Emeritus Distinguished Professor Richard J. Simpson PhD FATSE
LaTrobe University in Melbourne, Australia

Richard Simpson obtained his Ph.D. in biochemistry at Melbourne University and, currently, is Emeritus Distinguished Professor at LaTrobe University in Melbourne. He has made major contributions to the field of analytical protein chemistry and pioneered many classical microsequencing strategies and multi-dimensional HPLC/ PAGE separation tools. As a pioneer in his field, Prof. Simpson has made numerous groundbreaking advances in cytokine biology (IL-6, IL-9, LIF, rEGF, sIL-6:sIL-6R:sgp130 receptor complex). His team increasingly focusses on clinically relevant questions, such as identifying novel diagnostic/ prognostic biomarkers (proteins, fusion gene transcripts, lncRNAs) of colorectal cancer, and therapeutic role of extracellular vesicles (exosomes, midbody remnants). With over 600 publications Prof. Simpson is listed as one of the World’s Most Influential Scientific Minds (2015, Thompson-Reuter), and a Highly Cited Author (top 1% in the field of Biochemistry and Molecular Biology - Clarivate/WoS 2015-2024). He is listed in “Stanford University List of top 1% of World’s most influential scientists” (2022/2023) and he is a ScholarGPS ‘Respected Scholar’ (top 0.05% Scholars Worldwide). He has an h-index of 113 and > 67,000 citations according to Google Scholar and holds 14 patents. Prof. Simpson has written three leading textbooks on proteomics with Cold Spring Harbor Press. Prof. Simpson’s research contributions have garnered him numerous prestigious accolades including AMRAD Pharmacia Medal, Honorary Life Member of ASBMB, named Plenary Lecture ‘Simpson Lecture’ (Australian Proteomics Society), HUPO Distinguished Service Award, and a Centenary Medal conferred by the Australian Government, and a Centenary Medal conferred by the Australian Government "For service to Australian society in research and development. He has played a pivotal role in establishing protein chemistry in Asia-Oceana (Australasian Proteomics Society, HUPO, AOHUPO, and Asia Pacific Protein Society).   

Prof. Tae-Seong Kim

Kyung Hee University, South Korea

Tae-Seong Kim is a distinguished Professor in the Department of Biomedical Engineering at Kyung Hee University in the Republic of Korea. He earned his B.S. degree in Biomedical Engineering from the University of Southern California (USC) in 1991. He went on to receive M.S. degrees in both Biomedical and Electrical Engineering from USC in 1993 and 1998, respectively, and completed his Ph.D. in Biomedical Engineering at USC in 1999. Dr. Kim conducted his postdoctoral research in Cognitive Sciences at the University of California, Irvine in 2000. Subsequently, he joined the Alfred E. Mann Institute for Biomedical Engineering and the Department of Biomedical Engineering at USC as a Research Scientist and Research Assistant Professor. In 2004, Dr. Kim moved to Kyung Hee University, where he currently serves as a Professor. His research interests encompass biomedical imaging, bioelectromagnetism, neural engineering, and assistive lifecare technologies. He has been pioneering new methodologies in signal and image processing, machine learning, pattern classification, and artificial intelligence. Recently, Dr. Kim has initiated novel projects focused on the development of smart robotics and machine vision utilizing deep learning techniques. Dr. Kim is a prolific author, having published over 380 papers and twelve international book chapters. He holds ten international and domestic patents and has received numerous best paper awards.

Speech Title: Advancements in Deep Reinforcement Learning for Dual-Arm Robots in Medical and Lifecare Environments

Abstract: Robot intelligence is a dynamic interdisciplinary field encompassing robotics, machine learning, pattern recognition, and artificial intelligence. Recent advancements in machine learning, particularly deep reinforcement learning, have significantly accelerated the development of this field. These advancements enable robots to autonomously learn and perform a variety of tasks, mirroring human capabilities. This presentation will explore the methodologies of deep reinforcement learning applied to dual-arm robots, focusing on their ability to learn and execute long-horizon assistive tasks in medical and lifecare settings. The discussion will highlight the potential of these technologies to revolutionize patient care and support in healthcare environments.

Dr. Surakameth Mahasirimongol
Director of the Information and Communication Technology Center
Office of the Permanent Secretary, Ministry of Public Health