ICBET 2024          Full Paper Submission Deadline: 20 January, 2024     Conference Dates: 14th - 17th June 2024      Conference Place: Seoul, South Korea



Seoul, South Korea





Prof. Tae-Seong Kim

Kyung Hee University, Republic of Korea

Tae-Seong Kim received the B.S. degree in Biomedical Engineering from the University of Southern California (USC) in 1991, M.S. degrees in Biomedical and Electrical Engineering from USC in 1993 and 1998 respectively, and Ph.D. in Biomedical Engineering from USC in 1999. After his postdoctoral work in Cognitive Sciences at the University of California at Irvine in 2000, he joined the Alfred E. Mann Institute for Biomedical Engineering and Dept. of Biomedical Engineering at USC as Research Scientist and Research Assistant Professor. In 2004, he moved to Kyung Hee University in Republic of Korea where he is currently Professor in the Department of Biomedical Engineering. His research interests have spanned various areas of biomedical imaging, bioelectromagnetism, neural engineering, and assistive lifecare technologies. Dr. Kim has been developing novel methodologies in the fields of signal and image processing, machine learning, pattern classification, and artificial intelligence. Lately Dr. Kim has started novel projects in the developments of smart robotics and machine vision with deep learning methodologies. Dr. Kim has published more than 380 papers and twelve international book chapters. He holds ten international and domestic patents and has received numerous best paper awards.

Speech Title: "Deep Learning for Human Activity Recognition"

Abstract: Human activity recognition (HAR) is a rapidly growing and challenging research field that involves identifying and recognizing the specific actions and movements of humans based on multi-modal sensor data. It is an important study topic since it contributes to many important applications such as intelligent lifecare and surveillance systems, human computer/robot interfaces, intelligent healthcare systems etc. Recently deep learning methodologies are being utilized for HAR as they can learn features directly from raw data and significantly improve the performance of HAR. In this talk, I will present the latest works on multi-modal sensor based HAR using various deep learning models along with their applications in the areas of smart homes, healthcare, and robotics.

Prof. Tatsuya Akutsu

Kyoto University, Japan

Tatsuya Akutsu received B.Eng. and M.Eng. in Aeronautics and D.Eng. in Information Engineering from University of Tokyo, in 1984, 1986 and 1989, respectively. From 1989 to 1994, he was with Mechanical Engineering Laboratory. From 1994 to 1996, he was an Associate Professor in the Department of Computer Science at Gunma University. From 1996 to 2001, he was an Associate Professor in Human Genome Center, Institute of Medical Science, University of Tokyo. Since 2001, he has been a Professor in Bioinformatics Center, Institute for Chemical Research, Kyoto University. He is a fellow of Information Processing Society of Japan (IPSJ), and was an editor-in-chief of IPSJ Transactions on Bioinformatics for 2006-2009. His research interests include bioinformatics, complex networks, and dicrete algorithms.

Speech Title: "Graph Theoretic Approaches to Analysis of Biological Networks"

Abstract: For deeper understanding of biological systems, it is crucial to analyze biological networks, which include gene regulatory networks, protein-protein interaction networks, and metabolic networks. To this end, we have been applying such graph theoretic concepts as minimum dominating sets, feedback vertex sets, and dense subgraphs. Furthermore, we have extended these concepts to better extract useful information from biological networks. We have been applying these concepts to various biological problems, which include inference of cancer-related genes, analysis of aging genes, analysis of mouse brain neural networks, identification of protein hot spots, and comparison of metabolic networks in cancer and normal cells. In this talk, we briefly introduce these graph theoretic concepts and explain their extensions and applications.




Assoc. Prof. Md. Altaf-Ul-Amin

Nara Institute of Science and Technology, Japan

Md. Altaf-Ul-Amin received B.Sc. degree in Electrical and Electronic Engineering from Bangladesh University of Engineering and Technology (BUET), Dhaka, M.Sc. degree in Electrical, Electronic and Systems Engineering from Universiti Kebangsaan Malaysia (UKM) and PhD degree from Nara Institute of Science and Technology (NAIST), Japan. He received the best student paper award in the IEEE 10th Asian Test Symposium. Also, he received two other best paper awards as a co-author of journal articles. He previously worked in several universities in Bangladesh, Malaysia and Japan. Currently he is working as an associate professor in Computational Systems Biology Lab of NAIST. He is conducting research on Network Biology, Systems Biology, Cheminformatics and Biological Databases. He published around 90 peer reviewed papers in international journals and conference proceedings. Current google scholar citation index of his publications is more than 7500.

Speech Title: "Learning Vector Quantized Representation for Cancer Subtypes Identification Incorporating Omics Data"

Abstract: Defining and separating cancer subtypes is essential for facilitating personalized therapy modality and prognosis of patients. The definition of subtypes has been constantly recalibrated due to our deepened understanding. During this recalibration, researchers often rely on the clustering of cancer omics data to provide an intuitive visual reference that could reveal the intrinsic characteristics of subtypes. However, while existing studies have shown promising results, they suffer from issues associated with omics data: sample scarcity and high dimensionality. As such, existing methods often impose unrealistic assumptions to extract useful features from the data while avoiding overfitting spurious correlations. In this talk, we discuss the effectiveness of a recent strong generative model, Vector Quantized Variational AutoEncoder (VQ-VAE), to tackle the data issues and extract informative latent features that are crucial to the quality of subsequent clustering by retaining only information relevant to reconstructing the input. VQ-VAE does not impose strict assumptions; hence, its latent features are better representations of the input, capable of yielding superior clustering performance with any main stream clustering method.



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