[Turing-Southampton] VLC seminar - With Dr Sanmitra Ghosh on Wednesday 21 November @ 2pm

[Susan Davies]

***APOLOGIES IF YOU RECEIVE THIS MORE THAN ONCE***

Dr Sanmitra Ghosh from University of Cambridge speaking next Wednesday 21 November.

Event details:
Uncertainty and variability is intrinsic to a plethora of biological processes that we want to understand, model and predict. In cardiac modelling, sources of uncertainty stem from the experimental error in the measurements from our protocols, lack of knowledge about the underlying mechanisms leading to structural error in our models, variability due to differences in cell and ion channel states due to cells being in different settings and gene expression patterns, and variability due to the inherent stochasticity of some of these processes exhibited at multiple time and spatial scales. To accommodate mathematical/phenomenological models in safety-critical clinical practice and drug development, it is therefore of utmost importance to quantify and propagate these uncertainties to model predictions. Bayesian statistics plays a major role in carrying out uncertainty quantification effectively. However, cardiac models pose a unique set of challenges for Bayesian statistical methods. In this talk I would present Bayesian statistical and modern machine learning approaches towards "forward" (from inputs to model predictions) and "inverse" (from experimental data to model structure) uncertainty quantification in cellular cardiac electropysiological models. Specifically, I would present approaches to overcome the computational and statistical challenges associated with uncertainty quantification in mechanistic models, described by differential equations, and highlight some of the open challenges. Furthermore, I would discuss the potential of modern machine learning techniques such as black-box variational inference and probabilistic programming towards solving the uncertainty quantification problem efficiently.

Following are the references accompanying this talk:

1) Sanmitra Ghosh, David Gavaghan, Gary Mirams, "Gaussian process emulation for discontinuous response surfaces with applications for cardiac electrophysiology models", https://arxiv.org/abs/1805.10020v1<https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Farxiv.org%2Fabs%2F1805.10020v1&data=01%7C01%7C%7C5d1691c2f72045bf4d2208d64b9a5e94%7C4a5378f929f44d3ebe89669d03ada9d8%7C1&sdata=wtMyTxvA6L%2FPoxP2rYdzsyEDdqtvlW1j8uOnZENkp7M%3D&reserved=0>

2) Sanmitra Ghosh "Probabilistic Programming for Mechanistic Models (P2M2) tutorial repository", https://github.com/sanmitraghosh/P2M2<https://emea01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fsanmitraghosh%2FP2M2&data=01%7C01%7C%7C5d1691c2f72045bf4d2208d64b9a5e94%7C4a5378f929f44d3ebe89669d03ada9d8%7C1&sdata=g5HpaH%2Bep5cAngiouM3rPO6UKOwbHLQeo%2FYcz%2F2ajQ4%3D&reserved=0>

Speaker information:
Sanmitra obtained an MSc in System on Chip from the University of Southampton in 2011, following which he pursued doctoral research in the School of Electronics and Computer Science, University of Southampton and received a PhD in Electrical & Electronics Engineering in 2016. He was supervised by Dr Srinandan Dasmahapatra and Professor Koushik Maharatna. His doctoral research focused on the application of Gaussian processes for accelerating the ABC-SMC algorithm for learning dynamical systems and applied this method to fit plant electrophysiological models to experimental data. Following his PhD he joined as a postdoc at the Computer Science Department, University of Oxford. His research focus at Oxford was on developing methods to carry out inference, uncertainty quantification and experimental design for cardiac electrophysiological models. Sanmitra has recently joined the MRC Biostatistics Unit, University of Cambridge as a MRC postdoc fellow and will be working on the Bayes4Health project (http://www.lancaster.ac.uk/newbayes/<https://emea01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.lancaster.ac.uk%2Fnewbayes%2F&data=01%7C01%7C%7C5d1691c2f72045bf4d2208d64b9a5e94%7C4a5378f929f44d3ebe89669d03ada9d8%7C1&sdata=RnRLsLVNlvj1kbIna1mLRrO5eSm%2F0cLPOAaIDJ%2BoFw8%3D&reserved=0>).

Venue & Info:
Date: 21 November (Wednesday)
Time: 2-3pm
Venue: Building 32 Room 3077


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