Data science has revolutionised the social sciences at the same time as industries and academia are being transformed by the availability of big data, coupled with massive increases in computing power.
How best should we manage this change in a positive manner, and what can be expected from this trend in the future?
Professor Ken Benoit will be joined by experts from education, industry, and public policy for a panel discussion that brings together expertise in social and data sciences to explore how to address these challenges and seize opportunities for global prosperity.
Hosted by the LSE Data Science Institute (DSI), this event will celebrate the DSI's launch in 2020 and map out its ambitions for the coming years. The DSI’s mission is to support this revolution in “social data science” through spreading data science education across the School and supporting research and outreach in the applications of data science to its social scientific studies.
Please note that in person tickets are no longer available for this event.
Chief Executive, R² Factory at Rolls-Royce
Caroline has worked at the transformational edge of emerging digital technologies for more that 25 years, leading businesses' strategic response to technology and executing on organisational changes, new market developments and new business model launches.
Following senior roles at Telefonica, Sodexo, O2 and the Digital Catapult, Caroline led Rolls-Royce's R2 Data Labs for five years, delivering £400m of value back into the Group. During the pandemic, she pioneered the Emergent Alliance, which convened over 50 corporates, individuals, NGOs and governments for the first time to leverage expertise, data and resources to aid societal recovery.
Now leading the R2 Factory founding team, Caroline drives the company’s strategic vision to create a safe space to do hard things by blending unique experience, cutting-edge capability, thought leadership and new ways of working to create new opportunities and business models.
Voted one of the most influential people in UK technology (Computer Weekly UKtech50, 2022), Caroline holds a Masters degree from Oxford and has an unfinished PhD in modern gothic literature and how we all use scary monsters to help contain social anxiety.
Chief Data and Analytics Officer, NHS England
Ming has over 20 years’ experience in managing and delivering large scale change involving implementation of new operating models in complex and challenging environments. She joined the NHS in October 2009, initially leading commissioning support services in the West Midlands as the Managing Director for Healthcare Commissioning Services and then as the Managing Director for South Yorkshire and Bassetlaw Commissioning Support Unit.
Ming is currently the Chief Data and Analytics Officer for NHS England and is responsible for strategic development of data and analytics capability across NHS. In 2021 Ming was named the most influential person in data and analytics in DataIQ’s top 100 (the first and only fully-curated power list of the most influential data and analytics practitioners) and was awarded Data and Analytics Leader of the Year for transforming the way data and analytics is perceived across the health and care service.
The team Ming leads provides NHS England with the resources to support system transformation, strategic information assets, digital tools and analysis to underpin policy development and predictive capabilities to enhance decision making and improve health and care services. Prior to joining the NHS, Ming was a Partner with Accenture specialising in Strategy and Supply Chain work. She has worked in Europe, North America, and South East Asia where she was the Managing Partner for the Supply Chain Practice for 3 years.
Her experience spans global clients in Consumer Goods, Retail, Pharmaceutical, Manufacturing and Utility sectors. Ming holds a first-class degree in Pharmacy and an MBA from Warwick University.
Professor Helen Margetts OBE FBA
Professor of Internet and Society at the Oxford Internet Institute, University of Oxford and Director of the Public Policy Programme, The Alan Turing Institute
Helen Margetts OBE FBA is Professor of Society and the Internet in the Oxford Internet Institute (OII) at the University of Oxford, and Director of the Public Policy Programme at The Alan Turing Institute for Data Science and AI in London. From 2011-8 she was Director of the OII, a multi-disciplinary department of the University of Oxford. Before coming to Oxford, she was UCL's first Professor of Political Science and Director of the School of Public Policy (2000-4). She has degrees in Mathematics (BSc, Bristol), Politics (MSc, LSE) and Government (PhD, LSE).
She has researched and written extensively on the relationship between technology, government, politics and public policy, including Political Turbulence: How Social Media Shape Collective Action (with Peter John, Scott Hale, Taha Yasseri) which won the Political Studies Association’s 2017 prize for best politics book; Digital Era Governance (with Patrick Dunleavy); and The Tools of Government in the Digital Age (with Christopher Hood).
She received the Technical University of Munich’s Friedrich Schiedel prize (2018) for research and research leadership in Technology and Politics; was awarded the O.B.E for services to social and political science (2019); held the Kluge Chair in Technology & Society at the Library of Congress (2019); became a Fellow of the British Academy in 2019; and won the Meyer-Struckmann Prize for research on Digitization and Democracy from the Heinrich-Heine-University Düsseldorf in 2020.
Dr Stefan Hunt
Chief Data and Technology Insights Officer, Competition and Markets Authority
Stefan Hunt is the Chief Data and Technology Insight Officer at the UK’s Competition and Markets Authority. He works across the CMA’s digital portfolio, including oversight of cases involving large technology firms, input into a new digital regulation regime, and leading programmes of work on algorithms and choice architecture. He leads a unit of nearly 60 ‘technologists’ drawn from data science, engineering, technology insight, behavioural science, eDiscovery and digital forensics. These teams work on all major digital cases, and advise on technology, analyse large datasets, use machine learning, build software, scrutinise algorithms, and more. He is an Honorary Professor in economics at University of Nottingham, has a PhD in economics from Harvard University and has a bachelor’s in mathematics and experimental psychology from the University of Cambridge. He previously worked at the UK’s Financial Conduct Authority.
Professor Ken Benoit
Director, LSE Data Science Institute (DSI) and Professor of Computational Social Science, LSE Department of Methodology
Ken’s current research focuses on computational, quantitative methods for processing large amounts of textual data, mainly political texts and social media. Current interest span from the analysis of big data, including social media, and methods of text mining. He has published extensively on applications of measurement and the analysis of text as data in political science, including machine learning methods and text coding through crowd-sourcing, an approach that combines statistical scaling with the qualitative power of thousands of human coders working in tandem on small coding tasks.
He received his PhD in Government with a specialisation in statistical methodology from Harvard University.
His substantive research interests include comparative party competition, the European Parliament, electoral systems, and transitions to democracy. Much of his recent work involves estimating the electoral effects of campaign spending. He is also a leading specialist on Hungarian elections and the Hungarian electoral system. His methodological interests include statistical methodologies for the social sciences, especially those relating to measurement and quantitative text analysis. Recent data large-scale measurement projects in which he has been involved include estimating policy positions of political parties through expert surveys, manifesto coding, and text analysis.
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