Analytics Summit Speakers

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Plenary Speaker: Prof David Hand

Senior Research Investigator & Emeritus Professor of Mathematics Imperial College London

Talk Title: Validation of AI

Bio: David Hand is a co-proposer of the Validate AI Conference. He is Senior Research Investigator and Emeritus Professor of Mathematics at Imperial College, London, where he previously held the Chair of Statistics. He serves on the Board of the UK Statistics Authority and the European Statistical Advisory Committee. He is a former president of the Royal Statistical Society and has received many awards for his research, including the Guy Medal of the Royal Statistical Society, the Box Medal from the European Network for Business and Industrial Statistics, and the Research Award of the International Federation of Classification Societies. His 29 books include Principles of Data Mining, Measurement Theory and Practice, The Improbability Principle, Information Generation, and Intelligent Data Analysis.

David Foster David Foster, Founding Partner at Applied Data Science Partners

Talk Title: The Data Science Tech Tree - A Guide For Executives

The ecosystem of tools and technologies used by data science and AI professionals is vast and expanding at an ever-increasing rate. This talk presents The Data Science Tech Tree - a guide for executives that explains the key technologies and how they fit into the overall data science landscape. We'll cover machine learning methods, databases, cloud platforms, deployment strategies, visualisation software, programming languages and more. At the end of this talk, you'll understand which tools and technologies are required to solve the data challenges faced by your company and how they fit together to form a coherent and impactful data science strategy.

Bio: David Foster is a Founding Partner of Applied Data Science Partners, a data science consultancy building innovative AI solutions for clients. He holds an MA in Mathematics from Trinity College, Cambridge, UK and an MSc in Operational Research from the University of Warwick. David has won several international machine learning competitions and is the author of the best-selling book ‘Generative Deep Learning: Teaching Machines to Paint, Write, Compose and Play’. He has also authored several successful blog posts on deep reinforcement learning including ‘How To Build Your Own AlphaZero AI using Keras’.

David FosterSamhita Rao, Global Head of Operational Intelligence, Corporate Real Estate, HSBC

Talk Title: Data Analytics from a Business perspective

Bio: Samhita heads HSBC Corporate Real Estate’s (CRE’s) Global Operational Intelligence function. Samhita has over 20 years of cross-functional experience across Strategy, Transitions, Operations, Risk, HR and CRE and has lived and worked out of UK, China and India.

Most of Samhita’s career was spent in leading large global teams handling significant amounts of data, owning and driving data integrity in core data systems and in establishing and executing data governance models, data analytics and reporting. Samhita’s current focus is to establish a grass-roots movement to build a data-driven culture in her function and to leverage the power of data analytics to generate transformational insights that drive meaningful change for the organisation.

David FosterMatt King Product Director, O2 Motion - Smart Cities, Data and Analytics

Talk Title: Driving transport, environment and planning projects using data science


David FosterKaterina Papadimitraki Senior Data Analyst at Movement Strategies

Talk Title: Driving transport, environment and planning projects using data science

David Foster

Ganna Pogrebna Lead for Behavioural Data Science at the Alan Turing Institute

Talk Title: The Data Science of Hollywood: from Content Generation to AI-driven Supply Chains

Improving productivity and customer experience in the entertainment industry are very challenging tasks as they heavily depend on generating attractive content for the consumers.

The consumer-centric design (putting the consumers at the centre of the content development and production) focuses on ways in which businesses can deliver customized services and products which accurately reflect consumer preferences. We propose a new framework which allows to use data science to optimize content-generation in entertainment and test this framework for the motion picture industry.

In a series of projects, we use the natural language processing, image recognition as well as advanced sound analytics methodology combined with econometric analysis to explore whether and to what extent human emotions, imagery format, and music patterns shape consumer preferences for media and entertainment content, which, in turn, affect revenue streams. By analysing thousands of movie scripts, hundreds of thousands of music samples, and millions of frames, we (i) understand the emotional trajectories of motion pictures as well as (ii) measure the psychological loading of movie clips and trailers and test whether and to what extent these new metrics can influence popularity and box offices. Implications of this analysis for generating on-demand content and improving productivity in entertainment industries are discussed.

Bio: Ganna is a Professor of Behavioral Economics and Data Science at the University of Birmingham and a Fellow at the Alan Turing Institute. She is also a Turing Lead for the University of Birmingham and a Behavioral Data Science Lead at the Alan Turing Institute. Blending behavioral science, computer science, data analytics, engineering, and business model innovation, Ganna helps businesses, charities, cities, and individuals to better understand why they make decisions they make and how they can optimize their behavior to achieve higher profit, better (cyber)security, more desirable social outcomes, as well as flourish and bolster their well-being.

David FosterJack Snape Principal Analyst at Transport for the North

Talk Title: Making sense of simulated worlds: Using the principles of predictive analytics to make complex simulation models more usable

Modellers are pulled in a variety of directions – more detail, more policy options, more scenarios. Computing power is always increasing, but there is often a similar rate of increase in our ability to ask more questions of our models.

Simulation models are moving closer to ‘real world’ levels of detail and being linked together into complex ecosystems, due to need to understand interactions between sectors, such as transport, land-use and electricity generation. There are benefits to developing this functionality, but it leads to runtimes that make it challenging to use these models for strategy development.

At Transport for the North, we are applying the principles of predictive analytics to make our complex modelling ecosystems more usable. As our models become more detailed, their output databases become comparable to real-world big data. In contrast to the real world, we can control the parameter space to generate training data to build high-level predictive models. These models are meta-models: models of models. Their objective is to replicate the behaviour of detailed models, but with a fraction of the runtime, making these tools much more flexible and usable for developing transport strategies in a range future scenarios.

In this talk, we will outline the principles of the approach and set out a current example - a simplified representation of the North’s road and rail network, carefully calibrated to our detailed transport models. This is used in conjunction with our Northern Economy and Land-Use Model (NELUM) to allow an integrated approach to Land-Use Transport Interaction modelling with both fast runtimes and consistency with conventional models. Future developments, including the potential to use in freight modelling and with agent-based models will also be discussed.

Bio: Jack has a PhD in physics and six years’ experience working as an analyst in the Civil Service and in Local Government. Jack has worked in analysis across a range of policy areas, including manufacturing, climate change and transport. At the Committee on Climate Change he was responsible for advising the Government on reducing carbon emissions from transport in the UK. At TfN Jack leads on wider economic analysis. A key part of this is improving TfN’s capabilities to model economic transformation and changing patterns of land-use, using the Northern Economy and Land-Use Model (NELUM).