Human-Machine Interfaces & Vision Systems for Robotics

• Led more than 5 research and development projects focused on building human-machine interfaces to control robotic systems via Android apps, WinForms applications, and console tools.

• Conducted research in robotic control and trajectory planning, contributing to academic advancements and real-world implementations.

• Worked on sensor fusion tasks involving PIR, sonar, camera, touch, and gyroscope inputs — integrating and processing data from multiple sources.

• Completed two projects using microcontrollers and single-board computers such as Arduino, PIC32, Raspberry Pi, and Jetson TK1.

• Delivered two working computer vision applications involving object recognition, emotion detection, and a “leader-following” system (robot tracking a moving subject).

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.

• Led development of face and license plate analytics across 4 engineering teams.

• Built ML models for driver identification using sensor data (RF, MLP, DT, KNN).

• Researched video-based action recognition (publication under review).

• Created a facial recognition API using FastAPI and OpenVINO Model Server.

• Supported academic research with practical deployments and experiments

• 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.