This talk will discuss the Bank of England’s recent forays into Big Data and other unconventional data sources. Particular attention will be given to the practicalities of embedding data analytics in a business context. Examples of the Bank’s use of Big Data will be given, including the analysis of derivatives and mortgage data, Internet searches, and social media.

Dr. David Bholat leads a team of ten data scientists and researchers in Advanced Analytics, a Big Data division in the Bank of England which he helped to establish in 2014. The division is recognised as a leader among central banks in the area of Big Data, as noted in a recent MIT Sloan Review article profiling the division.

A former Fulbright fellow, Dr. Bholat graduated from Georgetown University’s School of Foreign Service with highest honours. He subsequently studied at the London School of Economics, the University of Chicago and London Business School. Publications in 2016 include Modelling metadata in central banks; Non-performing loans: regulatory and accounting treatments of assets; Peer-to-peer lending and financial innovation in the United Kingdom; and Accounting in central banks. Other previous publications relevant to the conference include Text mining for central banks; Big data and central banks; and The future of central bank data.

With the success of online social networks and microblogging platforms such as Facebook, Tumblr, and Twitter, the phenomenon of influence-driven propagations, has recently attracted the interest of computer scientists, sociologists, information technologists, and marketing specialists. In this talk we will take a data mining perspective, discussing what (and how) can be learned from a social network and a database of traces of past propagations over the social network. Starting from one of the key problems in this area, i.e. the identification of influential users, we will provide a brief overview of our recent contributions in this area. We will expose the connection between the phenomenon of information propagation and the existence of communities in social network, and we will go deeper in this new research topic arising at the overlap of information propagation analysis and community detection.

Francesco Bonchi is Research Leader at the ISI Foundation, Turin, Italy, where he leades the “Algorithmic Data Analytics” group. He is also Scientific Director for Data Mining at Eurecat (Technological Center of Catalunya), Barcelona. Before he was Director of Research at Yahoo Labs in Barcelona, Spain, where he was leading the Web Mining Research group.

His recent research interests include mining query-logs, social networks, and social media, as well as the privacy issues related to mining these kinds of sensible data. In the past he has been interested in data mining query languages, constrained pattern mining, mining spatiotemporal and mobility data, and privacy preserving data mining.

He will be PC Chair of the 16th IEEE International Conference on Data Mining (ICDM 2016) to be held in Barcelona in December 2016. He is member of the ECML PKDD Steering Committee, Associate Editor of the newly created IEEE Transactions on Big Data (TBD), of the IEEE Transactions on Knowledge and Data Engineering (TKDE), the ACM Transactions on Intelligent Systems and Technology (TIST), Knowledge and Information Systems (KAIS), and member of the Editorial Board of Data Mining and Knowledge Discovery (DMKD). He has been program co-chair of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010). Dr. Bonchi has also served as program co-chair of the first and second ACM SIGKDD International Workshop on Privacy, Security, and Trust in KDD (PinKDD 2007 and 2008), the 1st IEEE International Workshop on Privacy Aspects of Data Mining (PADM 2006), and the 4th International Workshop on Knowledge Discovery in Inductive Databases (KDID 2005). He is co-editor of the book “Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques” published by Chapman & Hall/CRC Press.

He earned his Ph.D. in computer science from the University of Pisa in December 2003.

The SpiNNaker (Spiking Neural Network Architecture) project aims to produce a massively-parallel computer capable of modelling large-scale neural networks in biological real time. The machine has been 15 years in conception and ten years in construction, and has far delivered a 100,000-core machine in a single 19-inch rack, which is now being expanded towards the million-core full system. Although primarily intended as a platform to support research into information processing in the brain, SpiNNaker has also proved useful for Deep Networks and similar applied Big Data applications. In this talk I will present an overview of the machine and the design principles that went into its development, and I will indicate the sort of applications for which it is proving useful.

Steve Furber CBE FRS FREng is ICL Professor of Computer Engineering in the School of Computer Science at the University of Manchester, UK. After completing a BA in mathematics and a PhD in aerodynamics at the University of Cambridge, UK, he spent the 1980s at Acorn Computers, where he was a principal designer of the BBC Microcomputer and the ARM 32-bit RISC microprocessor. Over 75 billion variants of the ARM processor have since been manufactured, powering much of the world’s mobile and embedded computing. He moved to the ICL Chair at Manchester in 1990 where he leads research into asynchronous and low-power systems and, more recently, neural systems engineering, where the SpiNNaker project is delivering a computer incorporating a million ARM processors optimised for brain modelling applications.

Big Data and Data Science have made commercial advances driven by research. Typical questions that arise during the modeling phase are whether it should be aimed to provide explanations to the user, what should be explained and how this should be done. This talk addresses these controversies using two exemplary industrial projects:  “Markov Networks for Planning” and “Medical Research Insights”.

Rudolf Kruse is Professor at the Otto-von-Guericke University of Magdeburg (Germany), where he is leading the Computational Intelligence Group. His current research interests include data science and intelligent systems. His group is successful in various industrial applications in cooperation with companies such as Volkswagen, SAP, Daimler, and British Telecom. He obtained his Ph.D. and his Habilitation in Mathematics from the Technical University of Braunschweig in 1980 and 1984 respectively. Following a stay at the Fraunhofer Gesellschaft, he joined the Technical University of Braunschweig as a professor of computer science in 1986. Since 1996 he is a full professor at the Department of Computer Science of the Otto-Von-Guericke University Magdeburg in Germany. Rudolf Kruse has coauthored 15 monographs and 25 books as well as more than 350 refereed technical papers in various scientific areas. He is associate editor of several scientific journals. He is a Fellow of the International Fuzzy Systems Association (IFSA), Fellow of the European Coordinating Committee for Artificial Intelligence (ECCAI) and Fellow of the Institute of Electrical and Electronics Engineers (IEEE).

Many data science or control problems can be qualified as sequence learning. Examples of sequences abound in fields such as natural language processing (e.g., speech recognition, machine translation, query formulation or image caption generation) or robotics (control and navigation). Their underlying challenge resides in learning long range memory of observed data. In this talk, we will look at the inner workings of recurrent neural networks and neural memory architectures for learning sequence representation and illustrate their state-of-the-art performance.

Piotr Mirowski is a Research Scientist at Google DeepMind, the research lab that focuses on solving Artificial General Intelligence and that investigates research directions such as deep reinforcement learning or systems neuroscience.
Piotr has an M.Sc. in computer science (2002) from ENSEEIHT in Toulouse, France and, prior to resuming his studies, worked as a research engineer at Schlumberger Research (2002-2005). He obtained his Ph.D. in computer science (2011) at New York University under the supervision of Prof. Yann LeCun. His doctoral work on using recurrent neural networks for learning representations of time series covered applications such as inferring gene regulation networks, predicting epileptic seizures, categorizing streams of online news and statistical language modeling for speech recognition (in collaboration with AT&T Labs Research). After his PhD, Piotr was a Member of Technical Staff at Bell Labs (2011-2013), where he focused on simultaneous localization and mapping for robotics, on indoor localization and on load forecasting for smart grids. Prior to joining Google DeepMind (2014-), Piotr worked as an applied scientist at Microsoft Bing (2013-2014), investigating deep learning methods for search query formulation.

2011 Janet Fabri Award for best doctoral dissertation in computer science, NYU.
2009 Young Investigator Award, International Workshop on Seizure Prediction IWSP4.
2009 Henning Biermann Award for outstanding contribution by a PhD student, NYU.
2008 Google Student Award, Machine Learning Symposium, New York Academy of Sciences.