DPhil in Computational Discovery

The DPhil in Computational Discovery is a multidisciplinary programme spanning projects in Advanced Molecular Simulations, Machine Learning and Quantum Computing to develop new tools and methodologies for life sciences discovery.

Applications must be received by 12 midday (UK time) on Friday 3 June 2022.

Project 1: Defining computation and connectivity in neuronal population activity underlying motor learning.

PI: Andrew Sharot

Motivated by recent work that link structure of population activity to the underlying synaptic connectivity (Dahmen et al., 2022) and our experience in cortical microcircuits (Peng et al., 2019, 2022), we aim to identify core changes in neuronal microcircuits that underlie motor learning and execution. We will develop novel approaches to extract activity signatures reflecting plastic changes on the local synaptic level and model how these constrain the overall dimensionality of neuronal population activity. The results will provide a microcircuit level understanding of learning in motor circuits and lay the groundwork to study neural network architecture in high-density electrophysiological recordings.

Project 2: Optimising therapy for brain disorders through AI-refined deep brain stimulation

PI: Hayriye Cagnan

Brain stimulation is extensively used to modulate neural activity in order to alter behaviour. In recent years, closed-loop stimulation techniques have gained increasing traction to sense a biomarker such as elevated neural activity patterns, and deliver stimulation in time with such events. Closed-loop stimulation techniques are used both for establishing a causal link between behaviour and neural activity, and also to treat various neurological and psychiatric conditions. Building on our recent work (West et al 2022, Cagnan et al 2017), this PhD project aims to formalise stimulation parametrisation by using theoretical models of brain circuits in combination with state of the art machine learning approaches. Specifically, we will train artificial neural networks to classify discrete brain states of interest and optimise stimulation parameters to achieve precise manipulation of activity propagating across brain circuits. The successful development of such an approach would provide a powerful framework to guide next generation stimulation strategies both for usage in basic science and clinical applications.

Project 3: Foundations of Stochastic Gradient Descent (and Generalization)

PI: Patrick Rebeschini

Stochastic gradient descent is one of the most widely used algorithmic paradigms in modern machine learning. Despite its popularity, there are many open questions related to its generalization capabilities. For instance, while there is preliminary evidence that early-stopped gradient descent applied to over-parameterized models is robust with respect to label mispecifications, a complete theory that can account for this phenomenon is currently lacking. The goal of this project is to rigorously investigate the robustness properties of early-stopped gradient descent from a theoretical point of view in simplified settings involving linear models, and to establish novel connections of such a methodology with the field of distributionally-robust optimization. The project will combine tools from the study of random structures in high-dimensional probability (e.g., concentration inequalities, theory of optimal transport) with the general framework of gradient and mirror descent methods from optimization and online learning (e.g., regularization).

Please contact the Theme Lead Phil Biggin if you have any questions about these projects.

Further details about the programme and information on how to apply can be found here.

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