Brain-Inspired Autonomous Systems
Systems that emulate neurological principles — from event-driven sensing to neuromorphic control for spacecraft and first-responder robotics.
Kyongsik Yun, Ph.D.
01 / Bio
Kyongsik Yun is a technologist at NASA's Jet Propulsion Laboratory, California Institute of Technology, 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. He has contributed to the Department of Defense and Department of Homeland Security through development and deployment of deep-learning technologies.
His current work includes flight software engineering for the Mars Sample Return mission's sample retrieval lander, and onboard software for next-generation avionics hardware (Snapdragon and HPSC). He also leads development of 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 — deployed across MGSS, HySDS, F Prime, Opera, and SDS.
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Kyongsik studied computational neuroscience at KAIST (BS, PhD) and computation & neural systems at Caltech (postdoc). His awards include the JPL Voyager Award, NASA Innovator Award, JPL Explorer Award, Marie Curie Fellowship for Neuroscience, IFMBE Young Investigator Award, and the SfN Hot Topic Award. He co-founded two biotechnology companies — Ybrain and BBB Technologies — which raised $45M in investment funding.
02 / Research Focus
Three areas. One through-line: brain-inspired computation at scale.
Multi-Modal Time-Series Modeling
Fusing heterogeneous streams (InSAR, groundwater, telemetry, DSN) into unified forecasting and anomaly-detection pipelines.
Machine Learning Applications
Deep-learning computer vision and NLP — applied to wildfire tracking, Mars EDL, medical classification, and large-scale software engineering (SLIM/LLMs).
03 / News
Selected press, talks, and milestones.
NASA JPL Technology Highlights
Remote estimation of geologic composition selected as one of JPL's Technology Highlights.
JPL TALENTS 2021
Featured for the development of a soil-composition estimation model.
Multi-Agent Motion Planning using Deep Learning
Deep-learning numerical model demonstrating 1000× the computational efficiency of the mathematical counterpart.
GSAW Best Presentation Award 2019
Transforming unstructured data into insight for anomaly detection in exploration ground systems.
JPL Explorer Award 2019
Scientific and technical excellence in deploying new technologies for operations at Kennedy Space Center.
Beer Goggles for Your Brain
Electronic scalp stimulation makes faces appear more attractive (Science / New Scientist).
Seoul Digital Forum
Wearable technology for Alzheimer's disease.
Wearable for Alzheimer's Patients
Brain signal-processing platform and wearable device, with large-scale Class III medical device trials (TechCrunch).
Social Synchronicity
A measurable connection between bonding and matched movements (Scientific Reports).
04 / Projects & Publications
Grouped by research area.
Full list with citation counts and PDFs on Google Scholar. Each title below links to the paper on Scholar.
Software Lifecycle Improvement & Modernization (SLIM)
NASA AMMOS · automated integration of software best practices using LLMs
- Lead developer. Deployed across MGSS, HySDS, F Prime, Opera, and SDS. site
- JPL Voyager Award (2024) — SLIM using large language models.
Next-Generation Avionics
Onboard flight software · Snapdragon & HPSC
- Onboard software implementation for next-generation avionics hardware (Snapdragon, HPSC).
- Yun, K., Bayard, D., Kubiak, G., Owens, A., Johnson, A., Johnson, R., Scharf, D., Lu, T. “Enhancing fault-tolerant space computing: GNC and Landing Vision System (LVS) implementations on next-gen multi-core processors.” IEEE Space Computing Conference, 2025.
Space Autonomy & Mission Operations
trajectory optimization · DSN anomaly detection · hardware acceleration
- Briden, J., Choi, C., Yun, K., Linares, R., Cauligi, A. “Constraint-informed learning for warm-starting trajectory optimization.” Journal of Guidance, Control, and Dynamics, 2025.
- Yermakov, A., Yun, K., Ratliff, L., Kutz, J. N. “A transformer-based deep learning approach to anomaly detection of high-bandwidth multivariate time-series satellite communications.” SpaceOps, 2025.
- Sabol, A., Yun, K., Adil, M., Choi, C., Madani, R. “Machine learning based relative orbit transfer for swarm spacecraft motion planning.” IEEE Aerospace, 2022.
- Yun, K., Choi, C., Alimo, R., Davis, A., Forster, L., Rahmani, A., Adil, M., Madani, R. “Multi-agent motion planning using deep learning for space applications.” ASCEND, 2020.
- Yun, K., Verma, R., Rebbapragada, U. “Time series comparisons in Deep Space Network.” AIAA-ASCEND, 2021.
- See more on Google Scholar →
Earth Science — Wildfire, InSAR & Groundwater
multi-sensor ML · self-supervised segmentation · land deformation
- LaHaye, N., Easley, A., Yun, K., Lee, H., Linstead, E., Garay, M. J., et al. “Development and application of self-supervised ML for smoke-plume and active-fire identification from FIREX-AQ.” Remote Sensing, 2025.
- LaHaye, N., Lee, H., Easley, A., Garay, M. J., Yun, K., Goodman, A., et al. “A comparison of model complexity, representative capabilities, and performance for self-supervised multi-sensor wildfire and smoke segmentation and tracking.” AGU, 2024.
- Yun, K., Adams, K., Reager, J., Liu, Z., Chavez, C., Turmon, M., Lu, T. “Remote estimation of geologic composition using InSAR in California's Central Valley.” arXiv, 2022.
- Stavros, E. N., Agha, A., Sirota, A., Quadrelli, M., Ebadi, K., Yun, K. “Smoke Sky — exploring new frontiers of unmanned aerial systems for wildland fire science and applications.” arXiv:1911.08288, 2019.
AI for First Responders
fire/smoke detection · AR · speech recognition · collision warning
- Yun, K., Huyen, A., Lu, T. “Deep neural networks for pattern recognition.” arXiv:1809.09645, 2018.
- Do, V., Huyen, A., Joubert, F. J., Gabriel, M., Yun, K., Lu, T., Chow, E. “A virtual assistant for first responders using natural language understanding and optical character recognition.” SPIE, 2022.
- Huyen, A., Yun, K., De Baun, S., Wiggins, S., Bustos, J., Lu, T., Chow, E. “Dynamic fire and smoke detection and classification for flashover prediction.” SPIE, 2021.
- Yun, K., Nguyen, L., Nguyen, T., Kim, D., Eldin, S., Huyen, A., Lu, T., Chow, E. “Small target detection for search and rescue using distributed deep learning and synthetic data generation.” SPIE, 2019.
- Yun, K., Yu, K., Osborne, J., Eldin, S., Nguyen, L., Huyen, A., Lu, T. “Improved visible-to-IR image transformation using synthetic data augmentation with cycle-consistent GANs.” SPIE, 2019.
- See more on Google Scholar →
Medical AI & Psychiatry
clinical prediction · imaging · surveys · COVID-19
- Oh, J., Oh, B.-L., Lee, K.-U., Chae, J.-H., Yun, K. “Identifying schizophrenia using structural MRI with a deep-learning algorithm.” Frontiers in Psychiatry, 2020.
- Oh, J., Yun, K., Hwang, J.-H., Chae, J.-H. “Classification of suicide attempts through a machine-learning algorithm based on multiple systemic psychiatric scales.” Frontiers in Psychiatry, 2017.
- Oh, J., Yun, K., Maoz, U., Kim, T.-S., Chae, J.-H. “Identifying depression in NHANES using a deep-learning algorithm.” Journal of Affective Disorders, 2019.
- Yun, K., Oh, J., Hong, T. H., Kim, E. Y. “Prediction of mortality in surgical intensive care unit patients using machine learning algorithms.” Frontiers in Medicine, 2021.
- Yun, K., Lee, J. S., Kim, E. Y., Chandra, H., Oh, B.-L., Oh, J. “Severe COVID-19 illness: risk factors and its burden on critical care resources.” Frontiers in Medicine, 2020.
- See more on Google Scholar →
Brain Stimulation & Neuromodulation
tACS · tDCS · clinical mechanisms
- Tavakoli, A. V., Yun, K. “Transcranial alternating current stimulation (tACS) mechanisms and protocols.” Frontiers in Cellular Neuroscience, 2017.
- Chib, V., Yun, K., Takahashi, H., Shimojo, S. “Noninvasive remote activation of the ventral midbrain by tDCS of prefrontal cortex.” Translational Psychiatry, 2013.
- Yun, K., Song, I.-U., Chung, Y. A. “Changes in cerebral glucose metabolism after 3 weeks of noninvasive electrical stimulation of mild cognitive impairment patients.” Alzheimer's Research & Therapy, 2016.
- Song, M., Shin, Y., Yun, K. “Beta-frequency EEG activity increased during tDCS.” Neuroreport, 2014.
Social & Cognitive Neuroscience
EEG hyperscanning · BCI · neural synchrony · decision-making
- Yun, K., Watanabe, K., Shimojo, S. “Interpersonal body and neural synchronization as a marker of implicit social interaction.” Scientific Reports, 2012.
- Chung, D., Yun, K., Jeong, J. “Decoding covert motivations of free riding and cooperation from multi-feature pattern analysis of EEG signals.” Social Cognitive & Affective Neuroscience, 2015.
- Yun, K. “On the same wavelength: face-to-face communication increases interpersonal neural synchronization.” Journal of Neuroscience, 2013.
- Yun, K., Bhattacharya, J., Sandkühler, S., Lin, Y.-J., Iwaki, S., Shimojo, S. “Causally linking neural dominance to perceptual dominance in a multisensory conflict.” NeuroReport, 2020.
- Kim, H., Kralik, J. D., Yun, K., Chung, Y., Jeong, J. “Neural correlates of public apology effectiveness.” Frontiers in Human Neuroscience, 2019.
- See more on Google Scholar →