Presentations

• “An Ensemble-Based Approach for Sample-Efficient Deep Reinforcement Learning from Self-Demonstration” (poster) – AgriFoRwArdS CDT Annual Conference 2024: Robots in Action [July 2024] – Norwich, UK.
• “Beyond 2D images” (oral) – AgriFoRwArdS CDT Summer School: Robotic Phenotyping [July 2024] – Wageningen, The Netherlands.
• “Coastal Surveying with a Hyperspectral Camera” (poster) – AgriFoRwArdS CDT Summer School: Robotic Phenotyping [July 2024] – Wageningen, The Netherlands.
• “Automating the Clock Drawing Test with Deep Learning and Saliency Maps” (oral) – EPIA Conference on Artificial Intelligence 2024 [September 2024] – Viana do Castelo, Portugal.

About me

My name is Violet, I am originally from Brighton, and I am joining the CDT in 2023. My primary research interests are in machine learning, explainable AI, and robot vision. Currently I am particularly engaged with deep feature extraction. Prior to joining the CDT, I studied an MComp (Integrated Master’s in Computing Science) at the University of East Anglia where I will be returning to complete my PHD. 

I chose to join the CDT because of my interests in machine learning research intersecting with robotics and how this can have a profound impact on the challenges currently facing agricultural production. I specifically wanted to study my PHD at UEA due to its expertise within the context of the CDT principally being in computer vision and artificial intelligence which I have particular interest in. 

MSc Project

An Ensemble-Based Approach for Sample-Efficient Deep Reinforcement Learning from Self-Demonstration

Deep reinforcement learning is an important field within autonomous robotics. This project aims to implement a proposed ensemble-based model for deep reinforcement learning and compare it with a set of established algorithms in a series of simulated robotic environments. To ensure portability, this will be implemented using the unified structure of the Stable-Baselines3 library.

PhD Project

Title to be confirmed