Projects · Angel Nieto Garcia

Things I've built

A selection of work spanning shipping Apple features, large-scale consumer and AR apps, and graduate machine-learning research.

PayPal In-App Browser

Led the Home, Shopping, and Rewards teams across Buy Now Pay Later, Analytics, Subscriptions, and PayPal+ in the UK, Germany, and US — 10+ engineers across multiple teams in the Consumer organization. Drove development of PayPal's new in-app browser: a seamless flow from an in-app offer to a merchant's storefront to checkout, with rewards and Pay Later applied inline. Enhanced core shopping flows across Android and iOS, ensuring reliability and performance at large consumer scale.

SwiftUI
iOS
Android
Consumer Apps
Leadership

iPhone Duo — Multi-IMU Camera Stabilization

Engineering DRI for a sensor-fusion feature that brought a second inertial measurement unit into the camera stabilization path on iPhone Duo — spanning a new private framework API, an extended sensor-to-ISP packet protocol, and a real-time fusion service on a low-power always-on coprocessor. Rather than duplicate the existing stabilization service, I made it a compile-time templated component parameterized over a configuration policy: a second instance costs a few dozen lines of configuration instead of a parallel implementation, with zero runtime dispatch and roughly 3 KB of additional coprocessor SRAM. Also built the debug tooling and field telemetry, integrated dual-gyro bias and in-field calibration, and drove it through cross-functional architecture review on schedule.

C++
Sensor Fusion
Firmware
Real-Time
Camera

Apple Watch Precision Finding

Multi-process, multi-threaded sensor-fusion pipeline letting an Apple Watch locate an iPhone via triangulation. Implemented moving/stationary and multi-floor detection, plus internal tools to log and replay finding sessions — cutting phone-search time by roughly a third.

Swift
Objective-C
Sensor Fusion
watchOS

AirPods Charging LED Detection

Designed and implemented a C++/assembly firmware framework for static and motion classification on custom Apple silicon, consumed by the Audio and AirPods teams. The framework provides device-state decisions at varying sample rates, enabling the Audio team to light the AirPods charging LED the correct color when the device is placed in its case.

C++
Assembly
Firmware
Embedded
Motion Classification

Camera Control Pocket Detection

Designed and implemented the pocket-detection algorithm and SPI for iPhone 16 / 16 Pro's Camera Control, deriving a low-power solution from Always On Display analytics to avoid accidental activations.

Algorithms
Low Power
CoreMotion
ML

Always On Display Pocket Detection

Designed and implemented the pocket-detection algorithm for iPhone's Always On Display feature — analyzing luminance and sensor-fusion data to suppress the display when the phone is pocketed, saving battery without sacrificing usability.

CoreMotion
Low Power
Algorithms
ML

Multi-Class Cancer Classification

Deep learning model for multi-class cancer classification from histopathology images, combining convolutional feature extraction with ensemble techniques to improve diagnostic accuracy across multiple cancer types.

PyTorch
Deep Learning
Computer Vision
Research

Royal Caribbean AR Experiences

Architected and built AR frameworks for Royal Caribbean cruises: marker-based games recreating classic fair and strategy games (Two70), and an X-ray experience streaming 360° captain's-view video when pointing an iPhone at markers on the ship's walls.

ARKit
Unity
Vision
Frameworks

Disney PhotoPass & Memory Maker

Developed and maintained Disney World / Disneyland app features: an Instagram-style PhotoPass gallery with photo editing, the shared Disney Core UI and services layer, and Memory Maker payments, transactions, and park ticketing.

iOS
Payments
Vision
Core Image

United Airlines Pilot Mobile

Built a Swift iPad scheduling app for 20,000 United Airlines pilots: a full-screen dashboard of current and upcoming flights, flight plans, vacations, airport door codes, delay notifications, bulletins, and crew information.

Swift
iPad
Enterprise

Motion Prediction for Autonomous Vehicles

Graduate deep-learning project: a VAE-LSTM model in PyTorch predicting vehicle motion, inspired by Social LSTM and Social GAN. Built a custom multi-agent dataset from Lyft Level 5 data feeding multiple agents' positions per frame.

Docs ↗Repo ↗

PyTorch
Deep Learning
Research