Ki Ryong Kwon | Artificial Intelligence in Statistics | Best Faculty Award

Prof. Ki Ryong Kwon | Artificial Intelligence in Statistics | Best Faculty Award

Pukyong National University | South Korea

Professor Ki-Ryong Kwon is an eminent and highly respected scholar in electronics engineering, artificial intelligence, and computer science, serving as a leading Professor in the Division of Computer Engineering and AI at Pukyong National University, South Korea. he pursued his academic journey at Kyungpook National University, where he completed his bachelor’s, master’s, and doctoral degrees in Electronics Engineering, later advancing his research expertise through a prestigious postdoctoral fellowship at the University of Minnesota under the mentorship of Prof. Ahmed H. Tewfik, followed by a visiting scholar appointment at Colorado State University that further broadened his international academic exposure. Throughout his long-standing academic career, Professor Kwon has demonstrated exceptional leadership by serving as Dean of the College of Engineering, Vice-Dean of Engineering, Director of the Artificial Intelligence Lab, Director of the Center for Start-up Foundation, Director of the Center of IP Transfer, and Vice-Director of Industry-University Foundation Cooperation, contributing extensively to academic development, research infrastructure advancement, and student innovation ecosystems. Beyond the university environment, he has played influential strategic roles as Chairman of the Global Fintech Industry Promotion Center, Vice-Chairman of the Korea Cloud Association, Vice-Chairman of the Busan Federation of Service Industry, and Chair of the 4th Industrial Revolution Leadership Promotion Team for Busan City, where he has been instrumental in guiding regional and national initiatives in AI-driven transformation, digital economy growth, and emerging technology integration. His contributions to the global research community include major leadership roles in IEEE, the Korea Multimedia Society, the Korea Information Processing Society, and multiple international conferences where he has served as General Chair, Industrial Chair, Program Chair, and Organizing Chair. His research encompasses deep learning, digital watermarking, image forensics, signal processing, marine AI applications, smart digital-twin systems, cybersecurity, and intelligent multi-agent architectures. With approximately 118 published research documents, around 1,309 citations, and an estimated h-index in the mid-20s, he maintains a strong and influential academic footprint. Over his career, he has been honored with numerous Best Paper Awards, national recognitions, institutional leadership awards, and international distinctions that reflect his outstanding dedication to research excellence, innovation leadership, and the advancement of technology-driven societal development.

Profile: Scopus

Kuruba Chandrakala | Machine Learning and Statistics | Best Researcher Award

Dr. Kuruba Chandrakala | Machine Learning and Statistics | Best Researcher Award

Siddhartha Academy of Higher Education | India

Dr. Kuruba Chandrakala is an emerging researcher in the domains of computer vision, deep learning, and medical image processing, currently serving as Assistant Professor (Selection Grade) in the CSE department at Siddhartha Academy of Higher Education, Vijayawada. She earned her Ph.D. from NIT Tiruchirappalli, preceded by M.Tech in Computer Science and Engineering with distinction from JNTU Kakinada and B.Tech in the same discipline from JNTU Anantapur. She has qualified both NET and APSET examinations. Her professional trajectory includes roles as Head of Department (CSE-AIML) at Vignan’s Nirula Institute of Technology & Science for Women and previous teaching appointments at VNITSW and SITAM, along with industry experience as a System Engineer with Tata Consultancy Services. Her publication record comprises five Scopus indexed papers, four of which are in SCIE journals, two IEEE conference papers, and one book chapter; she also holds one patent. Her Scopus metrics include an h-index of 4, 10 documents, and 150 citations. Her research has addressed areas such as diabetic retinopathy segmentation, robust blood vessel detection, and image enhancement through deep learning architectures. She teaches courses including Deep Learning, Machine Learning, Big Data Analytics, Cloud Computing, and programming in C, C++, Java, and Python. She has earned numerous certifications from NPTEL, Coursera, Microsoft, IBM, and Wipro and received awards such as the NPTEL Discipline Star and Wipro Project Excellence Award. Her leadership and mentoring roles include serving as a mentor for Wipro TalentNext, nodal officer for Microsoft Upskilling and APSCHE virtual internship programs, and coordinator for various hackathons. She is a life member of professional bodies such as CSI, ISTE, IAENG, and IET, and has delivered several invited and guest lectures, contributing significantly to academic excellence and research advancement.

Profiles: Scopus Google Scholar Orcid

Featured Publications

Chandrakala, K., & Gopalan, N. P. (2025). 3DECNN: A novel method for segmentation of diabetic retinopathy in retinal fundus images using 3D-edge CNN. Neural Computing and Applications.

Kuruba, C., Sharmila, S. K., Mounika, V., Aswini, D., & Poojitha, G. (2023). Three layered security model to prevent credit card fraud using LBPH and CNN-ResNet architecture. International Conference on Hybrid Intelligent Systems, 422–428.

Dharmaiah, K., Mebarek-Oudina, F., Sreenivasa Kumar, M., & Chandra Kala. (2023). Nuclear reactor application on Jeffrey fluid flow with Falkner-Skan factor, Brownian and thermophoresis, non-linear thermal radiation impacts past a wedge. Journal of the Indian Chemical Society, 100(2), 117.

Kuruba, C., & Gopalan, N. P. (2023). Robust blood vessel detection with image enhancement using relative intensity order transformation and deep learning. Biomedical Signal Processing and Control, 86, 105195.

Kuruba, C., Pushpalatha, N., Ramu, G., Suneetha, I., Kumar, M. R., & Harish, P. (2023). Data mining and deep learning-based hybrid health care application. Applied Nanoscience, 13(3), 2431–2437.