcome in a course. Paper presented at the meeting of ACM International Conference Proceeding Series
[Conference Paper]Authored by: Mathrani, A., Susnjak, T.
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Umer, R., Susnjak, T., Mathrani, A., & Suriadi, S. (2019). A learning analytics approach: Using online weekly student engagement data to make predictions on student performance. 2018 International Conference on Computing, Electronic and Electrical Engineering, ICE Cube 2018.
[Conference Paper in Published Proceedings]Authored by: Mathrani, A., Susnjak, T.
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Reyes, NH., Barczak, ALC., & Susnjak, T. (2018, August). Autonomous Navigation in Partially Known Confounding Maze-Like Terrains Using D* Lite with Poisoned Reverse. Presented at 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA). Kosice, Slovakia.
[Conference Oral Presentation]Authored by: Reyes, N., Susnjak, T.
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Reyes, NH., Barczak, ALC., & Susniak, T. (2018). Autonomous navigation in partially known confounding maze-like terrains using D∗Lite with poisoned reverse. DISA 2018 - IEEE World Symposium on Digital Intelligence for Systems and Machines, Proceedings. (pp. 67 - 76).
[Conference Paper in Published Proceedings]Authored by: Reyes, N., Susnjak, T.
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Umer, R., Susnjak, T., Mathrani, A., & Suriadi, S. (2017). Predicting Student’s Academic Performance in a MOOC Environment. DMCCIA-2017. (pp. 119 - 124). : 11th International Conference on Data Mining, Computers, Communication and Industrial Applications
[Conference Paper in Published Proceedings]Authored by: Mathrani, A., Susnjak, T.
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Reyes, NH., Barczak, ALC., Susnjak, T., & Jordan, A. (2017). Fast and smooth replanning for navigation in partially unknown terrain: The hybrid Fuzzy-D*lite algorithm. Advances in Intelligent Systems and Computing. Vol. 447 (pp. 31 - 41).
[Conference Paper in Published Proceedings]Authored by: Reyes, N., Susnjak, T.
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suriadi, S., Susnjak, T., Ponder-Sutton, ., Watters, P., & schumacher, . (2016). Characterizing problem gamblers in New Zealand: A novel expression of process cubes. Proceedings of the CAiSE’16 Forum at the 28th International Conference on Advanced Information Systems Engineering.
[Conference Paper in Published Proceedings]Authored by: Schumacher, C., Susnjak, T.
Alqahtani, S., Barczak, A., Reyes, N., Susnjak, T., & Ganley, A. (2016). Automatic alignment and comparison on images of petri dishes containing cell colonies. International Conference Image and Vision Computing New Zealand. Vol. 2016-November
[Conference Paper in Published Proceedings]Authored by: Reyes, N., Susnjak, T.
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Suriadi, S., Susnjak, T., Ponder-Sutton, AM., Watters, PA., & Schumacher, C.Characterizing problem gamblers in New Zealand: A novel expression of process cubes. CEUR Workshop Proceedings. (pp. 185 - 192). 1613-0073.
[Conference]Authored by: Schumacher, C., Susnjak, T.
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Safar, A., Reyes, NH., Barczak, ALC., Susnjak, T., & Ganley, A. (2016). Automatic alignment and comparison on images of petri dishes containing cell colonies. 2015 International Conference on Image and Vision Computing New Zealand (IVCNZ). (pp. 1 - 6). : 2015 International Conference on Image and Vision Computing New Zealand (IVCNZ)
[Conference Paper in Published Proceedings]Authored by: Reyes, N., Susnjak, T.
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Bayati, S., Parsons, D., Susnjak, T., & Heidary, M.Big data analytics on large-scale socio-technical software engineering archives. 2015 3rd International Conference on Information and Communication Technology, ICoICT 2015. (pp. 65 - 69).
[Conference]Authored by: Susnjak, T.
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Susnjak, T., Kerry, D., Barczak, A., Reyes, N., & Gal, Y. (2015). Wisdom of crowds: An empirical study of ensemble-based feature selection strategies. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9457 (pp. 526 - 538).
[Conference Paper in Published Proceedings]Authored by: Reyes, N., Susnjak, T.
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Parsons, D., Susnjak, T., & Mathrani, A. (2015). The software developer cycle: Career demographics and the market clock : SQL the new COBOL?. ACM International Conference Proceeding Series. Vol. 28-September-2015 (pp. 86 - 90).
[Conference Paper in Published Proceedings]Authored by: Mathrani, A., Susnjak, T.
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Barczak, ALC., susnjak, T., & Reyes, NH. (2014). Characterisation of the discriminative properties of the radial tchebichef moments for hand-written digits. Poster session presented at the meeting of 29th International Conference on Image and Vision Computing New Zealand, IVCNZ 2014. Hamilton
[Conference Poster]Authored by: Reyes, N., Susnjak, T.
Barczak, ALC., Susnjak, T., & Reyes, NH. (2014). Characterisation of the discriminative properties of the Radial Tchebichef Moments for hand-written digits. ACM International Conference Proceeding Series. Vol. 19-21-November-2014 (pp. 154 - 159).
[Conference Paper in Published Proceedings]Authored by: Reyes, N., Susnjak, T.
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Susnjak, T., Barczak, A., & Reyes, N. (2013). A decomposition machine-learning strategy for automated fruit grading. Lecture Notes in Engineering and Computer Science. Vol. 2 (pp. 819 - 825).
[Conference Paper in Published Proceedings]Authored by: Reyes, N., Susnjak, T.
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Barczak, ALC., Susnjak, T., Reyes, NH., & Jonhson, MJ. (2013). Colour segmentation for multiple low dynamic range images using boosted cascaded classifiers. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6710229. (pp. 136 - 141). : IVCNZ 2013 (International Conference on Image and Vision Computing New Zealand
[Conference Paper in Published Proceedings]Authored by: Johnson, M., Reyes, N., Susnjak, T.
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Barczak, ALC., Susnjak, T., Reyes, NH., & Johnson, MJ.Colour segmentation for multiple low dynamic range images using boosted cascaded classifiers.
[Conference Oral Presentation]Authored by: Johnson, M., Susnjak, T.
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Reyes, NH., Barczak, ALC., & Susnjak, T. (2013). Tuning fuzzy-based hybrid navigation systems using calibration maps. Advances in Intelligent Systems and Computing. Vol. 208 AISC (pp. 713 - 722).
[Conference Paper in Published Proceedings]Authored by: Reyes, N., Susnjak, T.
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Reyes, N., Barczak, A., & Susnjak, T. (2012, December). Tuning fuzzy-based hybrid navigation systems using calibration maps. Presented at The 1st International Conference on Robot Intelligence Technology and Applications 2012 (RITA 2012). Gwangju, South Korea.
[Conference Oral Presentation]Authored by: Reyes, N., Susnjak, T.
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Susnjak, T., Barczak, A., & Reyes, N. (2012, August). Multiclass cascades for ensemble-based boosting algorithms. Presented at Proceedings of the Sixth Starting AI Researchers' Symposium. Montpellier, France.
[Conference Oral Presentation]Authored by: Susnjak, T.
Susnjak, T., Barczak, A., Reyes, N., & Hawick, K. (2012, August). Multiclass cascades for ensemble-based boosting algorithms. Presented at ECAI 2012: 20th European Conference on Artificial Intelligence. Montpellier, France.
[Conference Oral Presentation]Authored by: Susnjak, T.
Mendonca, L., Barazani, B., Chaves, B., Torikai, D., Ibrahim, R., Piazzeta, M., . . . Susnjak, T.Study of a Copper Capacitive MEMS as a Sensor for Automotive Fuel Evaluation.
[Conference Paper]Authored by: Susnjak, T.
Susnjak, T., Barczak, A., Reyes, N., & Hawick, K. (2012). Multiclass cascades for ensemble-based boosting algorithms. Frontiers in Artificial Intelligence and Applications. Vol. 241 (pp. 330 - 335).
[Conference Paper in Published Proceedings]Authored by: Reyes, N., Susnjak, T.
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1
Susnjak, T., Barczak, A., Reyes, N., & Hawick, K. (2011). A new ensemble-based cascaded framework for multiclass training with simple weak learners. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6854 LNCS (pp. 563 - 570).
[Conference Paper in Published Proceedings]Authored by: Susnjak, T.
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Susnjak, T., Barczak, A., & Hawick, K. (2010, August). A modular approach to training cascades of boosted ensembles. Presented at Joint International Association for Pattern Recognition International Workshop, Structural and Syntactic Pattern Recognition and Statistical Techniques in Pattern Recognition 2010 Proceedings
[Conference Oral Presentation]Authored by: Susnjak, T.
Susnjak, T., Barczak, ALC., & Hawick, KA. (2010). Adaptive ensemble based learning in non-stationary environments with variable concept drift. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6443 LNCS (pp. 438 - 445).
[Conference Paper in Published Proceedings]Authored by: Susnjak, T.
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2
1
Susnjak, T., Barczak, AL., & Hawick, KA. (2010). A modular approach to training cascades of boosted ensembles. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 6218 LNCS (pp. 640 - 649).
[Conference Paper in Published Proceedings]Authored by: Susnjak, T.
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Susnjak, T., & Barczak, ALC. (2009). Accelerated classifier training using the PSL cascading structure. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5506 LNCS (pp. 945 - 952).
[Conference Paper in Published Proceedings]Authored by: Susnjak, T.
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Barczak, ALC., Reyes, NH., Susnjak, T., & Johnson, MJ. (2011). Real-time computation of moment invariants combined with contrast stretching. European Signal Processing Conference. (pp. 544 - 548).
[Conference Paper in Published Proceedings]Authored by: Johnson, M., Susnjak, T.
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Other

Susnjak, T. (2022). A Prescriptive Learning Analytics Framework: Beyond Predictive Modelling and onto Explainable AI with Prescriptive Analytics. arXiv
[Other]Authored by: Susnjak, T.
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Maddigan, P., & Susnjak, T. (2022). Forecasting Patient Demand at Urgent Care Clinics using Machine Learning. : arXiv
[Internet publication]Authored by: Susnjak, T.
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Barczak, A., Reyes, N., & Susnjak, T. (2019). Assessment of the local tchebichef moments method for texture classification by fine tuning extraction parameters. : arXiv
[Working Paper]Authored by: Reyes, N., Susnjak, T.
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Susnjak, T. (2013, April). On Fruit Sorting and Grading using Boosting and Rule Induction Algorithms. In 黑料网 I.T. Seminar.
[Oral Presentation]Authored by: Susnjak, T.
Susnjak, T. (2013, October). A Decomposition Machine-learning Strategy for Automated Fruit Sorting. In 黑料网 I.T. Seminar.
[Oral Presentation]Authored by: Susnjak, T.

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Barczak, A., reyes, N., Abastillas, A., Piccio, A., & Susnjak, T. (2012). MU_HandImages_ASL. Retreived from /~albarcza/gesture_dataset2012.html
[Dataset]Authored by: Susnjak, T.

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Uncategorised

Susnjak, T., & Griffin, E.JanuaryMay
[Preprint]Authored by: Susnjak, T.
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McIntosh, TR., Susnjak, T., Liu, T., Watters, P., & Halgamuge, MN.FebruaryMay
[Preprint]Authored by: Liu, T., Susnjak, T.
Read Online:
McIntosh, TR., Susnjak, T., Liu, T., Watters, P., Nowrozy, R., & Halgamuge, MN.FebruaryMay
[Preprint]Authored by: Liu, T., Susnjak, T.
Read Online:
Susnjak, T., Hwang, P., Reyes, N., Barczak, A., McIntosh, T., & Ranathunga, R.May
[Preprint]Authored by: Ranathunga, R., Reyes, N., Susnjak, T.
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Susnjak, T.July
[Preprint]Authored by: Susnjak, T.
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Susnjak, T.DecemberJuly
[Preprint]Authored by: Susnjak, T.
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Susnjak, T., & Maddigan, P.November
[Preprint]Authored by: Susnjak, T.
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Consultancy and Languages

Languages

  • German
    Spoken ability: Average
    Written ability: Average
  • Croatian
    Spoken ability: Average
    Written ability: Average
  • English
    Spoken ability: Excellent
    Written ability: Excellent

Teaching and Supervision

Teaching Statement

  • Data science papers
  • Analytics papers
Contact Supervisor

Summary of Doctoral Supervision

Position Current Completed
Main Supervisor 2 2
Co-supervisor 4 4

Current Doctoral Supervision

Main Supervisor of:

  • Lingbo Li - Doctor of Philosophy
    Utilizing Large Language Models for Automating Meta-analysis Research Synthesis
  • Binglan Han - Doctor of Philosophy
    Automation of Systematic Literature Review Tasks Using Large Language Models

Co-supervisor of:

  • Martin Brenner - Doctor of Philosophy
    Multimodal object perception
  • Maryam Tagharobi - Doctor of Philosophy
    Modeling capacity, capability, security and disruption in New Zealand construction industry
  • Amir Karimi - Doctor of Philosophy
    Dynamic Vulnerability Modelling for Separate Sewer Networks in the Face of Climate Change: A Data-Driven Approach
  • Masood Sujau - Doctor of Philosophy
    Applications of artificial intelligence methods in veterinary epidemiology

Completed Doctoral Supervision

Main Supervisor of:

  • 2023 - Gomathy Suganya Ramaswami - Doctor of Philosophy
    Learning Analytics: On Effectiveness of Dashboarding for Enhancing Student Learning
  • 2020 - Rahila Umer - Doctor of Philosophy
    Prediction of Students鈥� Performance Through Data Mining.

Co-supervisor of:

  • 2023 - Vajisha Wanniarachchi - Doctor of Philosophy
    Analysing Underpinning Patterns in Social Media Posts that Promote Fat Stigmatisation
  • 2022 - Nasim Ahmed - Doctor of Philosophy
    PERFORMANCE MODELLING, ANALYSIS AND PREDICTION OF SPARK JOBS IN HADOOP CLUSTER
  • 2022 - Rongyao Hu - Doctor of Philosophy
    Graph Learning and Its Applications
  • 2022 - Jiawei Zhao - Doctor of Philosophy
    Cross-Lingual Learning in Low Resource

Media and Links

Media

  • 30 Nov 2015 - Newspaper
    Data science: Making use of a valuable by-product
    http://m.nzherald.co.nz/technology/news/article.cfm?c_id=5&objectid=11551722 Together with PVC Ray Geor, I wrote an article for the Herald discussing the opportunities that Data Science is providin
  • 30 Jul 2016 - Television
    Killer robots fuelled by artificial intelligence
    https://tvnz.co.nz/seven-sharp/killer-robots-fuelled-artificial-intelligence-you-scared-video-6365632 In mid 2015, Prof Stephen Hawking wrote a letter warning about the potential threat that AI pose
  • 25 Jan 2016 - Magazine
    The 鈥榠nternet of things鈥� and the data challenge
    http://viewer.zmags.com/publication/c905856a#/c905856a/4 Precision agriculture is one of the key industries set to be revolutionised by the deluge of data that is about to become available through

Other Links