pRen-Hung Hwang received his Ph.D. degree in computer science from the University of Massachusetts, Amherst, Massachusetts, USA, in 1993. He joined the College of Artificial Intelligence, National Yang Ming Chiao Tung University in August 2022, where he is now the Dean of the AI College. Before he joined NYCU, he was with the Department of Computer Science and Information Engineering, National Chung Cheng University, Chia-Yi, Taiwan, from 1993 to 2022, where he served as the dean of the Office of Information Technology, the dean of the College of Engineering, and a distinguished professor of the Department of Computer Science and Information Engineering. He is currently an Associate Editor of IEEE Communications Surveys & Tutorials, IEEE Trans. on iVehicular Technology,/i and IEICE Trans. on Communications. Prof. Hwang published more than 260 international journal and conference papers. He is also a co-author of the textbook "Computer Networks: An Open Source Approach" (www.mhhe.com/lin), with Ying-Dar Lin and Fred Baker (McGraw-Hill, 2011). He received the Best Paper Award from The 6th International Conference on Internet of Vehicles 2019, IEEE Ubi-Media 2018, IEEE SC2 2017, IEEE IUCC 2014, and the IEEE Outstanding Paper Award from IEEE IC/ATC/ICA3PP 2012. He was the guest editor of IEEE Network, special issue on "Open Source for Networking: Development and Experimentation," IET Communications, a special issue on "WiMAX Integrated Communications," and the Journal of Security and Communication Networks, special issue on "Security and Privacy in Mobile Applications and Platforms (SPMNA)." He served as the general chair of the International Computer Symposium (ICS) 2016, International Symposium on Pervasive Systems, Algorithms, and Networks (ISPAN) 2018, International Symposium on Computer, Consumer, and Control (IS3C) 2018, and IEEE DataCom 2019 (The 5th IEEE International Conference on Big Data Intelligence and Computing). His current research interest is in Deep Learning, Wireless Communications, Network Security, AIoT, and Cloud/Edge/Fog Computing. Prof. Hwang is a senior member of IEEE./p

pThe Artificial Intelligence and Networking Technology (AINT) Lab consists of graduate students from both National Chung Cheng University and National Yang Ming Chiao Tung University. Currently, we have fourPh.D. students (two from India) and twelve Master students. Our main research interests include cybersecurity, deep learning, 5G/6G, and AIoT. We welcome foreign students to join our lab for research intern and degree programs (Ph.D. and Master of Engineering/Science). For details, please refer to Prof. Hwang's website (a href="https://rhhwang.csie.io/English/index.html"https://rhhwang.csie.io/English/index.html/a). Currently, we have following ongoing projects:/ppstrongNational Science and Technology Council (2022~2025)/strong/pulli6G Key Technologies and Applications: Optimizing Hierarchical Resource Offloading and AI as a Service Model (Integrated project with 5 subprojects)/liliHost-based Intrusion Detection System based on Multiple Data Sources and Deep Learning/li/ulpstrongDepartment of Defense (DoD) (2022~2026)/strong/pulliDoD Advanced Technology Research Program – IoT Security Vulnerability Detection/liliApplying for Active Defense (2024~2027)/li/ulpstrongTelecom Technology Center(TTC) (2021~2024)/strong/pulliDevelopment of AI Image Recognition System Security Testing Methods and Tools (2021)/liliDevelopment of Artificial Intelligence Face Recognition System Security Testing Tools (2022)/liliDevelopment of Security Testing Tools for Intrusion Detection Systems Based on Transformer (2023)/li/ulpstrongIndustrial Technology Research Institute (ITRI) (2023)/strong/pulliPreliminary Research on Information Security Strategies and Criteria for Energy Storage Systems/li/ulpstrongChina Steel Corporation (2023)/strong/pulliDevelopment of Technology for Counting the Quantity of Small and Medium-Sized Steel Ingots During Overhead Crane Handling/li/ulpstrongShin Kong Wu Ho-Su Memorial Hospital (2023)/strong/pulliPrediction of the Probability of Failure of Peritoneal Dialysis within One Year based on Deep Learning and Feature Importance Analysis/li/ul