<html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns:m="http://schemas.microsoft.com/office/2004/12/omml" xmlns="http://www.w3.org/TR/REC-html40">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=us-ascii">
<meta name="Generator" content="Microsoft Word 15 (filtered medium)">
<style><!--
/* Font Definitions */
@font-face
{font-family:"Cambria Math";
panose-1:2 4 5 3 5 4 6 3 2 4;}
@font-face
{font-family:Calibri;
panose-1:2 15 5 2 2 2 4 3 2 4;}
/* Style Definitions */
p.MsoNormal, li.MsoNormal, div.MsoNormal
{margin:0cm;
margin-bottom:.0001pt;
font-size:12.0pt;
font-family:"Times New Roman",serif;}
a:link, span.MsoHyperlink
{mso-style-priority:99;
color:blue;
text-decoration:underline;}
a:visited, span.MsoHyperlinkFollowed
{mso-style-priority:99;
color:purple;
text-decoration:underline;}
p.msonormal0, li.msonormal0, div.msonormal0
{mso-style-name:msonormal;
mso-margin-top-alt:auto;
margin-right:0cm;
mso-margin-bottom-alt:auto;
margin-left:0cm;
font-size:12.0pt;
font-family:"Times New Roman",serif;}
span.apple-converted-space
{mso-style-name:apple-converted-space;}
span.EmailStyle19
{mso-style-type:personal-reply;
font-family:"Calibri",sans-serif;
color:windowtext;}
.MsoChpDefault
{mso-style-type:export-only;
font-size:10.0pt;}
@page WordSection1
{size:612.0pt 792.0pt;
margin:72.0pt 72.0pt 72.0pt 72.0pt;}
div.WordSection1
{page:WordSection1;}
--></style><!--[if gte mso 9]><xml>
<o:shapedefaults v:ext="edit" spidmax="1026" />
</xml><![endif]--><!--[if gte mso 9]><xml>
<o:shapelayout v:ext="edit">
<o:idmap v:ext="edit" data="1" />
</o:shapelayout></xml><![endif]-->
</head>
<body lang="EN-GB" link="blue" vlink="purple">
<div class="WordSection1">
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">***APOLOGIES IF YOU RECEIVE THIS MORE THAN ONCE***<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p> </o:p></span></p>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">Dr Sanmitra Ghosh from University of Cambridge speaking next Wednesday 21 November.<o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> <o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">Event details:<span class="apple-converted-space"> </span></span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">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.<o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> <o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">Following are the references accompanying this talk:<span class="apple-converted-space"> </span><o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> <o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">1) Sanmitra Ghosh, David Gavaghan, Gary Mirams, “Gaussian process emulation for discontinuous response surfaces with applications for cardiac electrophysiology models”,<span class="apple-converted-space"> </span><a href="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"><span style="color:#954F72">https://arxiv.org/abs/1805.10020v1</span></a><o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> <o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">2) Sanmitra Ghosh “Probabilistic Programming for Mechanistic Models (P2M2) tutorial repository”,<span class="apple-converted-space"> </span><a href="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"><span style="color:windowtext">https://github.com/sanmitraghosh/P2M2</span></a><o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> <o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">Speaker information:</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">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 (<a href="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"><span style="color:#954F72">http://www.lancaster.ac.uk/newbayes/</span></a>).<o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> </span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">Venue & Info:</span></b><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"><o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">Date: 21 November (Wednesday)<o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">Time: 2-3pm<o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif">Venue: Building 32 Room 3077<o:p></o:p></span></p>
</div>
<div>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri",sans-serif"> <o:p></o:p></span></p>
</div>
<p class="MsoNormal"><o:p> </o:p></p>
</div>
</body>
</html>