Research · i2bl Lab, UCLA
Ferrofluid Computer Vision
Real-time vision for closed-loop magnetic control · Nov 2025 – Present
I'm developing a real-time computer-vision system that segments and localizes ferrofluid flow and magnetic sources from a live camera feed, then closes the loop, feeding those detections back to algorithmically place the magnetic field and steer ferrofluid trajectories across a microfluidic chip.
Computer VisionReal-time InferenceClosed-loop ControlMicrofluidics
The loop
Vision in, magnetic field out
The system runs as a perception-to-actuation loop: see the fluid and the sources, decide where the field should go, move it, and watch the result, continuously.
Sense
Live Camera
microfluidic chip
Segment
CV Model
ferrofluid + sources
Localize
Spatial Detect
positions in frame
Actuate
Field Placement
algorithmic
Result
Trajectory
controlled flow
Contributions
What I'm building
- Trained a real-time CV model to segment and localize ferrofluid flow and magnetic sources from live video.
- Integrated the spatial detections into an algorithm that adjusts magnetic field placement for precise correction and control.
- Targeting reliable control of ferrofluid trajectories on microfluidic chips.
Stack
Tools
PythonComputer VisionReal-time SegmentationMachine LearningClosed-loop ControlMicrofluidics
Have a demo clip or figure from the lab? It can drop right in here.