Keynote Speakers
2027 12th International Conference on Intelligent Information Technology (ICIIT 2027) aims to gather professors, researchers, scholars and industrial pioneers all over the world. ICIIT is the premier forum for the presentation and exchange of past experiences and new advances and research results in the field of theoretical and industrial experience. The conference welcomes contributions which promote the exchange of ideas and rational discourse between educators and researchers all over the world. We aim to building an idea-trading platform for the purpose of encouraging researcher participating in this event. ICIIT 2027 is welcome qualified persons to delivery a speech in the related fields. If you are interested, please send a brief CV with photo to the conference email box: iciit@cbees.net.
Prof. Jae Kyu Lee
Xi'an Jiaotong University, China / KAIST, Korea
Jae Kyu Lee, Ph.D. is the Distinguished Professor in the School
of Management at Xi’an Jiaotong University and Professor
Emeritus at the Korea Advanced Institute of Science and
Technology (KAIST). He served as professor and HHI Chair
Professor at KAIST from 1985 to 2016 after he received his Ph.D.
in Information and Operations Management from the Wharton
School, University of Pennsylvania. He received a Fellow Award
and the LEO Award, and served as President (2015-6) of the
Association for Information Systems. His research has focused on
AI for Decision Support Systems and Electronic Commerce, and he
founded the Bright Internet with a preventive cybersecurity
paradigm. He chaired the International Conference on Information
Systems and the Pacific Asia Conference on Information Systems.
He founded the International Conference on Electronic Commerce
and the journal Electronic Commerce Research and Applications
(Elsevier). Recently, he has been working on the development of
gradual personalization and domain adaptation for AI-based
classification models to select beneficial datasets and to
integrate databases from the perspective of individual users. He
has also been working on the development of Generative Genomics
using AI to distinguish the generative genes from evolutionary
genes to explain the origins of biological diversity. This
research uses extensive AI-based analysis over multi-omics big
databases in bioinformatics. He is the founder and president of
the Generative Genomics Research Council and its workshops as
described at https://www.ggenomics.org/. He received ten best
paper awards from major conferences, and received a national
decoration from the Korean government for his contributions to
the development of the IT industry. He has published many papers
in international journals such as MIS Quarterly, Information
Systems Research, Journal of the Association for Information
Systems, Management Science, Decision Support Systems,
Communications of ACM, International Journal of Electronic
Commerce, Expert System with Applications, European Journal of
Information Systems, and many others. He was the founding
president of the Korea Intelligent Information Systems Society,
and served as the president of the Korea Society of Management
Information Systems.
Further information can be found at
https://www.business.kaist.ac.kr/faculty/jklee/.
Speech Title: "Personalization and Adaptation of Training Datasets for AI-based Classification Models"
Prof. Balachandran Manavalan
Sungkyunkwan University (SKKU), Republic of Korea
Prof. Balachandran Manavalan is an Associate Professor in the Department of Integrative Biotechnology at Sungkyunkwan University (SKKU), Republic of Korea, where he leads the Computational Biology and Bioinformatics Laboratory. He earned his Ph.D. in Computational Biology from Ajou University in 2011. Before joining SKKU in 2022, he held research positions at the Korea Institute for Advanced Study and Ajou University School of Medicine. His research focuses on developing artificial intelligence and machine learning methods to address biological and biomedical challenges. His interests include peptide and protein function prediction, RNA modifications, immunoinformatics, multi-omics analysis, and biomedical image analysis. His laboratory integrates protein language models, deep learning, and ensemble learning to develop accurate and interpretable predictive tools, with applications in therapeutic discovery and disease characterization. Prof. Manavalan has been recognized among the world’s top 2% of scientists in Stanford University’s citation-based rankings and among the top 0.05% of scholars in Bioinformatics by ScholarGPS in 2026. He also received the 2026 SKKU RISING-Fellowship. His work emphasizes rigorous model evaluation, explainable AI, and accessible computational resources that support the wider research community.
Speech Title: "AI for Biological Discovery: From Protein Language Models to Biomedical Applications"
Abstract: Biological sequences and medical images offer complementary perspectives on molecular function and disease. Translating these data into reliable predictions requires computational methods that capture complex patterns and generalize across diverse biological settings. This keynote will explore how protein language models, deep learning, and ensemble learning can advance biological discovery and biomedical research. The presentation will highlight applications in peptide function and safety prediction, peptide–MHC interaction modeling, and computational pathology. These examples will demonstrate how biological knowledge and learned representations can guide predictive modeling across molecular and tissue scales. Particular attention will be given to data quality, rigorous evaluation, independent validation, and model interpretability, emphasizing the distinction between predictive accuracy and biological understanding. The keynote will conclude with perspectives on integrating complementary biological data and experimental evidence to develop more reliable and interpretable AI tools. It will offer practical insights for researchers applying intelligent information technologies to challenges in biology and medicine.