Keynote Speakers

Nuria Oliver

Nuria Oliver

Director of Research in Data Science, Vodafone

Nuria Oliver, is Director of Research in Data Science at Vodafone and Chief Data Scientist at Data-Pop Alliance. She has over 20 years of research experience in the areas of human behavior modeling and prediction from data and human-computer interaction. She holds a PhD in perceptual intelligence from MIT. She worked as a researcher at Microsoft Research for over 7 years and as the first female Scientific Director at Telefonica R&D for over 8 years. At the end of 2016 she was named Chief Data Scientist at Data-Pop Alliance and in early 2017 she also joined Vodafone as the first Director of Research in Data Science. Her work is well known with over 150 scientific publications that have received more than 11000 citations and a ten best paper award nominations and awards. She is co-inventor of 40 filed patents and she is a regular keynote speaker at international conferences. Nuria’s work and professional trajectory has received several awards, including the MIT TR100 (today TR35) Young Innovator Award (2004), the Rising Talent award by the Women’s Forum for the Economy and Society (2009) and the European Digital Woman of the Year award (2016). She has been named “an outstanding female director in technology” (El PAIS, 2012), one of “100 leaders for the future” (Capital, 2009) and one of the “40 youngsters who will mark the next millennium” (El PAIS, 1999). She became an ACM Distinguished Scientist in 2016, a Fellow of the European Association of Artificial Intelligence in 2016 and an IEEE Fellow in 2017. Her passion is to improve people’s quality of life, both individually and collectively, through technology. She is also passionate about scientific outreach. Hence, she regularly collaborates with the media (press, radio, TV) and gives non-technical talks about science and technology to broad audiences, and particularly to teenagers, with a special interest on girls.

Keynote Title: Towards Human Behavior Modeling from Mobile Data
Abstract: We live in a world of data, of big data, a big part of which has been generated by humans through their interactions with both the physical and digital world. A key element in the exponential growth of human behavioral data is the mobile phone. There are more mobile phones in the world as humans. The mobile phone is the piece of technology with the highest levels of adoption in human history. We carry them with us all through the day (and night, in many cases), leaving digital traces of our physical interactions. Mobile phones have become sensors of human activity in the large scale and also the most personal devices. In my talk, I will present a few of the projects that I have carried out in the area of modeling humans from a variety of mobile human behavioral data both at an individual level and collectively. Some of the projects include inferring personality, financial responsibility, boredom, taste to provide recommendations or crime, all from mobile phone data. I will conclude by highlighting opportunities and challenges associated with building data-driven models of human behavior

Valentina Salapura

Valentina Salapura

IBM T.J. Watson Research Center

Valentina is with the IBM Research in the Services Innovation Lab where she is helping IBM realize the value of cloud computing. In 2010, Dr. Salapura served as a lead for the Global Technical Outlook with the IBM Research Strategy and Worldwide Operations team to define IBM’s future research agenda and strategy working with the worldwide IBM research organizations. Previously, Valentina served as architect for Power Systems building workload-optimized systems for a Smarter Planet with a focus the processing unstructured data and business analytics. Valentina has been a technical leader for the Blue Gene program since its inception where she has contributed to the architecture and implementation of the BlueGene/Q, BlueGene/P, BlueGene/L and Cyclops systems. Valentina made seminal contributions to multiprocessor-based network architectures, power/performance characterization of a computer system, and emulation of microprocessors using FPGAs. Before joining IBM Research in 2000, Dr. Salapura was a faculty member with Technische Universität Wien, where she also received her Ph.D. degree. Valentina Salapura is recipient of the 2006 ACM Gordon Bell Prize for Special Achievements for the Blue Gene/L supercomputer and Quantum Chromodynamics. Dr. Salapura is the author of over 60 papers and several book chapters on processor and network architecture, and holds over 80 patents in this area. Dr. Salapura is a Fellow of the IEEE, and a Member of IBM Academy of Technology. Beyond her technical work, Valentina serves as an advocate for promoting the participation of women and underrepresented minorities in technical disciplines.

Mark Harman

Mark Harman

Facebook

Mark Harman is currently an engineering manager at Facebook and a part time professor of Software Engineering in the Department of Computer Science at University College London, where he directed the CREST centre for ten years (2006-2017) and was Head of Software Systems Engineering (2012-2017). He is widely known for work on source code analysis, software testing, app store analysis and Search Based Software Engineering (SBSE), a field he co-founded and which has grown rapidly to include over 1,600 authors spread over more than 40 countries. In February 2017, he and the other two co-founders of the start-up Majicke moved to Facebook, London, where they are working on software test automation.

Keynote Title: Search Based Software Engineering
Abstract: This keynote will present a brief overview of Search Based Software Engineering (SBSE), giving examples of successful application across the full spectrum of software engineering activities and problems. The talk will also cover recent results in genetic improvement. Starting from an existing version of a software system, genetic improvement uses SBSE to search the system's neighbourhood, constructing new versions that are faithful to desirable existing semantics, while optimising chosen measurable properties of interest. This search-based approach to program improvement has been successfully applied to program transplantation, porting, and specialisation, and to reducing the consumption of system resources such as time, memory and energy. This keynote is based on joint work at UCL with Earl Barr, Bobby Bruce, Yue Jia, Bill Langdon, Alexandru Marginean, Justyna Petke, Federica Sarro, Fan Wu and Yuanyuan Zhang at UCL.

Jan Hofmann

Jan Hofmann

Deutsche Telekom AG

Jan Hofmann is VP eCompany Products at Deutsche Telekom. In this role, he is responsible for the development and operation of products driving DT’s digital transformation. In addition, he is heading up DT Group’s innovation initiative eLIZA that drives the evolution of customer service using artificial intelligence. Jan joined DT in 2008 with a first tenure at Group Strategy. In 2012, he became Head of Video Advertising at digital advertising subsidiary InteractiveMedia, where in 2014 he moved on to set up a product department as VP Product. Before joining DT, Jan worked with Daimler and Deutsche Bank. He studied Physics and Product Design, and holds a PhD in Human Computer Interaction.

Keynote Title: AI-driven customer interactions: Pushing the telco envelope
Abstract: The brains of much-hyped chatbots and digital assistants range from simple, rule-based systems to complex deep learning applications. Two years into our journey to explore the future of telco customer service using digital assistants, we report on key learnings from these real-world applications and their daily interactions with our customers. What can perfectly be done by a simpler, rule-based bot? Where and how does an AI-based machine really make a difference? Which AI nuts should be cracked next to further improve the business case for AI-driven customer interactions?

Paolo Rosso

Paolo Rosso

Technical University of Valencia, Spain

Paolo Rosso (www.dsic.upv.es/~prosso/) is a professor at the Technical University of Valencia, Spain where he is also a member of the PRHLT research center. His research interests include author profiling and irony detection in social media, opinion spam detection, as well as text reuse and plagiarism detection. Since 2009 he has been involved in the organisation of PAN benchmark activities, since 2010 and 2011 in the framework of CLEF and FIRE evaluation forums, on plagiarism / text reuse detection and author profiling. He has been also co-organiser of the shared task on Sentiment analysis of figurative language in Twitter at SemEval-2015. Paolo Rosso has been PI in several national and international research projects, and he is co-author of 50+ articles in international journals and 400+ articles in conferences and workshops.

Keynote Title: Intelligent sentiment analysis tools: detecting deceptive opinions and irony
Abstract: The detection of deceptive reviews is a quite challenging problem and not only from an automatic perspective because only 60% of humans discriminate between truthful and fake opinions with a certain degree of accuracy. With the increasing of social media, consumers rely more than ever on online reviews to make their decisions: a recent survey found that more than 80% of them have reinforced or changed their decisions to purchase a product due to positive / negative online reviews. Unfortunately there is an increasing trend to post fake reviews with the aim to sound authentic and deceive the consumers in their decision. Sentiment analysis tools need to filter out fake reviews before analysing the polarity of the truthful ones that is already quite challenging especially in case of irony where what is literally said is usually negated in absence of an explicit negation marker. In the talk I will describe some of the state-of-the-art approaches to detect deceptive opinions and also irony.

Frank Wang

Frank Wang

Chairman, IEEE Computer Society, UK&I Chapter | University of Kent

Frank Z. Wang is the Professor in Future Computing and Head of School of Computing (2010-2016), University of Kent, the UK. The School of Computing was formally opened by Her Majesty the Queen. His led school achieved an amazing result in the 2014 UK government REF (Research Excellence Framework): the research intensity was ranked 12th out of over 150 computing departments in the UK. Professor Wang's research interests include brain-like computer, memristor theory and applications, deep learning, cloud computing, big data, and green computing, etc. He has been invited to deliver keynote speeches and invited talks to report his research worldwide, for example at Princeton University, Carnegie Mellon University, CERN, Hong Kong University of Sci. & Tech., Tsinghua University (Taiwan), Jawaharlal Nehru University, Sydney University of Technology, and University of Johannesburg. In 2004, he was appointed as Chair & Professor, Director of Centre for Grid Computing at CCHPCF (Cambridge-Cranfield High Performance Computing Facility). CCHPCF is a collaborative research facility in the Universities of Cambridge and Cranfield (with an investment size of £40 million). Prof Wang and his team have won an ACM/IEEE Super Computing finalist award. Prof Wang is Chairman (UK & Republic of Ireland Chapter) of the IEEE Computer Society and Fellow of British Computer Society.

Keynote Title: Intelligent Systems based on Deep Learning
Abstract: Deep learning was inspired by the 1981 Nobel Prize work by David H. Hubel & Torsten Wiesel, who found a cascading model in the human brain. We are building an intelligent system that works similarly to the human brain. Most of previous efforts to build brain-like machines have failed because it took about the same silicon area to emulate a CMOS synapse as that needed to emulate a neuron. In theory, any realistic implementation of a synapse should ideally be at least four orders of magnitude smaller than that required to build a neuron. The invention of the memristor opens a new way to implement synapses. A memristor is a simple 2-terminal element, which means a vast number of memristors could be integrated together with other CMOS elements, in a brain-like machine.