Christopher Lin

Student builder · Robotics · Baseball

I build systems that leave the screen.

I'm Christopher Lin. I like projects where code has to handle motion, sensors, hardware, and feedback from the real world: small robots learning to coordinate, a pitching motion becoming data, and prototypes that have to work beyond a clean demo.

Explore my workAbout me

The common thread is testing an idea, watching what changes, and adjusting until the system tells the truth.

Christopher working at a desk with swarm robot prototypes and electronics parts.
A workbench where simulation, solder, sensors, and code meet.

Robotics

A swarm robotics project exploring whether simple agents can coordinate through signals left in a shared environment instead of central commands.

The project began with carpenter ants and a question about indirect coordination: what happens if each robot only sees nearby signals, but the group shares a changing memory of where it has been? I built the idea first in simulation, then carried it into tracked physical robots.

Question
Can a group coordinate without a central controller?
Build
Train the behavior in simulation, then test the idea in hardware.
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Tracked swarm robot prototypes on a workbench with electronics hardware in front.
Moving out of simulation meant dealing with sensors, motors, timing, and the limits of physical parts.

Baseball as an engineering problem

Pitching Biomechanics

A baseball problem turned into a wearable-sensor and machine-learning project for studying pitching motion.

Pitching made the question personal: could low-cost sensors capture enough movement to make useful feedback more accessible? I connected IMUs to an Arduino, collected wireless throw data, and trained a sequence model to study how motion changes across a pitch.

Explore project
Question
Could a pitching motion become useful low-cost feedback?
Build
Wearable sensors, wireless data collection, and a sequence model.
Christopher wearing wired motion sensors while holding a baseball during throwing-data collection.
The collection setup was intentionally simple: wearable sensors, a ball, and repeated throws.
Two time-series charts showing hip IMU acceleration and angular velocity during a throw.
Each throw became a time-series signal with a visible burst around the pitching motion.

Baseball

On the field, feedback is immediate.

Baseball is where feedback feels fastest. A pitch misses by inches, a swing is late by a fraction, and the only useful response is to notice, adjust, and try again. Playing competitive travel baseball with UBC Junior Thunder has shaped how I work on technical problems too: prepare carefully, stay calm when the result is imperfect, and look for the smallest change that can make the next attempt better. Pitching also gave me one of my favorite engineering questions: how could motion, sensors, and software make athletic feedback more accessible?

Baseball and engineering are not separate tracks for me. They are both ways of learning from the real world.

Christopher standing on a baseball pitching mound in a UBC Thunder uniform before delivering a pitch.
Focus before motion: reading the field, holding the next decision.
PitchingDecision-makingTeamwork
Christopher sprinting out of the batter's box after making contact while a catcher and umpire stand behind home plate.
Action after contact: the same feedback loop, just faster.
UBC Thunder Junior PBL baseball team posing in uniform on a baseball field with coaches.
Team context matters: individual work still has to serve the group.

About

I learn by making ideas testable.

I started with software because it made ideas feel reachable. Over time, I wanted those ideas to leave the screen, so I moved toward electronics, sensors, robots, and machine learning systems that have to respond to messy physical inputs.

Right now I am most curious about small autonomous systems, machine learning that touches the physical world, and better ways to measure motion without expensive labs.

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