서승호 연구실

In the dynamic domain of multimodal learning, my research endeavors revolve around synergizing information from diverse sources, including video data, time-series sensor data, and language data. By developing innovative techniques, I aim to create a comprehensive world model that enhances our capacity to interpret and understand complex environments through multiple modalities. This interdisciplinary approach not only facilitates nuanced insights into human activities, as captured by wearable sensors, but also extends to the broader spectrum of multimedia data, contributing to advancements in areas such as computer vision, natural language processing, and the seamless integration of multimodal information for a more comprehensive understanding of the world. In the domain of Predictive Maintenance for industrial machines and Lithium-ion batteries, my research is dedicated to advancing the reliability and efficiency of critical systems. Specifically, I focus on developing cutting-edge techniques for fault detection and diagnosis, with a particular emphasis on identifying issues in machine components like bearings. Additionally, my work extends to the realm of Lithium-ion batteries, where I specialize in predicting the remaining useful life of these crucial energy storage devices. By leveraging data-driven approaches and advanced algorithms, I strive to provide actionable insights that empower industries to proactively address potential failures, optimize maintenance schedules, and ultimately enhance the overall performance and longevity of both industrial machinery and energy storage systems. [05.2026] We will organize the second GenAI4HS (Generative AI and Foundation Models for Human Sensing Workshop) at UbiComp 2026, in Shanghai, China. [08.2025] Dr. Sungho Suh has been