KY Kyongsik Yun, Ph.D.

01  /  Bio

Technologist at NASA's Jet Propulsion Laboratory, California Institute of Technology. Senior member of AIAA. I build brain-inspired technologies and systems — spanning deep-learning computer vision, natural-language processing, brain–computer interfaces, and multi-modal time-series modeling.

Recent work includes leading development of SLIM (Software Lifecycle Improvement & Modernization), NASA's open-source initiative for automated integration of software best practices using large language models; flight software engineering for the Mars Sample Return sample retrieval lander; and onboard software for next-generation avionics (Snapdragon, HPSC).

Awards include the JPL Voyager Award (2024, 2022, 2018), NASA Innovator Award (2022), JPL Explorer Award (2019), Marie Curie Fellowship for Neuroscience, IFMBE Young Investigator Award, and the SfN Hot Topic Award. I 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.

Brain-Inspired Autonomous Systems

Systems that emulate neurological principles — from event-driven sensing to neuromorphic control for spacecraft and first-responder robotics.

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).

Selected Awards

  • 2024JPL Voyager Award — SLIM with large language models
  • 2022JPL Voyager Award — Multimodal data fusion, California Central Valley groundwater
  • 2022NASA Innovator Award — Knowledge graphs for human space exploration
  • 2019JPL Explorer Award — Deep-learning computer vision for DHS
  • 2018JPL Voyager Award — Real-time data analytics for Kennedy Space Center

03  /  News

Selected press, talks, and milestones.

NASA JPL Technology Highlights

NASA JPL Technology Highlights

Remote estimation of geologic composition selected as one of JPL's Technology Highlights.

JPL TALENTS 2021

JPL TALENTS 2021

Featured for the development of a soil-composition estimation model.

Multi-Agent Motion Planning

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

GSAW Best Presentation Award 2019

Transforming unstructured data into insight for anomaly detection in exploration ground systems.

JPL Explorer Award 2019

JPL Explorer Award 2019

Scientific and technical excellence in deploying new technologies for operations at Kennedy Space Center.

Beer Goggles for Your Brain

Beer Goggles for Your Brain

Electronic scalp stimulation makes faces appear more attractive (New Scientist).

Seoul Digital Forum

Seoul Digital Forum

Wearable technology for Alzheimer's disease.

Wearable for Alzheimer's Patients

Wearable for Alzheimer's Patients

Brain signal-processing platform and wearable device, with large-scale Class III medical device trials.

Social Synchronicity

Social Synchronicity

A measurable connection between bonding and matched movements.

04  /  Projects & Publications

Grouped by research area.

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.

Mars Sample Return & Next-Generation Avionics

Flight software · Snapdragon & HPSC

  • Flight software engineering for the MSR sample retrieval lander.
  • Onboard software implementation for next-generation avionics (Snapdragon, HPSC).

Groundwater & Land Deformation Models

InSAR · groundwater depth · precipitation

Groundwater and land deformation
  • Yun, K., Kim, K., Pradhan, A., Reager, J., Liu, Z., Turmon, M., Huyen, A., Lu, T., Chandrasekaran, V., Stuart, A. "Filling the gap: Estimation of soil composition using InSAR, groundwater depth, and precipitation data in California's Central Valley." AGU, 2021.
  • Pradhan, A., Holder, D., Kim, K., Yun, K., Liu, Z., Reager, J., Turmon, M., Chandrasekaran, V., Stuart, A. "Spatio-temporal Gaussian Process Modeling of Land Subsidence with Well Water Level and InSAR Data." AGU, 2021.

Deep Space Network — Time-Series Compression & Anomaly Detection

telemetry · anomaly detection · comparison

Deep Space Network time series
  • Yun, K., Verma, R., Rebbapragada, U. "Time Series Comparisons in Deep Space Network." AIAA-ASCEND, 2021.
  • Monitoring spacecraft data to detect anomalies. 2025.

Wildfire Segmentation, Tracking & Forecasting

multi-sensor · self-supervised ML · UAS

Wildfire segmentation
  • LaHaye, N., Yun, K., et al. "Self-supervised ML for smoke-plume and active-fire identification from the FIREX-AQ dataset." 2025.
  • LaHaye, N., Yun, K., Lee, H., Goodman, A., Gorski, K., Garay, M. J., Kalashnikova, O. "Identification of wildfires and smoke plumes using multi-sensor input and unsupervised/supervised ML." AGU, 2021.
  • 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." arXiv:1911.08288, 2019.

Multi-Spacecraft Trajectory Planning (Deep Learning)

formation flying · constraint-informed learning

Multi-spacecraft trajectory planning
  • Constraint-informed learning for warm-starting trajectory optimization. 2025.
  • Sabol, J., Yun, K., Adil, M., Choi, C. "Machine Learning Based Relative Orbit Transfer for Formation Flying Swarms of Spacecraft." 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, p. 4233.

Entry, Descent & Landing (EDL)

GNC & LVS on multi-core processors

  • Evaluation of GNC and LVS deployment on next-generation multi-core processors for EDL operations. 2025.

Deep Learning Hardware Acceleration

memory-centric architectures · space autonomy

Deep learning hardware acceleration
  • Yun, K. "Accelerating autonomy in space using memory-centric architectures for deep neural networks." 2019.

AI for First Responders

fire/smoke detection · AR · speech recognition

AI for first responders
  • 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 Pattern Recognition & Tracking XXXII, vol. 11735, 2021.
  • Yun, K., Lu, T., Huyen, A. "Transforming unstructured voice and text data into insight for paramedic emergency service using RNNs/CNNs." SPIE vol. 11400, 2020.
  • Lu, T., Huyen, A., Nguyen, L., Osborne, J., Eldin, S., Yun, K. "Optimized training of DNNs for image analysis using synthetic objects and augmented reality." SPIE vol. 10995, 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 adversarial networks." SPIE vol. 10995, 2019.
  • 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 vol. 10995, 2019.
  • Yun, K., Huyen, A., Lu, T. "Deep neural networks for pattern recognition." arXiv:1809.09645, 2018.
  • Yun, K., Osborne, J., Lee, M., Lu, T., Chow, E. "Automatic speech recognition for launch control center communication using RNNs with data augmentation and custom LM." SPIE vol. 10652, 2018.
  • Yun, K., Lu, T., Chow, E. "Occluded object reconstruction for first responders with AR glasses using conditional GANs." SPIE vol. 10649, 2018.
  • Yun, K., Bustos, J., Lu, T. "Predicting rapid fire growth (flashover) using conditional GANs." Electronic Imaging 2018 (9): 127-1.
  • Yun, K. "AI for the future of first responders." 2017.

Machine Learning for Medical Data

clinical prediction · imaging · surveys

ML in medical data
  • 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 8 (2021): 406.
  • 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 7 (2020): 767.
  • Oh, J., Yun, K., Chae, J.-H., Kim, T.-S. "Association Between Macronutrients Intake and Depression in the United States and South Korea." Frontiers in Psychiatry 11 (2020): 207.
  • 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 11 (2020): 16.
  • 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 257 (2019): 623–631.
  • 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 8 (2017): 192.

Neuropsychiatric Treatment via Neuromodulation

tACS · tDCS · clinical mechanisms

Neuromodulation
  • Tavakoli, A. V., Yun, K. "Transcranial alternating current stimulation (tACS) mechanisms and protocols." Frontiers in Cellular Neuroscience 11 (2017): 214.
  • 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.
  • Chib, V.*, Yun, K.*, Takahashi, H., Shimojo, S. "Noninvasive remote activation of the ventral midbrain by tDCS of prefrontal cortex." Translational Psychiatry, 2013.

Neural Signal Processing

EEG · BCI · neural synchrony

Neural signal processing
  • 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 31, 13 (2020): 991–998.
  • Kim, H., Kralik, J. D., Yun, K., Chung, Y., Jeong, J. "Neural correlates of public apology effectiveness." Frontiers in Human Neuroscience 13 (2019): 229.
  • Yun, K., Stoica, A. "Improved target recognition response using collaborative brain–computer interfaces." IEEE SMC, 2016.
  • 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.
  • Lee, J., Yun, K. "Alcohol reduces cross-frequency theta-phase gamma-amplitude coupling in resting EEG." Alcoholism: Clinical & Experimental Research 38, 3 (2014): 770–776.
  • Yun, K. "On the same wavelength: face-to-face communication increases interpersonal neural synchronization." Journal of Neuroscience, 2013.
  • Yun, K., Watanabe, K., Shimojo, S. "Interpersonal body and neural synchronization as a marker of implicit social interaction." Scientific Reports, 2012.

05  /  Outreach & Contact

Talks, open positions, and collaboration.

Speaking & Press

Invited talks and media appearances have included the Seoul Digital Forum, New Scientist features on noninvasive brain stimulation, and JPL TALENTS.

Open Positions

Postdoctoral and research opportunities at JPL are posted on my open positions page and via LinkedIn.

Contact

kyongsik.yun@jpl.nasa.gov
Google Scholar · LinkedIn · JPL profile