Channing Tan
Robotics & Perception Engineer
Developing autonomous systems through ROS2-based control architectures, computer vision pipelines, and machine learning systems. Experienced in real-time perception, embedded controls, and signal processing for robotics applications.
About
I'm a robotics engineer specializing in autonomous systems, perception pipelines, and real-time control systems. My work spans computer vision, machine learning infrastructure, and embedded systems integration.
Education
The University of Tennessee, Knoxville
B.S. Electrical Engineering + B.A. Physics (May 2026)
GPA: 3.83 / 4.00
⚡ Completed dual degree program in 2.5 years
Experience
Oak Ridge National Laboratory — Computational Urban Sciences Group
Jun 2025 - Aug 2025
- Benchmarked spatiotemporal forecasting methods using PyTorch and TensorFlow
- Developed graph neural network encoding methods for large-scale spatial forecasting
- Reduced forecasting RMSE by 6.7% through architecture improvements
- Presented research at Smoky Mountain Conference 2025
My engineering approach emphasizes interdisciplinary collaboration, systems-level thinking, and practical implementation. I'm driven by the challenge of building reliable autonomous systems that bridge theoretical concepts with real-world deployment.
Featured Projects
Software and machine learning systems for robotics, perception, and signal processing
NASA Lunabotics Competition
Developed ROS2-based control architecture for autonomous lunar mining robot. Implemented multiplexer nodes, motor controller interfaces over CAN, and integrated Isaac ROS VSLAM with stereo camera pipelines for autonomous navigation.
🏆 14th out of 47 qualifying teams at UCF competition
Traffic Accident Detection & Localization
Multi-stage computer vision pipeline for traffic accident classification and spatiotemporal localization. Implemented 3D CNN architectures and integrated YOLO-based vehicle detection with temporal analysis.
Spiking Neural Networks for ASL Recognition
Developed convolutional spiking neural network for American Sign Language recognition using neuromorphic event-camera data. Implemented leaky integrate-and-fire neuron architectures with SNNTorch.
AI-Driven FIR Filter Design
Researched machine learning approaches for FIR digital filter optimization. Implemented XGBoost and Random Forest classifiers achieving 95% feasibility prediction accuracy with gradient-based coefficient optimization.
Hardware & Prototyping Experience
Building reliable systems from concept to competition. Hands-on experience with electrical design, embedded integration, and hardware debugging.
NASA Lunabotics Competition
Controls Systems Lead
Tennessee Lunabotics
Integrated embedded systems for autonomous lunar mining robot. Managed communication between Jetson Orin Nano, ESP32 microcontrollers, and motor drivers for competition deployment.
Key Work:
- ▸Configured Jetson Orin Nano environment with Isaac ROS integration
- ▸Developed CAN bus and UART communication pipelines
- ▸Integrated motor driver systems (MD20A, Spark MAX)
- ▸System-level debugging and validation for competition reliability
- ▸Team placed 14th out of 47 qualifying teams at UCF
FRC Competition Robotics
Electrical Systems Lead
Team 3140
Designed and maintained electrical systems for FRC competition robots. Implemented redundancy strategies that achieved zero electrical failures during competition operation.
Key Work:
- ▸Electrical system architecture and power distribution design
- ▸Motor controller integration (Spark MAX, RoboRIO)
- ▸Sensor wiring, signal conditioning, and PCB design
- ▸Competition debugging and rapid troubleshooting
- ▸Team achieved 2nd place alliance captain placement (2024 season)
Technical Skills
Core competencies across robotics, software, and hardware engineering
Robotics & Autonomy
Programming Languages
Machine Learning & Perception
Controls & Signal Processing
Embedded Systems & Hardware
Tools & Platforms
Resume
Download or view my full resume
Quick Summary
Education
B.S. Electrical Engineering + B.A. Physics
University of Tennessee, Knoxville (May 2026)
Experience
Oak Ridge National Laboratory — Computational Urban Sciences (Summer 2025)
NASA Lunabotics Competition — Controls Lead (2024-2026)
Focus Areas
Robotics • Computer Vision • Machine Learning • Controls • Embedded Systems
Get In Touch
Interested in robotics, autonomy, or embedded systems? Let's connect.
Knoxville, TN