Published in BioMedEng19, 2019
This is work I completed during my time as a Biomedical Engineering student. We developed a low-cost, highly effective and reliable systems for monitoring the breathing rate of anesthetized rodents. We open-sourced this system, and the code and other files are provided here. Link to Code
Published in The Multi-disciplinary Conference on Reinforcement Learning and Decision Making, 2022
In this conference submission, I outline some preliminary results showing the efficacy of using successor representations that represent the expected state dynamics given an action has a finite horizon (a numver of action repeats), which is also generated under some policy. Link to Paper
Published in Workshop on Symmetry and Geometry in Neural Representations, NeurIPS 2022, 2022
This workshop paper demonstrates a method for compressing representations of expected state dynamics into a hierarchy of successor representations, using the geometric decomposition. Link to Paper
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Undergraduate course, Imperial College London, Department of Bioengineering, 2019