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 at major journals and conferences, three of which received Best Paper awards.
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. New York, NY, USA: ACM. doi: 10.1145/3626772.3657674
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. IEEE. doi: 10.1109/icitee59582.2023.10317700
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
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
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
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
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. New York, NY, USA: ACM. doi: 10.1145/3626772.3657674
Pruthipanyasakul, Nareekarn, Kanungsukkasem, Nont, Urruty, Thierry and Leelanupab, Teerapong (2023). Deep Neural Networks for the Qualitative Analysis of Myocardial Perfusion Emission Computed Tomography Images. IEEE. doi: 10.1109/icitee59582.2023.10317700