• Healthcare condition monitoring using ICU data

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    Professor Chris Williams from the University of Edinburgh will discuss research aimed at improving ICU patient care using condition monitoring. This is often impeded by the presence of artifacts in the data; maintaining blood pressure in critically ill patients is a key management goal and yet it is the physiological variable most prone to error. Using data from vital signs data collected from the Neuro ICU at the Southern General Hospital, Glasgow, Chris will describe work on using the the Factorial Switching Linear Dynamical System (FSLDS) and the Discriminative Switching Linear Dynamical System (DSLDS) for the detection, removal and cleaning of artifacts. Chris will also present a non-linear dynamical system for modelling the effect of drug infusions on the vital signs of patients admitted in Intensive Care Units (ICUs). More specifically the work is interested in modelling the effect of a widely used anaesthetic drug called Propofolon a patient's monitored depth of anaesthesia and haemodynamics. The approach is compared with one from the Pharmacokinetics/Pharmacodynamics (PK/PD) literature. Joint work with: Konstantinos Georgatzis, Chris Hawthorne, Partha Lal, Martin Shaw, Ian Piper. Programme 6:30 Drinks and food 7:00 Introduction by QuantumBlack and talk by Professor Chris Williams 8:00 Questions + some more drinks 9:00-9:30 All done. Sponsor - QuantumBlack QuantumBlack is an advanced analytics firm operating at the intersection of strategy, technology & design to improve performance outcomes for organisations. We established QuantumBlack to re-imagine how organisations could continuously improve and out-learn their rivals. We want to change the world by solving one problem at a time and helping our clients use their own data to drive better decisions. Our mission is to help foster a culture of elite performance by eliciting the right input and enabling the right decision from the right data delivered in the right way. Acquired by McKinsey & Company in 2015, we were founded seven years ago to help our clients improve their asset, human, and operational performance by applying cutting-edge analytics to make the most of their data. With roots in Formula One, we now work across sector with some of the world's leading organisations in advanced industries, healthcare and finance. They are also hiring, so please check out their careers page: https://www.quantumblack.com/careers/

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  • The Bayesian Crowd; Scalable data combination for Citizen Science & Crowdfunding

    We're very pleased to announce our sixth meetup speaker, Stephen Roberts, Professor of Machine Learning at the University of Oxford and Faculty Fellow at The Alan Turing Institute. In Citizen Science and Crowdsourcing applications information from large numbers of agents need to be combined in an intelligent manner. For realistic deployment methods such combination should conform to optimality wherever possible, yet scale well with large numbers of information sources and amounts of data. This talk will focus on Bayesian information aggregation models; we discuss how the use of approximate inference, based on variational learning, allows excellent scaling properties whilst retaining high performance. We showcase the breadth of applicability of the approach with examples from large Citizen Science and Crowdsourcing domains, feedback and user-task allocation mechanisms. Programme 6:30pm: Arrive (drinks + food) 7:00pm: Talk 8:00 - 8:15pm: Questions 8:15 - 9ish: Finish off the food and drink! Professor Stephen Roberts: Stephen's main area of research lies in machine learning approaches to data analysis. He has particular interests in the development of machine learning theory for problems in time series analysis and decision theory. Current research applies Bayesian statistics, graphical models and information theory to diverse problem domains including astronomy, mathematical biology, finance and sensor networks. He leads the Machine Learning Research Group, is a Professorial Fellow of Somerville College, a Faculty fellow at the Alan Turing Institute, Director of the EPSRC Centre for Doctoral Training in Autonomous, Intelligent Machines and Systems (AIMS) and Director of the Oxford-Man Institute. Supporters Thanks once again to the Alan Turing Institute (https://turing.ac.uk) for supporting, and thanks to babylon health (https://www.babylonhealth.com/) for the food and drink. babylon health is a comprehensive, immediate and personalised health service that you can use at home, work or abroad so you can have peace of mind and know that safe medical advice is always to hand. Our platform pairs clinicians with the latest in technology, including cutting-edge AI, to help us deliver on our mission of providing accessible, affordable health care for everyone on earth.

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  • Bayesian Network Modelling: with Examples in Genetics and Systems Biology

    Bayesian Network Modelling: with Examples in Genetics and Systems Biology by Dr Marco Scutari. Programme: Arrive by 6:30, with food and drink served; 6:45ish: Introduction by venue and sponsor, followed by talk by Dr Marco Scutari. 8:00: Finish off the food and drink. Bio; Marco Scutari is a Lecturer in Statistics at the Department of Statistics, University of Oxford. He is the author and maintainer of the bnlearn R package, and of the book "Bayesian Networks in R: with Applications in Systems Biology" and "Bayesian Networks with Examples in R". His research focuses on the theory and computational aspects of Bayesian networks and their applications to genetics and systems biology. Sponsors; Thanks go to Ocado Technology (http://www.ocadotechnology.com/join-us) and the Alan Turing Institute (https://turing.ac.uk/) for supporting the event.

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  • Deep Nets, Bayes And The Story Of AI (Part 2)

    Lloyds Register

    We're lucky enough to have Dr David Barber from UCL giving us a second talk on the story of Deep Nets, Bayes And AI. There's no requirement to have attended the first talk, and this time round we'll be covering more in depth topics from an applied perspective. 18:30 Drinks & food 19:00 Deep Nets, Bayes & the story of AI 20:00 Questions & Networking Dr David Barber David Barber received a BA in Mathematics from Cambridge University and subsequently a PhD in Theoretical Physics (Statistical Mechanics) from Edinburgh University. He is currently Reader in Information Processing in the department of Computer Science at UCL where he develops novel information processing schemes, mainly based on the application of probabilistic reasoning. Prior to joining UCL he was a lecturer at Aston and Edinburgh Universities. He is also the author of the well known 'Bayesian Reasoning and Machine Learning'. Sponsors Semantic Evolution (http://www.semantic-evolution.com) is sponsoring the food and drink for the meetup. Semantic are a market leader in providing unstructured data classification, extraction and normalisation technologies to the finance industry. As a rapidly growing company they are constantly on the lookout for talented and creative problem solvers with practical experience of AI, and are currently hiring! For those looking to gain experience they are also pleased to announce their 2016 summer internship program and welcome applications via their website (http://www.semantic-evolution.com). Please note: If you're not on the RSVP list please don't attend the meetup as security won't let you in. You may also be turned away if you're late to the meetup so we don't disturb the speaker and other attendees.

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  • Introduction to Industrial Applications of Graphical Models

    Location visible to members

    (NOTE: If you are not in the RSVP 'yes' list, please do not attend as security won't let you in). Dr. Ralf Herbrich, the Director of Machine Learning at Amazon, will give an introduction to the application of graphical models in industry; drawing on his extensive experience in some of the top tech companies around today; Microsoft, Facebook and Amazon. It's a great opportunity to understand how the industry is working with graphical models and to gain some international insight on how the utilisation of data and graphical models have evolved. Programme 6:30pm for beer, nibbles and chat 7:00pm 'Introduction to Industry Applications of Graphical Models' 8:00pm Q&A (and more beer) Sponsors We're looking for sponsors (for the food & drink) for this meetup and future meetups - if your company would like some exposure around the big data & machine learning space then let us know! Ralf Herbrich Since November 2012, Ralf has worked at Amazon (https://www.amazon.com/) as Director of Machine Learning Science; until August 2013 he worked in Seattle and then in Berlin, Germany, in the areas of Forecasting, Content Linkage, Scalable Machine Learning Services and Vision-Assisted Technologies. From October 2011 to November 2012, he worked at Facebook (https://www.facebook.com/) in Palo Alto & Menlo Park leading the Unified Ranking and Allocation team. This team is focused on building horizontal large-scale machine learning infrastructure for learning user-action-rate predictors that enabled unified value experiences across the products. From 2009 to 2011, he was Director of Microsoft’s Future Social Experiences (FUSE) Lab UK (http://fuse.microsoft.com/) demonstrating and enabling new social experiences through development of computational intelligence technologies on large online data collections. From 2006 – 2009, together with Thore Graepel (http://research.microsoft.com/en-us/people/thoreg/default.aspx), he was leading the Applied Games (http://research.microsoft.com/en-us/groups/apg/default.aspx) and the Online Services and Advertising (OSA) research (http://research.microsoft.com/en-us/groups/osa/default.aspx) group which engaged in research at the intersection of machine learning and computer games as well as research in search and online advertising combining insights from machine learning, information retrieval, game theory, artificial intelligence and social network analysis. He joined Microsoft Research (http://research.microsoft.com/) in 2000 as a Postdoctoral researcher and Research Fellow of the Darwin College Cambridge (http://www.dar.cam.ac.uk/). Prior to joining Microsoft, Ralf worked at the Technical University Berlin (http://www.tu-berlin.de/menue/home/parameter/en/) as a teaching assistant where he obtained both a diploma degree in Computer Science and a Ph.D. degree in Statistics. His research interests include Bayesian inference and decision making, computer games, kernel methods and statistical learning theory.

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  • Bayesian Networks And The Search For Causality

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    “I would rather discover a single causal law than be king of Persia” (Democritus,[masked]BC). Dr. Ricardo Silva from the Department of Statistical Science at UCL will give an introduction to Bayes rule, Bayesian networks and how these powerful techniques can be used to infer correlation and causation in data. We're once again very lucky to be hosted at Lloyds Register in central London with the event co-sponsored by Bayes Server. Programme 6:30pm for beer, food and chat 7:00pm 'Bayesian Networks And The Search For Causality' 8:00pm Q&A (and more beer) Dr Ricardo Silva Ricardo is a Lecturer at the Department of Statistical Science and Adjunct Faculty of the Gatsby Computational Neuroscience Unit. Prior to that, Ricardo got his PhD from the newly formed Machine Learning Department at Carnegie Mellon University in 2005. Ricardo also spent two years at the Gatsby Computational Neuroscience Unit as a Senior Research Fellow, and one year as a postdoctoral researcher at the Statistical Laboratory in Cambridge. Sponsorship & Future Talks If your company would like to co-sponsor this or future meetups please get in touch.

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  • Deep Nets, Bayes and the story of AI

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    We're delighted to announce the first Bayesian networks meetup hosted at the 250 year old Lloyds Register Group headquarters in Fenchurch Street, London, co-sponsored by Lloyds Register and Bayes Server. To kick off the series, Dr David Barber from UCL will introduce Deep Nets, Bayes and the story of Artificial Intelligence, and the application of cutting edge approaches to real world problems. Programme 6:30pm for beer, food and chat 7:00pm 'Deep Nets, Bayes and the story of AI' 8:00pm Q&A (and more beer) David Barber David Barber received a BA in Mathematics from Cambridge University and subsequently a PhD in Theoretical Physics (Statistical Mechanics) from Edinburgh University. He is currently Reader in Information Processing in the department of Computer Science UCL where he develops novel information processing schemes, mainly based on the application of probabilistic reasoning. Prior to joining UCL he was a lecturer at Aston and Edinburgh Universities.

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