Senior R&D Engineer · Eyebot · Boston
Bringing machine
vision into focus.
I build the perception and calibration systems inside eye-scanning hardware — stereo depth, infrared photoretinoscopy, and the unglamorous measurement work that turns a camera into an instrument a clinic can trust.
Work
01 — ProfessionalSenior R&D Engineer
Eyebot
- Designed and shipped the eyescanner's pupillary distance measurement system from scratch on stereo-depth cameras, with an ML calibration model mapping camera geometry to a pixel-to-millimetre scale — 0.57 mm mean error, a ≈3× accuracy improvement, deployed fleet-wide.
- Developed the facetracking algorithm — real-time face detection and 3D localization on Luxonis OAK-D stereo cameras — driving closed-loop linear and angular motor control to align the optics to a user's eyes, with an open-loop feed-forward stage when camera feedback drops out.
- Built the world's fastest binocular lensometer from scratch, including the core lens border-detection and progressive-lens detection algorithms, extracting SPH, CYL, AXIS and PD from a user's glasses.
- Developed the infrared photoretinoscopy pipeline — pupil detection, slope extraction, real-time prescription — and a diopter calibration curve that cut prescription error ≈30% below the prior production system.
- Created the internal factory test suite through which all kiosk calibration and bring-up is performed, now standard across manufacturing; owned the full release lifecycle.
R&D Engineer
Eyebot
- Optimized lens-scanning software in Python for a significant increase in processing speed, and implemented calibration routines for the eye- and lens-scanning hardware.
- Designed and fabricated mechanical calibration "eyes" via rapid prototyping, and built a reusable repeatability-testing framework used to validate the eye- and lens-scanning fleet.
R&D Engineering Intern
Eyebot
- Performed data analysis on eye data from clinic operations to identify and correct errors, improving the accuracy and reliability of Eyebot's equipment.
Robotics Research Assistant
New York University · AI4CE Lab
- Designed and integrated a PCB with an NVIDIA Jetson to unify the systems of a mobile 3D-printing robot.
- Managed the design and creation of a multi-DOF robotic arm, enabling optimized control for 3D printing.
- Built a simulation environment in Gazebo, Python and ROS to validate the robot's systems.
About
02 — BackgroundI work where cameras stop being cameras and start being instruments. That means stereo geometry, infrared optics, and calibration — the part of computer vision where a number has to be right, not merely plausible, because somebody is going to wear the answer on their face.
At Eyebot I own the measurement stack of a self-service eye exam: finding a face in 3D and driving motors to meet it, reading a prescription out of infrared reflections, and pulling SPH, CYL and AXIS out of a pair of glasses. Most of the work is proving the thing is accurate — mechanical calibration eyes, repeatability rigs, clinical studies, and the factory tests that let a kiosk leave the building.
Before that: a master's at NYU in mechatronics and robotics, research on mobile 3D-printing robots at the AI4CE Lab, and a mechatronics degree in Auckland. The projects below are from that period — controls, perception and a fair amount of building things that move.
- Now
- Senior R&D Engineer, Eyebot — Boston, MA
- Education
- MS Mechatronics & Robotics, NYU — GPA 3.97, 2023
BE (Hons) Mechatronics, Auckland — First Class Honours, 2020 - Languages
- Python
- Vision & ML
- OpenCV, PyTorch, TensorFlow — detection & segmentation, stereo depth, camera calibration
- Hardware
- NVIDIA Jetson, Raspberry Pi, Luxonis OAK-D — RS485 / I²C / UART
- Tools
- ROS, Gazebo, Google Cloud, Git