Validate AI Directors

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Shakeel is a co-proposer and chair of the committee of the Validate AI Conference. He has been a great advocate of Artificial Intelligence supporting capability building in HMRC as well as sharing his expertise across government departments and tax administrations globally over the last decade. Prior to this he worked in Financial Services leading some major supervised and unsupervised machine learning initiatives for 10 years. In the last five years he has worked closely with academics with extensive tacit industry knowledge to develop a novel Data Science Masterclass program. He graduated with a Masters in Operational Research at Strathclyde Business School which has greatly aided his ability over his career to deliver in the AI field combining maths, programming and problem structuring methods. His extensive experience across private, public and academic sectors has also enabled him to benefit from the notion of the triple helix model.  

 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. 

 Marta Kwiatkowska is a co-proposer of the Validate AI Conference. She is Professor of Computing Systems and Fellow of Trinity College, University of Oxford. Prior to this she was Professor in the School of Computer Science at the University of Birmingham, Lecturer at the University of Leicester and Assistant Professor at the Jagiellonian University in Cracow, Poland. Kwiatkowska has made fundamental contributions to the theory and practice of model checking for probabilistic systems, focusing on automated techniques for verification and synthesis from quantitative specifications. More recently, she has been working on safety and robustness verification for neural networks with provable guarantees. She led the development of the PRISM model checker, the leading software tool in the area and winner of the HVC Award 2016. Probabilistic model checking has been adopted in diverse fields, including distributed computing, wireless networks, security, robotics, healthcare, systems biology, DNA computing and nanotechnology, with genuine flaws found and corrected in real-world protocols. Kwiatkowska is the first female winner of the Royal Society Milner Award and was awarded an honorary doctorate from KTH Royal Institute of Technology in Stockholm. She won two ERC Advanced Grants, VERIWARE and FUN2MODEL, the latter focusing on developing probabilistic verification methods for deep learning, and is a coinvestigator of the EPSRC Programme Grant on Mobile Autonomy. Kwiatkowska is a Fellow of the Royal Society, Fellow of ACM, Member of Academia Europea and Fellow of the BCS.  

 Professor Yike Guo has been the Founding Director of Data Science Institute, Imperial College London since April 2014. He is also the Dean of School of Computer Science, Shanghai University, China since April 2015.

Professor Guo lectured at Imperial College London from 1997 and become professor in computing science in the Department of Computing, Imperial College in 2002. He is a world renowned researcher in the area of big data analysis with the broad experiences of applying the data mining and machine learning technology for scientific applications, especially in the areas of engineering and medicine. In the last 20 years, he managed research projects as PI or Co-PI with total funding of £130 million (with £35m as Principal Investigator) with significant social and economic impacts. In last 17 years, he has been leading two companies, InforSense and IDBS by translating his technology achievements into successful products. He was the Chief Executive Officer of Inforsense, a spinout company of ICL he established from 1999 to 2009 and Chief Innovation Officer of IDBS, after its acquisition of Inforsense in 2009. The products Professor Guo has been leading to develop have more than 50000 users worldwide. 

Professor Guo has a strong collaboration with China in research and commercial development. He is non-executive director of public listed companies and involved in the development of many start-up companies. He is the Chief Strategist in Data Economy at Shanghai Industrial Technology Institute since 2013; Oversea Scientific Advisor in Big Data of Beijing Municipality Government since 2013 and Oversea Scientific Advisor in Big Data and AI of Jiangsu Provincial Government since 2016. He was named as one of the 10 most influential data scientists in China by China’s National Information Centre in 2017 and won the prestigious Friendship Award from Jiangsu provincial government for the contribution of building up Big Data and AI industrial strategy for the province.

 Dr. Tony Bellotti is a co-proposer of the Validate AI Conference following discussions relating to the Masterclass learning program he delivered in Machine Learning at HMRC. He is now Associate Professor in the School of Computer Science at University of Nottingham in Ningbo, China. Until recently, he was senior lecturer in statistics in the Mathematics department at Imperial College London UK. He received his PhD in machine learning from Royal Holloway, University of London in 2006 and he was a Research Fellow in the Credit Research Centre at the University of Edinburgh from 2007 to 2010. He has taught credit scoring and quantitative methods in retail finance at Imperial College London since 2010. His main area of expertise, research and publication is the application of statistical models and machine learning to consumer credit risk, with particular interest in model risk, dynamic survival models and expected loss estimation .