Deploying Real-Time Face Recognition Across Multiple Cameras

• Trained and deployed face detection and feature extraction models to production.

• Created APIs and inference pipelines using Triton Inference Server and TensorRT.

• Designed a video stream processing system handling input from multiple cameras.

• Increased vector search performance 20x by optimising FAISS-based similarity search.

• Built dockerised infrastructure for scalable deployment.

As a computer vision expert, I was consulted to address the challenge of facial pore recognition for a beauty application. The problem involved accurately identifying and measuring individual pores on various skin types and tones. To tackle this, I designed an algorithm that could adaptively capture high-resolution images and extract relevant features. Leveraging machine learning, I trained a model on a comprehensive dataset that I helped gather, comprising different face images under various lighting conditions. The resulting algorithm could recognize and measure individual pores with high precision, providing personalized skincare recommendations to users, and thereby enhancing the app's user experience.

With a 9-year tenure as a freelance Audio Technology Consultant, I crafted a wide array of technical solutions. These encompassed the development and implementation of new real-time audio effects for Arm Cortex M4 hardware, the creation of VST plugins with the JUCE framework, the design and integration of a real-time processing system using Beagle Bone Black and the Bela realtime platform, as well as contributions in musical sound synthesis and Machine Learning consultancy for an interactive musical project.

Over the last two years, I've been dedicated to promoting the integration of AI among scientists at the globally acclaimed marine research institution, Plymouth Marine Laboratory (PML). PML's core research program is focused on critical areas such as climate change, marine pollution, and sustainability. In a recent research study published in July 2023, I developed Machine Learning approaches to ascertain the primary factors influencing the phytoplankton carbon-to-chlorophyll ratio in the Atlantic Basin.These methods provided valuable insights into the environmental factors affecting phytoplankton carbon and chlorophyll levels in the corresponding figures.

Led a team of 3 developers in designing and maintaining RuBackup’s software ecosystem.

Designed and implemented a modular Python library for backup integrations, now published on PyPi.

Released the first stable version of the web interface and REST API for RuBackup’s backup control system.

Developed and deployed an autoregressive model using statsmodels to predict remaining storage capacity.

Formalised internal coding standards, GitLab practices, and documentation processes to improve code quality and maintainability.