Josh Southern

Curvature Filtrations for GGME

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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.

Protein Docking

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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.

Music and the brain

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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.

Emotion from smartwatch data

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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

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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.

I'm currently a PhD student at Imperial College London, supervised by Prof. Michael Bronstein and Dr. Kirill Veselkov and part of the AI4Health CDT. I am interested in geometric deep learning, generative models and their applications (particularly in biology, healthcare and sport). I enjoy working on interdisciplinary projects and I am currently collaborating with Bruno Correia's lab at EPFL on protein design, as well as on a metabolomics + hyperfoods project with a team at Imperial.

Prior to my PhD, I did my Masters degree in Applied Mathematics, also at Imperial College London, and then spent two years as a Machine Learning Researcher at a Health Tech startup working with wearable device data, trying to benefit the mental health space. Outside of academia, I am obsessed with sport (particularly tennis), pubs and jazz. Feel free to reach out if you want to discuss ideas, have questions about my research or want to recommend places in London or Lausanne!

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