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Real Artificial Intelligence - Speakers

Details of the speakers and their presentations are given below.

Dr. Hakan Duman (Volkswagen Data Lab)

From descriptive analytics to prescriptive analytics - Applying smart analytics and AI to real business questions

With the increasing amount of data now being available, companies in every industry are looking to exploit data for completive advantage, i.e. by understanding customer needs, identifying emerging markets, decreasing production costs and increasing sales etc. Traditional business intelligence solutions are no longer capable of dealing with the huge volume and variety of data produced and captured every day in businesses or made available through ubiquitously interconnected and networked products. However, the rise of computational processing power and the considerable fall of storage technology costs enable the development and utilisation of complex algorithms for profound big data analytics and data mining applications addressing real business questions.

Most common domains for successfully applying advanced big data analytics in businesses are in marketing and after sales, e.g. for obtaining a more holistic view of products and customer lifecycles, online advertisement, targeted marketing, as well as in understanding und optimizing business processes and improving business insights. However more opportunities have recently been identified, such as in production, finance, purchasing, logistics etc. where advanced big data analytics and artificial intelligence and automation bring value to the business and give the organisations the competitive benefit they need.

In the talk, first we will provide a short introduction to advanced big data analytics principles then present an excursion on real business questions and discuss how organisations have found many advantages in their explorations with big data.

Dr. Hakan Duman received his B.Sc. degree in computer science from the Applied University of Regensburg, Germany, and his Ph.D. degree in computer science from the University of Essex, UK, in 1999 and 2008, respectively. He is currently Lead Data Scientist at the Volkswagen Data Lab and Honorary Senior Lecturer at the University of Essex. Prior to that, he worked as Senior Research Scientist at BT Research Labs. He is author of over 30 original research papers in international journals, book chapters, and in international conference proceedings and holds 5 international patents. His research interests include Computational Intelligence, Intelligent Data Analytics / Big Data, Robotics, Ambient and Ubiquitous Intelligence, and Visual Analytics. He is IEEE Senior Member.


Dr Christos Tsotskas (Transport Systems Catapult)

AI and Modelling

The talk focus on the use of short term traffic prediction methods to proactively manage the strategic road network. I will highlight outcomes that could assist in de-risking the adoption of such methods and instil confidence in Highways England to procure or stimulate the market to develop fit-for-purpose tools which could be deployed within its traffic operations environment.

Christos Tsotskas has extensive experience in Scientific Computing and Complex Systems Engineering in real-world problems. Strong supporter and user of open source tools and open engineering methods. Wide knowledge of computer software, hardware and security, including large scale infrastructure. Mathematical modelling for highly intensive industrial environments and more generic problems. Research and Development Skills throughout the complete cycle of products and services; from system- requirements capturing, -designing, -implementation till -testing, – prediction, and -risk assessment. Complex big data analysis and interpretation. Extensive experience with office applications and good technical writing. Quick Learner and adapting to changes. Project Management and Development Management (team and time). Technical and non-technical communication. Most recent training: Advanced Neural Networks, Grid Computing, Business & Innovation.


Prof Trevor Martin (BT/University of Bristol)

AI and Security: When computer says no and human asks why

The new, data-driven, AI is able to digest large amounts of data and identify abnormal patterns. It is promoted as a key tool in enhancing the security of information systems, networks and devices. However, there are two important caveats - the general issue (highlighted by the explainable AI programme) that the effectiveness of data-driven AI is limited by its inability to explain decisions and actions to human users. This is particularly relevant when considering multiple related events across different timescales. The second limitation, specific to cybersecurity, is that data-driven methods tend to require good quality data in large quantities. An attack involving exploitation of previously unknown weaknesses is typically in poorly populated areas of the data space. In this talk, I will emphasise the need for cybersecurity systems to be based on human-computer collaboration, rather than on computer autonomy. This requires a knowledge-based approach which allows human input as well as data-driven methods. I will briefly outline a framework for specifying and detecting multi-event sequences in security applications.

Trevor Martin is Professor of Artificial Intelligence at the University of Bristol, UK and a BT Senior Research Fellow, working with the Security Futures Practice. His research covers soft computing in artificial intelligence applied to areas such as security analytics, extraction and integration of semi-structured information, soft concept hierarchies, and fundamental approaches to fuzzy uncertainty. In addition to substantial funding from BT, this work has been supported by the European Commission, MOD, GCHQ, EPSRC and DTI. He is a member of the editorial boards of journals such as Fuzzy Sets and Systems and Evolving Systems, and has served on many conference programme and organising committees, including IEEE Fuzzy Systems programme chair in 2007 and technical co-chair in 2010 and 2015. He is a co-organiser of the URSW (Uncertain Reasoning for the Semantic Web) series of workshops, chairs the IEEE Computational Intelligence Society’s Semantic Web Task Force and is a member of the IEEE’s recently established FML (Fuzzy Markup Language) Standards group. He has published over 230 papers in refereed conferences, journals and books, and is named inventor on 15 patents. He is a Chartered Engineer and member of the BCS and IEEE, as well as serving on the UK EPSRC College.


Ralph Traphoner (Empolis)

AI and Decision Support

Ralph Traphöner is Director of Technologies at Empolis Information Management GmbH. After studying computer science and business administration at the University of Kaiserslautern, focusing on practical applications in AI and Case-Based Reasoning, Ralph Traphöner co-founded the company TECINNO, today a part of Empolis, in 1991. He managed the EU funded projects INRECA I and II (Induction and Reasoning from Cases), was co-ordinating project manager of WEBSELL (Intelligent Sales Assistants for the World-Wide-Web), was project manager in SEKT (Semantic Enabled Knowledge Technologies). Up to today he has actively been involved in 15 European Projects throughout Framework Programmes 3 to 7. He also participated and managed other research projects funded by national institutions, such as the German research endeavour THESEUS.

Ralph Traphöner also acted as advisor, evaluator and reviewer for the Fifth, Sixth and Seventh Framework Programmes and has contributed as an expert to the EP2010 study. Expertise: Case-Based Reasoning, Intelligent Information Retrieval and Search Technology, Electronic Commerce, Knowledge Management and Artificial Intelligence. He has given many tutorials in AI and has presented courses on knowledge management and related topics. The focus of his work during the last two years was on consulting clients with respect to applying AI to big data in the field of technical service and mechanical engineering.

Beyond AI he is a supporter of the FCK, i.e. the Kaiserslautern football team, a collector of mechanical computing machinery and loves to take photographs.

Dr. Stelios Kapetanakis (University of Brighton)

AI and Transport: AI Innovation in British Railways

Promoting innovation in the Railways industry can be a challenging task due to a rapidly growing urban population (more than 70% of the global population is expected to live in cities in the next 30 years), a need for a constantly-being-mobile middle class and an ongoing demand for a truly digital and modern railway from millions of passengers. In such a demanding environment, Artificial Intelligence seems “the only way forward” that could push further existing saturated boundaries and drive forwards pragmatic solutions to real problems. This talk will illustrate both challenges and viable solutions in the Railway industry and present a real case study.

Dr Stelios Kapetanakis is a Principal Lecturer in Business intelligence and Enterprise in the School of Computing, Engineering and Mathematics, University of Brighton. He has a PhD in Artificial Intelligence and an MBA in Knowledge and Innovation Management. For the past 12 years Dr Kapetanakis is working closely with EU SMEs and large organisations, having an extended portfolio of applied Artificial Intelligence projects, Research consultancies and EU funding. Dr Kapetanakis has also worked as an independent consultant for the EU Banking sector, the UK Railways, the US Healthcare, Clarksons and Airbus. His research interests include large scale Data Mining and Machine Learning, Case-based Reasoning and Spatial-Temporal reasoning.

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