Kyongsik Yun is a technologist at NASA's Jet Propulsion Laboratory (JPL) and a senior member of the American Institute of Aeronautics and Astronautics (AIAA). His research focuses on brain-inspired autonomous systems and multi-modal heterogeneous time series modeling, advancing machine learning in computer vision and natural language processing. Kyongsik has contributed to the Department of Defense (DoD) and Department of Homeland Security (DHS) through significant development and implementation of deep learning technologies. His work includes flight software engineering for the Mars Sample Return (MSR) mission’s sample retrieval lander and current projects focused on onboard software implementation for next-generation avionics hardware, including Snapdragon and HPSC.

Kyongsik also serves as a lead developer for SLIM (Software Lifecycle Improvement & Modernization), an open-source initiative by NASA's Advanced Multi-Mission Operations System (AMMOS) for automated integration of software best practices using large language models. SLIM has made impactful improvements across numerous NASA projects, such as MGSS, HySDS, F Prime, Opera, and SDS.

Caltech Computation & Neural Systems Postdoc; KAIST Computational Neuroscience BS & PhD

• Flight Computing and Avionics  | Flight Software Tools And Analysis Technologies
• Artificial Intelligence, Machine Learning, and Data Science  | Deep Learning And Data Analytics
• Autonomous Systems  | Anomaly Detection And Recovery

• JPL Voyager Award | Software Lifecycle Improvement & Modernization (SLIM) using large language models (2024)
• JPL Voyager Award | Multimodal data fusion and time series forecasting for groundwater in California's Central Valley (2022)
• NASA Award | NASA Innovator | Creating knowledge graphs for supporting future human space exploration (2022)
• JPL Explorer Award | Deep learning computer vision system for DHS (2019)
• JPL Voyager Award | Real-time data analytics system for Kennedy Space Center (2018)