Department of Aerospace Engineering & Robotics Vehicle Optimization, Dynamics, Control and Autonomy Lab• Developed an LSTM-accelerated Time Shift Governor (DL-TSG) for spacecraft rendezvous and docking in SpaceX Crew3 mission and elliptical orbits, implementing a phase-adaptive sliding window and a mission-specific loss function, ensuring precise time shift predictions and adherence to space mission constraints• Developed and implemented a TSG-guided MPC-CBF, enhancing ACC system safety and efficiency by dynamically adjusting reference trajectories to account for unpredictable lead vehicle behaviors and obstacle avoidance, while reducing computational costs- “Deep Learning-accelerated Time Shift Governor for Spacecraft Proximity Operations”, AIAA SCITECH 2025 Forum, To be presented at SciTech 2025

Department of Aerospace Engineering & Robotics Distributed Autonomous Systems and Control Lab• Developed and implemented the Probabilistic Ensemble Neural Network (PENN) for real-time adaptation of Input Constrained Control Barrier Functions (ICCBF).• Designed and integrated a two-step uncertainty verification process using Jensen-Rényi Divergence (JRD) and distributionally robust Conditional Value at Risk (CVaR) to ensure model confidence and local validity.• Conducted simulations and robot experiments for robot navigation to validate the method’s effectiveness and safety guarantees.

Department of Computer ScienceSoft Computing Lab• Developed control and vision algorithms for a mobile robot to autonomously deploy a multi-modal deep learning-based network for gas leakage detection in an industrial factory• Implemented prototypical transformer network based few-shot learning on in-vehicle noise classification• Presented novel in-vehicle noise classification deep learning model using dynamic prototype-guided memory replay method - “Dynamic Prototype-guided Memory Replay for In-Vehicle Noise Classification”, Korea Data Mining Society (KDMS 2023), SAS Student Paper Award (Honorable Mention)- “Disentangled Prototyping with Triplet-trained Prototypical Network for Few-shot Learning in In-vehicle Noise Classification”, IEEE Access, 2024

Artificial Intelligence Robot InstituteAssistive and Interactive Robotics Lab• Improved wearable hip complex assistive robot (MoonWalk) with 4DOF active joint• Modeled a - estimation deep learning model using MoonWalk for - estimation based - identification - "Gait Phase Estimation Method Adaptable to Changes in Gait Speed on Level Ground and Stairs", The Journal of Korea Robotics Society, 2023

1st Fighter WingHeadquarter Intelligence Agency• Honorable discharge, distinguished recognition in marksmanship (top-ranked)• Collected and analyzed information related to potential threats to national security.• Provided intelligence briefings to commanders and other personnel, contributing to the overall situational awareness and decision-making processes.

Department of Physical EducationIntegrative Sports Science Research Lab• Initiated and developed a portable real-time ankle angle analysis audio-visual feedback system• Validated IMU measurement of joint kinematics with vicon system using a developed wearable device • Conceptualized subtalar joint angle prediction algorithm with learning method (random forest) - “Measurement of ankle joint movements using IMUs during running”, Sensors, 2021

School of Mechanical EngineeringResearch lab of Manufacturing Mechatronics• Investigated the feasibility of a linear motor active damper to deal with vibration on the entire frequency• Actual experiments applied to 1DOF and 2DOF structures which achieved vibration reduction approximately 63.7% for 1DOF and an average of 52% for 2DOF structures

Department of Mechanical EngineeringInnovative Design and Integrated Manufacturing Lab• Developed lab automation using an autonomous mobile manipulator• Integrated communication nodes of lab facilities and the robot with ROS• Customized modular mechanical end effector with torque and position controller• Devised low-cost appropriate robotic manipulator(Open quasi direct drive robot) - International S.M.A.R.T Startup Competition, 1st Place, Innovative Technology and Energy Center

School of Mechanical EngineeringKnowledge Based Design Lab• Established hardware and control system of smart lock-out tag-out IoT system to the engineering plant• Integrated real-time cloud-based controller and P&ID VR system with Unity - “An Intelligent Lock-Out Tag-Out System for Monitoring and Control of the Locked Device”, IEEE International Conference on Industrial Engineering and Engineering Management 2019, Poster session

School of Mechanical EngineeringNano Electromechanical Device Lab• Participated in a lab experiment in the process and testing of making Multi-layered Fresnel lens-shaped etched graphene.- “Manufacturing Method of Multi-Gas Sensor Using Ultra-Thin Film Lens”, Korean patent