Overview
Background
Dr. Teerapong Leelanupab is a Senior Research Fellow at the University of Queensland, Electrical Engineering and Computer Science School. He was an Associate Professor in Information Technology at the School of Information Technology, King Mongkut’s Institute of Technology Ladkrabang, Thailand, from August 8, 2019. He was also a Co-Founder and active member of the Intelligence Lab for Cognitive and Business Analytics (IcBiz). He is also a Data Science and Information Technology Director at two start-up companies, Modgut and Thaibiogenix International (TBI), which are the first companies to commercialise human gut microbiome test services in Thailand and develop a complete digital traceability and test order management platform for providing retail and corporate customers, and research partners with such services.
Teerapong's main research interests are Text and Multimedia Information Retrieval (IR), Health Data Science, Machine Learning in Medical Imaging, Natural Language Processing and Adaptive, Contextual and Interactive Systems. He has been a principal investigator and co-principal investigator of several research projects granted by government agencies in Thailand, such as the Health Systems Research Institute (HSRI), National Research Council of Thailand (NRCT), Thailand Research Fund (TRF), and Program Management Unit for Human Resources & Institutional Development, Research and Innovation (PMU-B). His team won the first prize in Microsoft’s Imagine Cup Thailand 2015 and several national IT innovation awards. He was honourably listed among the top 400 scientists in Thai academic institutions according to a Google Scholar Citations (GSC) profile. He was honourably listed among the top 400 scientists in Thai academic institutions, according to a Google Scholar Citations profile. He published over sixty scientific papers in major journals and conferences, three of which received Best Paper awards.
Availability
- Dr Teerapong Leelanupab is:
- Available for supervision
Qualifications
- Bachelor, King Mongkut's Institute of Technology Ladkrabang
- Masters (Coursework) of Software Engineering, University College London (UCL)
- Doctor of Philosophy of Computing Science, University of Glasgow
Research interests
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Applied Machine Learning for Medicine
Deep and traditional machine learning techniques for enhancing classification and regression to identify key insights in the health domain; Deep neural networks; Transfer learning; Explainable AI; Mitigate imbalance issue in health data;
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Health and Biomedical Information Retrieval and Data Science
Health information retrieval models and strategies for users searching the web for health advice; Automated or interactive approaches to support medical systematic reviews, focusing on the screening phase and literature search for review; Retrieval models for cohort identification in clinical trials using electronic medical records; Retrieval models and strategies for clinical decision support and evidence-based medicine.
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Applications of Large Language Models in Medicine
LLMs for health and biomedical information access, focusing on retrieval-augmented generation (RAG) and agentic RAG; High-quality prompt engineering; Zero-shot learning; Few-shot learning; Many-shot learning; Chain-of-Thought (COT); LLM distillation; Fine-tuning LLM.
Works
Search Professor Teerapong Leelanupab’s works on UQ eSpace
2024
Conference Publication
Embark on DenseQuest: a system for selecting the best dense retriever for a custom collection
Khramtsova, Ekaterina, Leelanupab, Teerapong, Zhuang, Shengyao, Baktashmotlagh, Mahsa and Zuccon, Guido (2024). Embark on DenseQuest: a system for selecting the best dense retriever for a custom collection. SIGIR '24: 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, United States, 14-18 July 2024. New York, NY, United States: ACM. doi: 10.1145/3626772.3657674
2023
Conference Publication
Deep neural networks for the qualitative analysis of myocardial perfusion emission computed tomography images
Pruthipanyasakul, Nareekarn, Kanungsukkasem, Nont, Urruty, Thierry and Leelanupab, Teerapong (2023). Deep neural networks for the qualitative analysis of myocardial perfusion emission computed tomography images. 15th International Conference on Information Technology and Electrical Engineering (ICITEE), Chiang Mai, Thailand, 26-27 October 2023. Piscataway, NJ, United States: IEEE. doi: 10.1109/icitee59582.2023.10317700
2023
Book Chapter
When are Latent Topics Useful for Text Mining? : Enriching Bag-of-Words Representations with Information Extraction in Thai News Articles
Kanungsukkasem, Nont, Chuangkrud, Piyawat, Pitichotchokphokhin, Pimpitcha, Damrongrat, Chaianun and Leelanupab, Teerapong (2023). When are Latent Topics Useful for Text Mining? : Enriching Bag-of-Words Representations with Information Extraction in Thai News Articles. Recent Challenges in Intelligent Information and Database Systems. (pp. 205-219) Cham: Springer Nature Switzerland. doi: 10.1007/978-3-031-42430-4_17
2022
Journal Article
A multi-sequences MRI deep framework study applied to glioma classfication
Coupet, Matthieu, Urruty, Thierry, Leelanupab, Teerapong, Naudin, Mathieu, Bourdon, Pascal, Maloigne, Christine Fernandez and Guillevin, Rémy (2022). A multi-sequences MRI deep framework study applied to glioma classfication. Multimedia Tools and Applications, 81 (10), 13563-13591. doi: 10.1007/s11042-022-12316-1
2019
Journal Article
Financial Latent Dirichlet Allocation (FinLDA): Feature Extraction in Text and Data Mining for Financial Time Series Prediction
Kanungsukkasem, Nont and Leelanupab, Teerapong (2019). Financial Latent Dirichlet Allocation (FinLDA): Feature Extraction in Text and Data Mining for Financial Time Series Prediction. IEEE Access, 7, 71645-71664. doi: 10.1109/access.2019.2919993
2013
Journal Article
Crowdsourcing interactions: using crowdsourcing for evaluating interactive information retrieval systems
Zuccon, Guido, Leelanupab, Teerapong, Whiting, Stewart, Yilmaz, Emine, Jose, Joemon M. and Azzopardi, Leif (2013). Crowdsourcing interactions: using crowdsourcing for evaluating interactive information retrieval systems. Information Retrieval, 16 (2), 267-305. doi: 10.1007/s10791-012-9206-z
2012
Conference Publication
A comprehensive analysis of parameter settings for novelty-biased cumulative gain
Leelanupab, Teerapong, Zuccon, Guido and M. Jose, Joemon (2012). A comprehensive analysis of parameter settings for novelty-biased cumulative gain. 21st ACM international Conference on Information and Knowledge Management, Maui, HI, United States, 29 October - 02 November 2012. New York, NY, United States: ACM Press. doi: 10.1145/2396761.2398550
2011
Conference Publication
A Query-Basis Approach to Parametrizing Novelty-Biased Cumulative Gain
Leelanupab, Teerapong, Zuccon, Guido and Jose, Joemon M. (2011). A Query-Basis Approach to Parametrizing Novelty-Biased Cumulative Gain. 3rd International Conference on the Theory of Information Retrieval (ICTIR 2011), Bertinoro, Italy, 12-14 September 2011. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-23318-0_32
2010
Conference Publication
When Two Is Better Than One: A Study of Ranking Paradigms and Their Integrations for Subtopic Retrieval
Leelanupab, Teerapong, Zuccon, Guido and Jose, Joemon M. (2010). When Two Is Better Than One: A Study of Ranking Paradigms and Their Integrations for Subtopic Retrieval. 6th Asia Information Retrieval Societies Conference (AIRS 2010), Taipei, Taiwan, 1-3 December 2010. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-17187-1_15
2010
Book Chapter
University of Glasgow at ImageCLEFPhoto 2009: Optimising Similarity and Diversity in Image Retrieval
Leelanupab, Teerapong, Zuccon, Guido, Goyal, Anuj, Halvey, Martin, Punitha, P. and Jose, Joemon M. (2010). University of Glasgow at ImageCLEFPhoto 2009: Optimising Similarity and Diversity in Image Retrieval. Lecture Notes in Computer Science. (pp. 133-141) Berlin, Heidelberg: Springer. doi: 10.1007/978-3-642-15751-6_14
2010
Book Chapter
Revisiting sub–topic retrieval in the ImageCLEF 2009 photo retrieval task
Leelanupab, Teerapong, Zuccon, Guido and Jose, Joemon M. (2010). Revisiting sub–topic retrieval in the ImageCLEF 2009 photo retrieval task. ImageCLEF. (pp. 277-294) Berlin, Heidelberg: Springer Berlin Heidelberg. doi: 10.1007/978-3-642-15181-1_15
Supervision
Availability
- Dr Teerapong Leelanupab is:
- Available for supervision
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Available projects
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Optimizing Large Language Models for Healthcare Question Answering
Explore advanced strategies to enhance LLM performance in healthcare QA tasks, leveraging methods like domain-specific fine-tuning, ensemble refinement prompting, retrieval-augmented training, and adaptive interaction frameworks.
Media
Enquiries
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