Master Student, Soongsil University, South Korea
📘 Hyston Kayange is a Malawian researcher and IT professional currently pursuing his Master’s in Computer Science and Engineering at Soongsil University in Seoul, South Korea. With a strong foundation in IT and a passion for Machine Learning and Deep Learning, Hyston is dedicated to advancing recommender systems and computer vision applications. His research focuses on health fitness recommendation systems, aiming to personalize and improve exercise suggestions through advanced deep learning techniques.
Publication Profile
Google Scholar
Education
🎓 MS in Computer Science & Engineering Soongsil University, Seoul, South Korea (Aug 2022–Present) Specializing in personalized recommendation systems with a focus on fitness applications under the guidance of Prof. Jongsun Choi. BS in Computer Science
Daeyang University, Malawi (2017–2021) Developed the “MTHANDIZI” computer vision project, aimed at bridging communication between natural language speakers and the deaf.
Experience
💼 Assistant Researcher System Software Lab, Soongsil University, Seoul, South Korea (Sep 2022–Present) Conducted research on deep recommendation systems, focusing on adaptive feature selection for personalized recommendations in health and fitness. Developed a hybrid model using Neural Dynamic Bayesian Networks to enhance heart rate prediction accuracy. ICT Officer United Civil Servant SACCO, Malawi (2021–2022). Managed fintech systems, ensuring efficient operation and user support, along with network and server configuration.
Research Interests
🔍 Hyston’s research centers on Machine and Deep Learning applications, especially in developing recommendation systems. He aims to leverage neural networks to improve data mining, recommender systems, and computer vision tasks like object detection and assistive technologies, with a particular interest in adaptive feature selection for personalized fitness recommendations.
Awards
🏆 Hyston has presented his work at international conferences, including ICOIN 2024, where he shared insights on adaptive feature selection in deep recommendation systems.
Publications
ProAdaFS: Probabilistic and Adaptive Feature Selection in Deep Recommendation Systems
Authors: H. Kayange, J. Mun, Y. Park, J. Choi, & J. Choi
Journal: Learning font-style space using style-guided discriminator for few-shot font generation, 2024.
Cited by
Deep Adaptive Feature Selection in Deep Recommendation Systems
Authors: H. Kayange, A. Kumar, Y. Lee, H. Jung, J. Choi
Journal: Deep Recommendation Systems, 2023.
Cited by