Learning and Mining from Multimodal DAtaset(LAMDA) Lab

Markov Decision Process(MDP) based Network Coding Scheme for Mobile Video Streaming over Unreliable Wireless Links - In mathematics, a Markov decision process (MDP) is a discrete-time stochastic control process. It provides a mathematical framework for modeling decision making in situations where outcomes are partly random and partly under the control of a decision maker. MDPs are useful for studying optimization problems solved via dynamic programming. MDPs were known at least as early as the 1950s; a core body of research on Markov decision processes resulted from Ronald Howard's 1960 book, Dynamic Programming and Markov Processes. They are used in many disciplines, including robotics, automatic control, economics and manufacturing. The name of MDPs comes from the Russian mathematician Andrey Markov as they are an extension of Markov chains. Jin-ah Park, Sungjin Park, Jooyoung Lim and Hayoung Oh, "Speak2UI: Voice-Driven Low-Level Mobile Interaction Using Context-Aware LLM Command Parsing", International Journal of Human-Computer Interaction, Sep.2026, I.F. = 5.5. Min Ah Kim, Jaehee Yi, Seong Young Lee, Chaerim Park, Mee Young Um, Karen Kyeunghae Lee and Hayoung Oh, "Using photovoice to understand experiences of young adults in recovery from substance use disorder in South Korea", JOURNAL OF ETHNICITY IN SUBSTANCE ABUSE, Jan.2026 (SCIE), I.F. = 1.2. Kyubum Hwang, Mirae Kim, Jiyoon Lee, Yuseon Jang, Kwangjune Choi, Junsang Yu, Jooyoung Lim and Hayoung Oh", Power Engineering Domain Pretrained Language Model with Natural Language Processing Benchmarks. COMPUTERS & ELECTRICAL ENGINEERING, May.2026 (SCIE), I.F. = 7.17. Hayeon Yang, Jiheun Hong, Seongjin Jo, Jooyoung Lim, Hayoung Oh, "GREEN: Generative Retrieval-Enhanced Emotional Support Conversations", SAGE Open , Nov.2025