Cohort 2024

PhD researchers who joined the Practice-Oriented AI CDT in the 2024 cohort.

Berenika Ewart-James

Berenika Ewart-James

Berenika investigates how simulations can be used to personalise physical activity plans using minimal user data, targeting scalable and privacy-conscious health interventions.

Supervisor: Prof. Raul Santos-Rodriguez
School of Engineering Mathematics and Technology

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Bezawit Tilaye

Bezawit Tilaye

Bezawit focuses on the evaluation and mitigation of cognitive biases in AI-assisted decision-making, with the aim of developing responsible AI systems that better support human judgment and decision-making.

Supervisor: Dr. Telmo de Menezes e Silva Filho
School of Engineering Mathematics and Technology

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David Fox

David Fox

David focuses on developing techniques that generate high-quality interpolated video frames while maintaining computational efficiency, with potential impact in media production and video streaming.

Supervisor: Dr. Aaron Zhang
School of Computer Science

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Jack Woodcock

Jack Woodcock

Jack evaluates how effectively ML-based climate models capture extreme weather events, examining the models’ understanding of the underlying physics to offer insights into their predictive capabilities and limitations for real-world climate forecasting.

Supervisor: Dr. Peter Watson
School of Geographical Sciences

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Jake Chastney

Jake Chastney

Jake is building a reasoning pipeline that combines neural and symbolic methods over knowledge graphs, with mechanisms for empirical rule validation and probabilistic inference, aimed at complex decision-making tasks.

Supervisor: Dr. James Cussens
School of Computer Science

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James Brock

James Brock

James investigates how to improve forest change analysis for practitioners through vision foundation models and interactive agents, aiming to enhance the utility, accuracy, and usability of forest monitoring tools for conservation efforts.

Supervisor: Dr. Nantheera Anantrasirichai School of Computer Science

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Jono Vincent

Jono Vincent

Jono explores how adversarial strategies develop in the game of Go, aiming to classify and analyse these behaviours. The project contributes to improving the robustness and interpretability of AI in strategic games.

Supervisor: Dr. Telmo de Menezes e Silva Filho
School of Engineering Mathematics and Technology

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Moses Guran

Moses Guran

Moses introduces a novel video compression method inspired by neural spiking behaviour. The project aims to reduce data rates while preserving quality, opening new directions in biologically inspired video coding.

Supervisor: Dr. Aaron Zhang
School of Computer Science

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