We propose an expressive and efficient approach that combines the strengths of two prominent extensions of Graph Neural Networks (GNNs): Subgraph GNNs and Structural Encodings (SEs). Our approach leverages walk-based centrality measures, both as a powerful form of SE and also as a subgraph selection strategy for Subgraph GNNs.

Work which explores how virtual nodes effect oversquashing and node heterogeneity for Graph Neural Networks

Collaboration with the AIDOS lab at Helmholtz Munich. Exploring the stability and expressivity of discrete curvature on graphs, and combining it with topological data analysis to obtain robust, expressive descriptors for Graph Generative Model Evaluation.

Collaboration with the Correia lab at EPFL developing better metrics for protein generation

Collaboration with the Correia lab at EPFL developing a model to addresses the backbone designability problem by using protein sketch templates

Collaboration with the Correia lab at EPFL and the Baker Lab describing an approach to simultaneously scaffold multiple functional sites in a single domain protein using deep learning

using biological networks to map the space of food molecules and provide genomic-driven nutritional interventions to radiotherapy-resistant rectal cancer patients

Collaboration with the Correia lab at EPFL. Creating a physics-informed deep neural network for rigid-body protein docking

The first hire at this London based startup in October 2018. I have been working as a Machine Learning Researcher building emotion recognition technologies using data collected from a wearable device. We have implemeted this technology as a real-time product which increases data quality in mental health treatment.

Used tools from Information Theory (PMIME) to create a time-varying causal network between EEG time series. I then used network science approaches, particularly the Shannon entropy of the distribution of community sizes, to analyze differences in brain function for musicians when they are either improvising or playing a set piece.

Presented at NetSci 2019.

Extended work on emotion recognition from medical grade ECG devices to PPG collected from a smartwatch. Made statistical comparisons of the ECG and PPG time-series and used Transfer Learning to try and improve model performance.

Poster presented at ACII 2019.

Bayesian deep learning for emotion recognition. Used an LSTM-FCN to improve state of the art accuracy for predicting emotional valence from unimodal heartbeat data. Incorporated Bayesian considerations into the model using Monte-Carlo dropout for uncertainty quantification and to tune the accuracy and coverage.