JEONJU · KOREA · EST. 2026
Physical
AI Lab
Bridging machine intelligence and the physical world — embedded systems, autonomous robotics, and real-time AI at the edge.
AFFILIATED INSTITUTIONS
Research Areas
01
🤖Embodied Intelligence
AI that learns through physical interaction and sensorimotor feedback loops.
02
🦾Autonomous Robotics
Robots that can operate independently in complex environments.
03
🤝Human-Robot Interaction
Designing safe, intuitive interfaces between people and robotic systems.
04
📡Sensor Fusion & Perception
Integrating data from multiple sensors to improve perception accuracy.
05
🌐Digital Twin & Simulation
Virtual replicas of physical systems for analysis and optimization.
06
⚡Edge AI & TinyML
Deploying machine learning models on edge devices for real-time inference.
07
⚙️Industrial Applications
Applications of AI in industrial settings for automation and optimization.
08
🔒Safety & Ethics
Ensuring the safe and ethical deployment of AI systems.
09
🗃️Open Datasets
Curated datasets for physical AI research released openly.
10
📖Open Curriculum
Accessible educational resources for learning about AI and robotics.
Research
A Low-Cost Sentinel-1 SAR and Google Earth Engine Pipeline for Flood Detection in Mid-Size Cities: Validation on the July 2023 Jeonju Flood Event
AI-Enhanced Cell-Free CRISPR Diagnostics for Decentralized Biosensing
Design and Implementation of a Low-Cost IoT-Based Smart Irrigation System with Real-Time Monitoring
Design and Performance Characterization of a Low-Cost Rough Vacuum System for Hands-On Semiconductor Education
Machine Learning Prediction of Melting and Boiling Points of Elements Using Atomic Descriptors
Machine Learning Prediction of Melting and Boiling Points of Elements Using Atomic Descriptors
Parametric CFD Analysis of Throat Height and Diffuser Angle in a Simplified Ground-Effect Race Car Floor
Projects
A Context-Aware Korean Slang and Idiom Dataset
A dataset of 189 Korean slang phrases with appropriate English translations was created and labeled with sentiment, int...
Normalized Handwritten Hangul Graphemes Dataset (NHHG) + Pangrams
89,100 handwritten Hangul graphemes (2,200 per consonant, 1,100 per vowel grapheme) were collected from 110 freshmen un...
KSPAI — Korean Society for Physical AI (founding)
Founding member and organiser of the Korean Society for Physical AI (한국피지컬AI학회). The society promotes research exchange...
Manchu Ancient Script OCR Dataset (MASD)
[Our paper](https://doi.org/10.56977/jicce.2024.22.1.80) demonstrated that it is possible to semi-manually gather ancie...
Handwritten Cherokee Script OCR Dataset (CSD)
[Our paper](https://doi.org/10.56977/jicce.2024.22.1.80) demonstrated that it is possible to semi-manually gather ancie...
Curriculum
Four structured tracks from first principles to advanced robotics. Fully bilingual EN / 한국어.
Foundations of Physical AI
Sensors, actuators, and the full AI stack from first principles. No prior ML experience required.
8 units EN · 한국어Edge AI and Embedded Systems
TinyML, RTOS, and real-time inference on constrained hardware. Covers Arduino, ESP32, and Raspberry Pi.
10 units EN · 한국어Robotic Systems and Control
Kinematics, ROS 2, and perception–action loops. Build and program a 4-DOF robot arm from scratch.
12 units EN · 한국어Datasets and Benchmarks
Lab-produced open datasets for Physical AI research. All released under CC-BY 4.0.
3 units EN · 한국어Course plug-in modules
Physical AI add-ons for courses not primarily focused on AI — drop into any syllabus in 1–3 weeks.
AI-aware memory management
Efficient C++ patterns for inference workloads — stack vs. heap, arena allocators, and avoiding dynamic memory on bare-metal targets.
3 weeksSensor fusion fundamentals
Kalman filters and multi-sensor pipelines — plug into any IoT course to add a Physical AI dimension.
2 weeksTime-series data for physical systems
InfluxDB and real-time sensor logging — adds a physical AI data layer to any database course.
2 weeksNeural network hardware mappings
How circuit theory underlies Physical AI acceleration chips — MAC arrays, memory bandwidth, and power analysis.
1 weekNotes
RESEARCH OPPORTUNITIES
Work with the lab
I work with motivated undergraduates on conference papers, open datasets, and Physical AI projects — Arduino robotics, edge ML, embedded systems. No prior AI background required. If you have a capstone project with potential, or just want to learn by building something real, reach out.
About
PRINCIPAL INVESTIGATOR
The Physical AI Lab is a research group based in Jeonju, Korea, working at the intersection of machine learning, embedded hardware, and physical systems.
I teach across five Korean universities and run an open research practice — papers, datasets, and code are published wherever possible. I'm also a founding organiser of KSPAI (한국피지컬AI학회), the Korean Society for Physical AI.
Wyoming native. Laid back. Will talk robotics over coffee.
AFFILIATED INSTITUTIONS