Dr. C. Lee Giles is the David Reese Professor at the College of Information Sciences and Technology at the Pennsylvania State University, University Park, PA. He is also Professor of Computer Science and Engineering, Professor of Supply Chain and Information Systems, and Director of the Intelligent Systems Research Laboratory. He directs the CiteSeerx project and codirects the ChemxSeer project at Penn State. He has been associated with Columbia University, the University of Maryland, University of Pennsylvania, Princeton University, the University of Pisa and the University of Trento.
His research is or has been supported by NSF, NASA, DARPA, Microsoft, FAST Search and Transfer, Ford, IBM, Internet Archive, Lockheed-Martin, Alcatel/Lucent, NEC, Raytheon, Smithsonian, US Department of Treasury, and Yahoo. He has consulted for or been on advisory boards of NEC, FAST Search and Transfer, PJM, KXEN, US Department of Treasury, and the US Department of Defense.
Title: Machine Learning and Data Mining for Scholarly Big Data
Marios M. Polycarpou is a Professor of Electrical and Computer Engineering and the Director of the KIOS Research Center for Intelligent Systems and Networks at the University of Cyprus. He received the B.A. degree in Computer Science and the B.Sc. degree in Electrical Engineering both from Rice University, Houston, TX, USA in 1987, and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Southern California, Los Angeles, CA, in 1989 and 1992 respectively. In 1992, he joined the University of Cincinnati, Ohio, USA, where he reached the rank of Professor of Electrical and Computer Engineering and Computer Science. In 2001, he was the first faculty to join the newly established Department of Electrical and Computer Engineering at the University of Cyprus, where he served as founding Department Chair from 2001 to 2008. His teaching and research interests are in intelligent systems and control, fault diagnosis, adaptive and cooperative control systems, computational intelligence and distributed systems. Dr. Polycarpou has published more than 240 articles in refereed journals, edited books and refereed conference proceedings, and co-authored the book Adaptive Approximation Based Control, published by Wiley in 2006. He is also the holder of 6 patents.
Title: Fault Detection and Isolation in Uncertain Big-Data Environments
Vincenzo Piuri is Full Professor in Computer Engineering at the University of Milan, Italy (since 2000), where he has also been Department Chair (2007-2012). He was Associate Professor at Politecnico di Milano, Italy (1992-2000), visiting professor at the University of Texas at Austin, USA (summers 1996-1999), and visiting researcher at George Mason University, USA (summers 2012-2013). He received his M.S. and Ph.D. in Computer Engineering from Politecnico di Milano, Italy.
Title: Computational Intelligence Technologies for 3D Surface Reconstruction
Barak Pearlmutter is a Professor in the Hamilton Institute at NUI Maynooth in Co. Kildare, Ireland with a primary appointment in the Department of Computer Science. He is the principal of Brain and Computation Lab.
Title: Critical Dynamics and Pathological Phenomena in the Brain
Steve Furber is the ICL Professor of Computer Engineering in the School of Computer Science at the University of Manchester. He received his B.A. degree in Mathematics in 1974 and his Ph.D. in Aerodynamics in 1980 from the University of Cambridge, England. From 1981 to 1990 he worked in the hardware development group within the R&D department at Acorn Computers Ltd, and was a principal designer of the BBC Microcomputer and the ARM 32-bit RISC microprocessor, both of which earned Acorn Computers a Queen's Award for Technology. Upon moving to the University of Manchester in 1990 he established the Amulet research group which has interests in asynchronous logic design and power-efficient computing, and which merged with the Parallel Architectures and Languages group in 2000 to form the Advanced Processor Technologies group. From 2003 to 2008 the APT group was supported by an EPSRC Portfolio Partnership Award. Steve served as Head of the Department of Computer Science in the Victoria University of Manchester from 2001 up to the merger with UMIST in 2004.
Title: The SpiNNaker Project
Giacomo Rizzolatti is a full professor of human physiology at the University of Parma, Italy. He studied Medicine and obtained the specialization in Neurology at Padua. He is the Senior Scientist of the research team that discovered mirror neurons in the frontal and parietal cortex of the macaque monkey, and has written many scientific articles on the topic. He is a past president of the European Brain and Behaviour Society. Rizzolatti was the 2007 co-recipient, with Leonardo Fogassi and Vittorio Gallese, for the University of Louisville Grawemeyer Award for Psychology.
Title: The Double Life of the Motor System: Action Production and Action Understanding
Technical Bio: Vladimir Cherkassky is Professor of Electrical and Computer Engineering at the University of Minnesota, Twin Cities. He received MS in Operations Research from Moscow Aviation Institute in 1976 and PhD in Electrical and Computer Engineering from the University of Texas at Austin in 1985. He has worked on theory and applications of statistical learning since late 1980’s and he has co-authored the monograph Learning from Data, now in its second edition. He is also the author of a new textbook Predictive Learning www.VCtextbook.com
Title: Methodological Aspects of VC-theory
Abstract: Vapnik-Chervonenkis theory (VC-theory), aka Statistical Learning Theory, provides mathematical framework for predictive data-analytic methods developed in machine learning, statistics, data mining, signal processing, bioinformatics etc. The VC-theory was developed in the late 1970’s in the former USSR. However, it became widely known only in mid-1990’s after introduction of SVMs. Current interest in VC-theory Theory can be measured by 31 million hits on Google search; likewise search on Support Vector Machines yields 6.9 million hits. Even though VC-theory is widely known as a mathematical theory, its methodological contributions are not well known or appreciated. This talk focuses on VC-theoretical methodology for estimating data-analytic predictive models. In particular, I will discuss important differences between knowledge discovery in classical science and modern data-analytic knowledge discovery. The classical framework encourages a popular view that knowledge can be discovered by applying readily available statistical/ machine learning software to the growing volumes of data, expressed as a view known as Big Data: more_data→ more_knowledge. An opposite view is that scientific inquiry starts with asking intelligent questions. That is, Science starts from problems, and not from observations (K. Popper). For data-analytic modeling, this view emphasizes proper formalization of application domain requirements and selecting proper type of inductive inference. To this end, VC-theoretical methodology clearly separates the problem setting from a learning method or algorithm. This talk will describe several non-standard learning settings (developed in VC-theory) and contrast them to standard inductive setting adopted in most machine learning and statistical algorithms. Various methodological issues will be illustrated using real-life application examples.
Bio: Anders Sandberg is a researcher, science debater, futurist, transhumanist and author. He holds a Ph.D. in computational neuroscience from Stockholm University, and is currently a James Martin Research Fellow at the Future of Humanity Institute at Oxford University. Sandberg's research centres on societal and ethical issues surrounding human enhancement and new technology, as well as on assessing the capabilities and underlying science of future technologies. His recent contributions include work on cognitive enhancement (methods, impacts, and policy analysis); a technical roadmap on whole brain emulation; on neuroethics; and on global catastrophic risks, particularly on the question of how to take into account the subjective uncertainty in risk estimates of low-likelihood, high-consequence risk. He is well known as a commentator and participant in the public debate about human enhancement internationally, as well as for his academic publications in neuroscience, ethics, and future studies. He is co-founder of and writer for the think tank Eudoxa, and is a co-founder of the Orion's Arm collaborative world building project. Between 1996 and 2000 he was Chairman of the Swedish Transhumanist Association. He was also the scientific producer for the neuroscience exhibition "Se Hjärnan!" ("Behold the Brain!"), organized by Swedish Travelling Exhibitions, the Swedish Research Council and the Knowledge Foundation, that toured Sweden in 2005–2006. In 2007 he was a postdoctoral research fellow at the Uehiro Centre for Practical Ethics at Oxford University, working on the EU-funded ENHANCE project on the ethics of human enhancement.
Title: Ethics and large-scale neural simulations: when do we need to start caring for networks, rather than about them?
Bio: CESARE ALIPPI received the degree in electronic engineering cum laude in 1990 and the PhD in 1995 from Politecnico di Milano, Italy. Currently, he is a Full Professor of information processing systems with the Politecnico di Milano. He has been a visiting researcher at UCL (UK), MIT (USA), ESPCI (F), CASIA (RC), A*STAR (SIN). Alippi is an IEEE Fellow, Distinguished lecturer of the IEEE CIS, Member of the Board of Governors of INNS, Vice-President education of IEEE CIS, Associate editor (AE) of the IEEE Computational Intelligence Magazine, past AE of the IEEE-Trans. Instrumentation and Measurements, IEEE-Trans. Neural Networks, and member and chair of other IEEE committees. In 2004 he received the IEEE Instrumentation and Measurement Society Young Engineer Award; in 2013 he received the IBM Faculty Award. Among the others, Alippi was General chair of the International Joint Conference on Neural Networks (IJCNN) in 2012, Program chair in 2014, Co-Chair in 2011. He was General chair of the IEEE Symposium Series on Computational Intelligence 2014. Current research activity addresses adaptation and learning in non-stationary environments and Intelligence for embedded systems. Alippi holds 5 patents, has published in 2014 a monograph with Springer on “Intelligence for embedded systems” and (co)-authored more than 200 papers in international journals and conference proceedings. Home Page: http://home.dei.polimi.it/alippi/
Title: Intelligence for Cyber-Physical and Embedded Systems
Abstract: The emergence of non-trivial embedded sensor units and cyber-physical systems has made possible the design and implementation of sophisticated applications where large amounts of real-time data are collected to constitute a big data picture as time passes. Acquired data are then processed at local, cluster-of-units or server level to take the appropriate actions or make the most suitable decision. Within this framework, intelligence mechanisms play a key role to provide systems with advanced functionalities. Intelligent mechanisms are needed to optimally harvest and manage the residual energy, identify possible faults affecting some sensors, reduce the communication bandwidth, solve the compromise between accuracy and computational complexity, guarantee appropriate performances within an evolving, time invariant environment. The talk will show how the use of intelligence can boost the next generation of embedded and cyber-physical-based applications, generation whose footprint is already around us.