CV

Robotics & ML Engineer

SAIF Autonomy

• Developed MVP for safety-critical assurance layer (ASTM F3269-21) for autonomous systems, designing system architecture and data flow.
• Engineered real-time multi-objective optimization algorithms and deterministic models for constrained autonomous navigation.
• Prototyped a real-time privacy censorship system for autonomous drones using geometric computer vision.
• Conducted exploratory research on Deep Reinforcement Learning (PPO) for control of complex satellite systems.

Research Intern

Bristol Robotics Lab

• Led the design of an autonomous drone featuring a novel tendon-actuated perching mechanism for forest monitoring.
• Developed 3D perception and navigation pipelines using depth cameras, YOLOv8-based detection, and VIO SLAM.
• Integrated and tested hardware (NVIDIA Jetson Orin Nano, FCU) through real-world perching experiments.
• Work culminated in a publication at IROS 2025.

Research Intern

University of Cambridge

• Developed a state-of-the-art Braille-reading robot achieving 315 WPM at 87.5% accuracy, published in IEEE-RAL.
• Built a real-time tactile perception pipeline using PyTorch autoencoders, YOLOv8 detection, and NLP error correction.
• Designed the robotic system and tactile sensing setup, integrating a UR3 arm with a DIGIT sensor.

R&D Software Intern

Hexagon

• Developed ML models in PyTorch to predict gearbox component safety factors with <1% error.
• Automated data gathering and model creation by integrating Python and C++ APIs with proprietary packages.
• Designed prototype software and a custom clustering algorithm to enhance gearbox durability predictions.

Distinction with Honours - Double 1st (Top 20%)

University of Cambridge

MEng in Information & Computer Engineering

• Thesis: Uncertainty in Navigation (Computational Neuroscience)

University of Cambridge

BA in Aerospace Engineering

High-Speed Tactile Braille Reading via Biomimetic Sliding Interactions

Tendon-driven Grasper Design for Aerial Robot Perching on Tree Branches