Hi, my name is Zerun! I’m a sophomore in Electrical Engineering at the Georgia Institute of Technology, with interests in robotics, AI/ML, and embedded systems. I will geek out with you for hours if you want to talk about building robots that can sense, learn, and adapt to the world we live in, optimizing control systems for autonomous machines, or why the intersection of hardware and intelligence is where the real magic happens.
More About Me
For me, robotics is the most beautiful expression of engineering, where circuits, code, and motion come together to create something alive. When my robot dog struggles to stand, or when a PID loop oscillates, it’s not a failure, but feedback. Building robots is how I explore how intelligence takes form in motion.
My journey began with VEX Robotics. Started as a team member, I focused on debugging drive systems and tuning PID parameters. Over time, I grew into a team captain and later coached two teams in my high school. Leading design brainstorming, setting up testing frameworks, and teaching basic control algorithms and robotics structures to younger students inspired me to envision a future where robots communicate and collaborate, just like how modules are now combined. My focus shifted from individual subsystems to complete architectures, studying how sensing, control, and communication interact as one coherent system. I’ve contributed to the development of neural networks that can predict mechanical failures and to the advancement of robots that can perceive environments under low-light conditions. What mattered most wasn’t just performance metrics, but making the systems robust enough to operate in the real world, under constraints.
At Georgia Tech, I am fortunate to have access to a broader and more powerful platform to continue my focus on system-level engineering. In the Yellow Jacket Space Program, I work on the avionics of a student-built rocket, where every circuit, signal, and command must perform flawlessly under high-G acceleration, vibration, and temperature extremes. At HyTech Racing, I assist in refining the power-sensing and data systems of an FSAE electric racing car that balances both speed and safety demands. Both projects push me to engineer with an even smaller tolerance for error, translating my robotics mindset into systems that must operate perfectly in unforgiving environments. Alongside these team projects, I’m developing a quadruped robot that combines embedded control with reinforcement learning on my own, serving as a research platform for future work, such as perception and navigation.
Skills
Programming & Frameworks
- Languages: Python, C++, Rust
- ML/AI Frameworks: PyTorch, YOLOv5
- Embedded Platforms: Arduino, Raspberry Pi, ESP32
Robotics & Control Systems
- Control Algorithms: PID Optimization, Autonomous Navigation
- Hardware Integration: Multi-sensor Fusion, Communication Protocols
AI & Machine Learning
- Computer Vision: Real-time Object Detection
- Deep Learning: Neural Network Optimization, Bayesian Optimization
- Reinforcement Learning: Deep RL, MuJoCo Simulation
Hardware Design
- PCB Design: KiCad, Altium Designer
- CAD & 3D Modeling: SolidWorks, Shapr3D
Leadership & Collaboration
- Team Leadership: Led teams of 6–15 members across robotics, space settlement design, and academic projects
- Mentorship & Teaching: Coached robotics teams, conducted weekly tutorials, compiled educational materials
- Project Management: Supervised cross-functional teams, organized inter-school competitions
Languages
- English (Professional), Chinese (Native)
Career Goals
- Advance General-Purpose Robotics — Contribute to embodied AI and adaptive robotic systems that can operate in diverse real-world environments
- Bridge Research and Industry — Translate cutting-edge robotics research into practical, market-ready solutions
- Master AI and Control Systems — Deepen expertise in reinforcement learning, computer vision, and intelligent control algorithms
- Lead Impactful Innovation — Develop technologies that meaningfully improve how we live and work, from space exploration to autonomous systems
I believe we’re at a critical moment in robotics: AI’s transition from experimental stages to real-world deployment and improved hardware, yet fundamental challenges remain — particularly robot-relevant data scarcity, the sim-to-real transfer gap, and the need for safety guarantees in closed-loop deployment. Emerging solutions, including world foundation models and advances in high-fidelity simulation, are beginning to address these bottlenecks, making this an exciting inflection point for the field.
After graduating in December 2028 with a B.S. in Electrical Engineering (focus: Robotics and Signal Processing, AI minor), I plan to join industry immediately to work on embodied AI and autonomous systems. I’m particularly drawn to companies or startups that balance innovative research with product development, where I can contribute to both the technical challenges of robot learning and the practical considerations of deployment.