Program at a glance (tentative)
Friday, 25-Feb
17:00-19:00	Social program
Saturday, 26-Feb
9:00-10:30	David Keyes, Dean, MCSE Computer science theory to support research in the information age John E. Hopcroft (Cornell University)
10:30-11:00	Coffee break
11:00-12:30 Session chair: Philip Yu	Machine Learning and Beyond Leslie Valiant (Harvard) Context-aware on-Demand Data Mining and Applications in Web Search Jian Pei (Simon Fraser University) Toward Perception-based Computing Andrzej Skowron (Warsaw University, Poland)
12:30-14:00	Lunch @ conference site
14:00-15:30 Session chair: Kurt Mehlhorn	Quantum Computing: A Great Science in the Making Andrew Chi-Chih Yao (Tsinghua University, Beijing) On the Arithmetic Complexity of Euler Function Manindra Agrawal (IIT Kanpur) Discrete Geometry and Its Applications Otfried Cheong (Korea Advanced Institute of Science & Technology)
15:30-16:00	Coffee break
16:00-17:30  Session chair: Alfred Aho	A Top-down Graph-based Approach to Frequent Pattern Mining Philip S. Yu (University of Illinois at Chicago) Embarrassingly Parallel Database Systems Anastasia Ailamaki (EPFL, Switzerland) Sustainable Computing and Computing for Sustainability: New Challenges in High-Performance Computing Ishfaq Ahmad (University of Texas at Arlington) Monte-Carlo Tree Search: From playing Go to Feature Selection and Active Learning Michele Sebag (INRIA)
17:30-19:00	Free time
19:00-20:30	Dinner @ KAUST yacht club
Sunday, 27-Feb
9:00-10:30 Session chair: Leslie Valiant	I'm in the Database, But Nobody Knows: An Introduction to Differential Privacy Cynthia Dwork (Microsoft) Processing information without learning it Mikhail Atallah (Purdue University)
10:30-11:00	Coffee break
11:00-12:30 Session chair: David Keyes	Quantum Computer Compilers Alfred V. Aho (Columbia University) Web Based Problem Solving Process (A 2020 Computer User) Teodor Rus (University of Iowa) Building Next Generation Massive Data Centers Mounir Hamdi (Hong Kong University of Science and Technology)
12:30-14:00	Lunch @ conference site
14:00-16:00 Session chair: Andrew Yao	Networked Control of Autonomous Systems Daniela Rus (MIT) Personalization, Socialization, Contextualization: Preferences and Attitudes for Advanced Information Provision Yannis Ioannidis (University of Athens) The Physarum Computer Kurt Mehlhorn (Max-Planck-Institut für Informatik) Algorithms for handling massive datasets Lars Arge (Aarhus University)
16:00-23:00	Social program - Dinner
Monday, 28-Feb
9:00-10:30	KAUST Museum tour
10:30-12:00	Tour of core facilities
12:00-	Free time / individual meetings

		The program in details
		Title: Computer science theory to support research in the information age Abstract: The last forty years have seen computer science evolve as a major academic discipline. Today the field is undergoing a fundamental change. Some of the drivers of this change are the internet, the World Wide Web, large quantities of information in digital form and wide spread use of computers for accessing information. The change is requiring universities to revise the content of computer science programs. This talk will cover the changes in the theoretical foundations of computer science needed to support the information age. John E. Hopcroft is the IBM Professor of Engineering and Applied Mathematics in Computer Science at Cornell University. He received his BS (1961) from Seattle University and his M.S. (1962) and Ph.D. (1964) in electrical engineering from Stanford University. His research centers on theoretical aspects of computer science. He served as dean of Cornell University’s College of Engineering from 1994 until 2001. He is a member of the National Academy of Sciences, of the National Academy of Engineering, and a fellow of the American Academy of Arts and Sciences, the American Association for the Advancement of Science, the Institute of Electrical and Electronics Engineers, the Association of Computing Machinery, and the Society of Industrial and Applied Mathematics. In 1986 he was awarded the A. M. Turing Award for his research contributions. In 1992, he was appointed by President George Bush to the National Science Board, which oversees the National Science Foundation, and served through May 1998. He received the IEEE Harry Goode Memorial Award in 2005, the Computing Research Association’s Distinguished Service Award in 2007, the ACM Karl V. Karlstrom Outstanding Educator Award in 2009, and the IEEE von Neumann Medal in 2010. He has honorary degrees from Seattle University, the National College of Ireland, the University of Sydney, St Petersburg State University and is an honorary professor at the Beijing Institute of Technology and at Yunnan University. He serves on the Packard Foundation’s Science Advisory Board, Microsoft Technical Advisory Board for Research Asia and the advisory boards of IIIT Delhi and Seattle University’s College of Engineering. The Chinese Academy of Sciences has designated him as an Einstein professor of the Academy.
		Title: Machine Learning and Beyond Abstract: Inductive learning is a cognitive phenomenon which has proved amenable both to theoretical analysis as well as practical exploitation. We have definitions of what goals need to be achieved when learning is done, and hence some understanding of the inherent possibilities and limitations of the phenomenon. As a technology machine learning has proved successful in a rapidly growing area of applications, aided by the availability of simple but highly effective learning algorithms, as well as of fast computers and plentiful data.     However, not all of cognition can be accounted for by inductive learning. The question we ask here is whether one can build on the success of machine learning to address the broader goals of artificial intelligence. We regard reasoning as the other main component of cognition. We suggest that the central challenge therefore is to unify the formulation of these two fundamental phenomena, learning and reasoning, with one common semantics into a single framework. We propose Robust Logic for this role. We shall describe this framework, as well as some experiments that seek to test it. Leslie Valiant is currently T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University, where he has taught since 1982. He was educated at King's College, Cambridge; Imperial College, London, and at Warwick University where he received his Ph.D. in computer science in 1974. Before coming to Harvard he had taught at Carnegie-Mellon University, Leeds University, and the University of Edinburgh. His work has ranged over several areas of theoretical computer science, including complexity theory, computational learning, and parallel computation. In these areas he is known for the notion of #P-completeness for classifying quantitative questions according to their computational difficulty, the PAC model of learning that defines what it means for a machine to learn, the bulk synchronous model of parallel computation, and the information routing method known as Valiant load-balancing. He also has active interests in computational neuroscience, evolution and the foundations of artificial intelligence. He received the Nevanlinna Prize at the International Congress of Mathematicians in 1986, the Knuth Award in 1997, and the EATCS award in 2008. He is a Fellow of the Royal Society (London) and a member of the National Academy of Sciences (USA).
		Title: Context-aware on-Demand Data Mining and Applications in Web Search Abstract: How can we make an application smarter?  The fast increasing availability of user data and the latest advances in data mining technology provide a unique opportunity to produce data driven applications smarter than ever before.  The critical trick is to develop context-aware on-demand data mining techniques, and integrate them into every step of applications.  In this talk, I will discuss some important building blocks for context-aware on-demand data mining, and use our experience in building a few search engine scale, data driven applications to illustrate the opportunities and the challenges. Jian Pei is currently an Associate Professor at the School of Computing Science, Simon Fraser University, Canada.  He received a Ph.D. degree in Computing Science from the same university in 2002, under Dr. Jiawei Han's supervision.  His research interests are to develop effective and efficient data analysis techniques for novel data intensive applications. Particularly, he is currently interested in various techniques of data mining, Web search, information retrieval, data warehousing, online analytical processing, and database systems, as well as their applications in social networks, business intelligence, and health-informatics. His research has been supported in part by government funding agencies such as the Natural Sciences and Engineering Research Council of Canada (NSERC) and the National Science Foundation (NSF) of the United States, as well as industry partners such as Microsoft, HP, IBM, and SAP BusinessObjects.  Currently, his priority in research is on developing industry relations and collaboration, and transferring technologies developed in his group to industry applications by building proof-of-concept prototypes.  He is the author of one textbook, two monographs and over 160 research papers in refereed journals and conferences.  He has regularly served in the organization committees and the program committees of the major conferences in his fields.  His publications have attracted more than 10,000 citations.  He is the associate editor-in-chief of IEEE Transactions of Knowledge and Data Engineering (TKDE), and an associate editor of ACM Transactions on Knowledge Discovery from Data (TKDD), Data Mining and Knowledge Discovery: An International Journal (DMKD), Statistical Analysis and Data Mining (SAM), and Intelligent Data Analysis: An International Journal (IDA). He is a senior member of the ACM and the IEEE.  He is the recipient of the British Columbia Innovation Council 2005 Young Innovator Award, an NSERC 2008 Discovery Accelerator Supplements Award, an IBM Faculty Award (2006), a KDD Best Application Paper Award (2008), and an IEEE Outstanding Paper Award (2007).
		Title: Toward Perception-based Computing Abstract: We discuss basic notions of Perception-based Computing (PBC). Perception is characterized by sensory measurements and ability to apply them to reason about satisfiability of complex vague concepts used, e.g., as guards for actions or invariants to be preserved by agents. Such reasoning is often referred as adaptive judgment. Vague concepts can be approximated on the basis of sensory attributes rather than defined exactly. Approximations usually need to be induced by using hierarchical modeling. Computations require interactions between granules of different complexity, such as elementary sensory granules, granules representing components of agent states, or complex granules representing classifiers that approximate concepts. We base our approach to interactive computations on generalized information systems and rough sets. We show that such systems can be used for modeling advanced forms of interactions in hierarchical modeling. Unfortunately, discovery of structures for hierarchical modeling is still a challenge. On the other hand, it is often possible to acquire or approximate them from domain knowledge. Given appropriate hierarchical structures, it becomes feasible to perform adaptive judgment, starting from sensory measurements and ending with conclusions about satisfiability degrees of vague target guards. Thus, our main claim is that PBC should enable users (experts, researchers, students) to submit domain knowledge, by means of a dialog. It should be also possible to submit hypotheses about domain knowledge to be checked semi-automatically. PBC should be designed more like laboratories (e.g.,  helping users in their research) rather than fully automatic data mining or knowledge discovery toolkit. In particular, further progress in understanding visual perception -- as a special area of PBC -- will be possible, if it becomes more open for cooperation with experts from neuroscience, psychology or cognitive science. In general, we believe that PBC will soon become necessity in many research areas. We discuss how granules such as complex vague concepts can be approximated using domain ontology approximation. This illustrates how the activation of complex granules can be realized, i.e., how, in the discussed model, the perception results are computed. Some real-life projects related, e.g., to medical decision support, identification of complex behavioral patterns on the road, sunspot classification or dialog based search engines are based on the discussed approach. Andrzej Skowron received the Ph. D. and D. Sci. (habilitation) from the University of Warsaw in Poland. In 1991 he received the Scientific Title of Professor. He is Full Professor in the Faculty of Mathematics, Computer Science and Mechanics at Warsaw University. Andrzej Skowron is the (co)author of more than 350 scientific publications. His areas of expertise include reasoning with incomplete information, approximate reasoning, soft computing methods and applications, rough sets, rough mereology, granular computing, synthesis and analysis of complex objects, intelligent agents, knowledge discovery and data mining, decision support systems, adaptive and autonomous systems. He was the supervisor of more than 20 PhD Thesis. He was also involved in several national and international research and commercial projects related to, e.g., data mining (fraud detection, web mining), control of unmanned vehicles, medical decision support systems and approximate reasoning in distributed environments. In the period 1995-2009 he was the Editor-in-Chief of Fundamenta Informaticae journal. He is the co-editor-in-chief of the LNCS Transactions on Rough Sets journal published by Springer. He is on Editorial Boards of many others journals including Knowledge Discovery and Data Mining. Andrzej Skowron was the President of the International Rough Set Society from 1996 to 2000. He served or is currently serving on the program committees of more than 100 international conferences and workshops, as program or steering committee member, program chair or co-chair. He has delivered numerous invited talks at international conferences including a plenary talk at the 16-th IFIP World Computer Congress (Beijing, 2000). Throughout his career Andrzej Skowron received many awards for his achievements.
		Title: Quantum Computing: A Great Science in the Making Abstract: In recent years, the scientific world has seen much excitement over the development of quantum computing, and the ever increasing possibility of building real quantum computers. What’s the advantage of quantum computing? What are the secrets in the atoms that could potentially unleash such enormous power, to be used for computing and information processing? In this talk, we will take a look at quantum computing, and make the case that we are witnessing a great science in the making. Andrew Chi-Chih Yao is currently the Dean of the Institute for Interdisciplinary Information Sciences, at Tsinghua University, Beijing. He received his BS in Physics (1967) from National Taiwan University, PhD in Physics (1972) from Harvard University, and PhD in Computer Science (1975) from the University of Illinois. From 1975 onward, Yao served on the faculty at MIT, Stanford, UC Berkeley, and during 1986 – 2004, as William and Edna Macaleer Professor of Engineering and Applied Science at Princeton University. In 2004, he left Princeton to become a Professor of Computer Science at Tsinghua Univeristy in Beijing. He is also a Distinguished Professor-at-Large at the Chinese University of Hong Kong. Yao’s research interests are in the theory of computation and its applications to cryptography and quantum computing. He is recipient of the prestigious A.M. Turing Award in year 2000 for his contributions to the theory of computation, including pseudorandom number generation, cryptography, and communication complexity. He has received numerous other honors and awards, including the George Polya Prize, the Donald E. Knuth Prize, and honorary degrees from the Chinese University of Hong Kong, the City University of Hong Kong, the Hong Kong University of Science and Technology, and the University of Waterloo. He is a member of the US National Academy of Sciences, the American Academy of Arts and Sciences, and the Chinese Academy of Sciences.
		Title: On the Arithmetic Complexity of Euler Function Abstract: Euler function is one of the basic function in the theory of modular forms. We investigate the complexity of computing approximations of Euler function and show that this problem is closely related to proving arithmetic circuit lower bounds on the permanent polynomial. Manindra Agrawal did his BTech (1986) and PhD (1991) from the department of Computer Science and Engineering at IIT Kanpur. After a brief stint at Chennai Mathematical Institute, he joined the department of Computer Science and Engineering at IIT Kanpur in 1996. He is N Rama Rao chair professor since 2003. Manindra works in theory of computation; specifically, in complexity theory, algorithmic number theory and algebra. His best known work is in algorithmic number theory: along with two of his students, he designed the first deterministic polynomial time algorithm for testing primality of a number. He is a fellow of Indian National Science Academy, Indian Academy of Sciences, National Academy of Sciences, and Indian National Academy of Engineers. He is a recipient of Clay Research Award, ICTP Prize, Distinguished Alumnus Award of IIT Kanpur, Godel Prize, Fulkerson Prize, Shanti Swarup Bhatnagar Award, and Infosys Mathematics Prize.
		Title: Discrete Geometry and Its Applications Abstract: Discrete geometry is hardly a "hot trend", nor is it computer science. Starting with problems considered by Kepler and Gauss, and heavily influenced by Erdos, discrete geometry studies the combinatorial structure of geometric objects.  We review some areas in discrete geometry that are of interest in computer science, and discuss some results on transversal theory and packings. Otfried Cheong received his Ph.D. at FU Berlin in 1992, and held faculty positions at Utrecht University, Pohang University of Science & Technology, Hong Kong University of Science & Technology, and TU Eindhoven before joining the Korea Advanced Institute of Science & Technology in 2005.  He has been a full professor there since 2009, and also holds an affiliate position in the Department of Mathematical Sciences. His research spans the areas of Discrete and Computational Geometry, and he supervises doctoral students from both departments. In 2010, he was responsible for the complete redesign of the freshman programming course taken by all KAIST freshmen.
		Title: A Top-down Graph-based Approach to Frequent Pattern Mining Abstract: Frequent pattern mining is a fundamental problem in data mining research. State-of-the-art algorithms are based on bottom-up approaches growing patterns step by step from short to long, hence cannot mine very long patterns in a large database. In this talk, we examine how to apply graph analysis techniques to perform frequent item-set mining. Here we focus on mining top-k long maximal frequent patterns because long patterns are in general more interesting ones. Different from traditional bottom-up strategies, the graph based approach works in a top-down manner. The approach pulls large maximal cliques from a pattern graph constructed after some fast initial processing, and directly uses such large-sized maximal cliques as promising candidates for long frequent patterns. A separate refinement stage is then applied to further transform these candidates into true maximal patterns.                                     Philip S. Yu received the B.S. Degree in E.E. from National Taiwan University, the M.S. and Ph.D. degrees in E.E. from Stanford University, and the M.B.A. degree from New York University. He is currently a Professor in the Department of Computer Science at the University of Illinois at Chicago and also holds the Wexler Chair in Information Technology.   He spent most of his career at IBM Thomas J. Watson Research Center and was manager of the Software Tools and Techniques group. His research interests include data mining, privacy preserving data publishing, data stream, Internet applications and technologies, and database systems. Dr. Yu has published more than 600 papers in refereed journals and conferences. He holds or has applied for more than 300 US patents.  Dr. Yu is a Fellow of the ACM and the IEEE.  He is an associate editor of ACM Transactions on Knowledge Discovery from Data.  He is on the steering committee of the IEEE Conference on Data Mining and ACM Conference on Information and Knowledge Management and was a member of the IEEE Data Engineering steering committee.  He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004). He had also served as an associate editor of ACM Transactions on the Internet Technology and Knowledge and Information Systems.  He had received several IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, 2 Research Division Awards and the 94th plateau of Invention Achievement Awards.  He was an IBM Master Inventor.  Dr. Yu received a Research Contributions Award from IEEE Intl. Conference on Data Mining in 2003 and also an IEEE Region 1 Award for "promoting and perpetuating numerous new electrical engineering concepts" in 1999.
		Title: Embarrassingly Parallel Database Systems Abstract: As the number of compute elements per chip increases exponentially, embarrassingly parallel hardware calls for embarrassingly scalable database applications on chip multiprocessors. Despite that database systems have long optimized for parallel execution, however, known methods are of bounded utility in harvesting scalable performance from the available raw parallelism. Common sense is often contradicted; for instance, increasing on-chip cache size or aggressively sharing data among processors is often detrimental to performance. As the number of hardware contexts grows, favoring scalability over single-thread performance leads to higher overall gains. This talk presents lessons learned when trying to scale database workloads on chip multiprocessors. When transforming a database storage manager from a single-threaded Atlas into a multi-threaded Lernaean Hydra which scales to infinity, major rethinking of basic constructs at all system levels is in order. At the system level, choosing a mechanism to access critical sections becomes crucial: spinning wastes cycles, while blocking incurs high overhead. When processing transactions, concurrency needs to be converted into parallelism – a challenging task, even for transactional workloads that are inherently concurrent. Often, parallelism needs to be extracted from seemingly serial operations; extensive research in distributed systems proves to be very useful in this context. When processing queries, service-oriented architectures provide an excellent framework to exploit available parallelism. The tradeoffs and trends in the above research directions will be discussed using examples from the StagedDB/CMP and ShoreMT projects at EPFL. Anastasia (Natassa) Ailamaki is a Professor of Computer Sciences at the Ecole Polytechnique Federale de Lausanne (EPFL) in Switzerland. Her research interests are in database systems and applications, and in particular (a) in strengthening the interaction between the database software and the underlying hardware and I/O devices, including flash technology, and (b) in automating database design and computational database support for scientific applications. She has received a Finmeccanica endowed chair from the Computer Science Department at Carnegie Mellon (2007), a European Young Investigator Award from the European Science Foundation (2007), an Alfred P. Sloan Research Fellowship (2005), six best-paper awards at top conferences (2001-2006), and an NSF CAREER award (2002). She earned her Ph.D. in Computer Science from the University of Wisconsin-Madison in 2000.
		Title: Sustainable Computing and Computing for Sustainability: New Challenges in High-Performance Computing Abstract: The realm of high-performance computing is emerging towards two related domains: The first is about curtailing electricity consumption of computers. This necessitates myriad innovations, such as designing sustainable hardware platforms, low power components, energy-efficient architectures, collection and modeling of temperature and power consumption data, re-inventing algorithms, and intelligent software control assisted by scheduling techniques. Equally important is the issue of restraining the temperature of computer systems so that their cooling costs can be reduced as well.     Since high-performance computing systems and data centers consume considerable amounts of electricity (for instance, an Exascale computer system, the next holy grail in ultra-high-speed computing, will need hundreds of megawatts of electricity, virtually requiring its own power plant), they exert a hefty toll on the consumption of precious natural resources such as oil and coal. The conversion of these resources to electricity in turn results in carbon emissions that can adversely affect the environment, a threat that is rapidly escalating. Thus, innovation, standardization, and inventive methodologies are vitally required at several fronts.     The second domain is the application of high-performance computing on creating sustainable environments and contributing towards a greener Earth. Examples include collection of sensory data for monitoring and modeling of geographical, oceanic and atmospheric environments, as well as their simulation and visualization. Several inter-disciplinary grand challenges lie ahead where computing, engineering and fundamental sciences can make everlasting societal and economical impacts.     In this talk, we present some of these research issues and present a few specific problem formulations and their solutions. Ishfaq Ahmad  received a B.Sc. in Electrical Engineering from the University of Engineering and Technology, Pakistan, in 1985, and an MS in Computer Engineering and a Ph.D. in Computer Science from Syracuse University, New York, U.S.A., in 1987 and 1992, respectively. Since 2002, he has been a professor of Computer Science and Engineering at the University of Texas at Arlington. Prior to that, he was an associate professor of computer science at the Hong Kong University of Science and Technology. His research focus is on the broader areas of parallel and distributed computing systems and their applications, optimization algorithms, multimedia systems, video compression, and energy-aware green computing.     Dr. Ahmad has received numerous research awards, including three best paper awards at leading conferences and the best paper award for the IEEE Transactions on Circuits and Systems for Video Technology, IEEE Service Appreciation Award, and Outstanding Area Editor Award from the IEEE Transactions on Circuits and Systems for Video Technology.  He was elevated to the IEEE Fellow grade in 2008.     Prof. Ahmad’s current research is funded by the U.S. Department of Justice, National Science Foundation, SRC, Department of Education, and several companies. He is the founding editor-in-chief of a new Journal, Sustainable Computing: Informatics and Systems, and a co-founder of the International Green Computing Conference. He is an editor of the Journal of Parallel and Distributed Computing, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Parallel and Distributed Systems, and Hindawi Journal of Electrical and Computer Engineering. He has guest edited several special issues and has been a member of the editorial boards of the IEEE Transactions on Multimedia and IEEE Concurrency.
		Title: Monte-Carlo Tree Search: From playing Go to Feature Selection and Active Learning Abstract: The game of Go replaced the game of chess as grand challenge of Artificial Intelligence since Deep Blue won against Kasparov in 1997. Two reasons for this fact are the size of the Go search space, defying brute force approaches, and the lack of any evaluation function, accurately characterizing the strength of a game position. Despite these difficulties significant advances have been made in computer-Go since 2006. The talk will briefly describe the MoGo algorithm, first to win against a professional human player (in 9x9 in 2007; with handicap 6 in 19x19 in 2009). The ingredients behind MoGo are deceptively simple: the algorithm plays against itself and gradually determines the best moves while addressing an Exploration vs Exploitation dilemma. Actually, many problems of optimization under uncertainty can be tackled as if they were games; the talk will show how the MoGo strategy can be used to achieve active learning and feature selection. Michele Sebag  With a scientific background in Mathematics (Ecole Normale Supérieure, aggregation of Maths, Master in Applied Maths), she went to Thales in 1980 where she learned by doing computer science as engineer, and project management as senior engineer. She became consulting engineer and pioneered Machine Learning for Numerical Engineering. She passed her PhD in 1990 and became researcher in CNRS in 1991 at Ecole Polytechnique. She took the lead of the Machine Learning group at Université Paris-Sud Orsay, and co-founded the TAO research group CNRS-INRIA with Marc Schoenauer in 2003. The specificity of the TAO group with respect to other machine learning groups worldwide lies in the theoretical and algorithmic coupling of machine learning and stochastic optimization; MoGo is among the algorithms best demonstrating the merits of such a tight coupling. Michele Sebag is member of the European Machine Learning and Knowledge Discovery from Databases Steering Committee, member of the PASCAL Network of Excellence Steering Committee, member of the editorial board of Machine Learning journal, of the Genetic Programming and Evolvable Hardware journal. She was program co-chair of the European Conference on Machine Learning, and Principles and Practice of Knowledge Discovery from Databases in 2010. She authored and co-authored over 100 refereed papers and chapters.
		Title: I'm in the Database, But Nobody Knows: An Introduction to Differential Privacy Abstract: The problem of statistical disclosure control - revealing accurate statistics about a population while preserving the privacy of individuals - has a venerable history. An extensive literature spans multiple disciplines: statistics, theoretical computer science, security, and databases.  Yet privacy breaches abound, both on paper and in practice. This talk introduces a large body of work revisiting the problem from the perspective of modern cryptography. We define differential privacy, a mathematically rigorous and comprehensive notion of privacy tailored to private data analysis.  We then present general techniques for achieving differential privacy while simultaneously preserving utility of the data, together with impossibility results that guided its development. Cynthia Dwork, a theoretical computer scientist, has made fundamental contributions to cryptography, distributed computing, and complexity theory. Her current focus is the development of a mathematically rigorous framework and algorithmic techniques for the privacy-preserving analysis of data. A Distinguished Scientist at Microsoft, Dwork is a recipient of the Edsger W. Dijkstra Prize and a member of the US National Academy of Engineering and the American Academy of Arts and Sciences.
		Title: Processing information without learning it Abstract: Although utility-computing technologies like cloud computing can yield substantial economic, social, and scientific benefits, there are impediments to achieving their full potential. One of the impediments is a reluctance to disclose information, for fear of losing control over its subsequent dissemination and usage.  Moreover, laws often forbid the disclosure of certain kinds of information (e.g., health, financial), or strictly regulate the form and timing of that disclosure.  We review security technologies that can mitigate this problem, and that allow data owners to enforce their approved purposes on their data.  We consider both the computational and the storage aspects of this problem.  This includes computational outsourcing, in which weak clients use powerful remote servers to carry out intensive computational tasks without revealing to the servers anything about the data or the computed answers; and storage outsourcing, in which storage-limited clients use remote servers to store/search/manipulate massive data without revealing to the servers anything about the data or the queries and updates on it. Mikhail (Mike) Atallah  obtained the Ph.D. from the Johns Hopkins University in 1982 and immediately joined the Computer Science Department at Purdue University, where he was promoted to Associate Professor in 1986, to Professor in 1989, and to Distinguished Professor in 2004.  His research interests include information security, distributed computing, algorithms, and computational geometry. A Fellow of both the ACM and IEEE, he has served on the editorial boards of top journals, and on the program committees of top conferences and workshops.  He was keynote and invited speaker at many national and international meetings, and a speaker nine times in the Distinguished Lecture Series of top Computer Science Departments. He was selected in 1999 as one of the best teachers in the history of Purdue and included in a permanent wall display of Purdue's best teachers past and present.
		Title: Quantum Computer Compilers Abstract: Quantum computing is an exciting emerging field that offers great potential for next generation information processing but also presents great scientific and engineering challenges. Assuming that someday we will be able to build scalable and reliable quantum computers, we will need to create programming languages and compilers that will allow programmers to harness quantum phenomena. In this talk, I will examine some of the formidable challenges that face quantum computer compiler writers. Alfred Aho is the Lawrence Gussman Professor in the Computer Science Department at Columbia University. He received a B.A.Sc. in Engineering Physics from the University of Toronto and a Ph.D. in Electrical Engineering/Computer Science from Princeton University. Prior to his current position, Aho served as vice president of the Computing Sciences Research Center at Bell Labs, the lab that invented UNIX, C and C++. He was also a member of technical staff, department head, and director of this Center. He served as Chair of the Department of Computer Science at Columbia University from 1995 to 1997. He has served as Chair of ACM's Special Interest Group on Algorithms and Computability Theory, and twice as Chair of the Advisory Committee for the National Science Foundation's Computer and Information Science and Engineering Directorate. Aho is well known for his many papers and books on algorithms and data structures, programming languages, compilers, and the foundations of computer science. He is the "A" in AWK, a widely used pattern-matching language. Aho's current research interests include programming languages, compilers, algorithms, software engineering, and quantum computing. Aho has won the IEEE John von Neumann Medal and is a Member of the U.S. National Academy of Engineering and the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science, ACM, Bell Labs, and IEEE. In 2003 he received the Great Teacher Award from the Society of Columbia Graduates.
		Title: Web Based Problem Solving Process (A 2020 Computer User) Abstract: I (perhaps like some of you) wonder lately what happens with Computer Science excitement. Student enrollment does no longer sustain the excitement we use to see in being a computer scientist; research topics do not come on the measure; money spent on various projects seem not justified by the results (though Web projects may seem to contradict this). It seems like Computer Science has reached a sort of maturity (like we felt about Geometry in 1960-s) where all fundamental problems have been solved (OS have been standardized, compiler construction and software engineering are no longer of interest, formal methods are obsolete, etc., etc., etc.) I believe that CS is far from lacking stamina for excitement. Like Geometry in 1960-s, currently CS may be at a crossroads point where its maturity reached a level at which computer technology became free to move out of the hands of CS experts, exactly like geometric modeling moved into Computation Geometry and Robotics. This talk proposes a potential path to recover computer science excitement. This will be done by contrasting the Computer Based Problem Solving Process (CBPSP), as performed by computer experts, with the Web Based Problem Solving Process (WBPSP), as performed by problem domain experts (not necessary computer experts). We will identify software tools computer experts need to develop for non-experts in order to transform the computer from a universal number crunching tool into a domain dedicated cognitive tool. This metamorphosis will show that computer science excitement is alive and barely scratched. I and my PhD students demonstrate this with a problem solving methodology where problem solver uses the natural language of the problem domain as a computer language. We show how this process evolves the application domain with new concepts exactly as the universe of discourse of the domain expert is evolved by the natural learning process. Teodor Rus is a professor of computer in the Department of Computer science, University of Iowa, Iowa City, IA. In his long Computer Science carrier Teodor Rus crossed from Academia to Industry and from industry to academia. His main industry contribution are in the design and implementation of programming languages (he was part of Ada Europe Group) and the design and implementation of operating systems (he was part of French CII IRIS Operating Systems). His main contribution in Academia are many books and papers on software system design and implementation, algebraic methodology and software technology, and computation theory. He taught such diverse courses as introduction to system software, compiler construction, operating systems, genetic algorithms, parallel programming, computational linguistics, theory of computation. Recently his interest focuses on software development for no-expert computer users.
		Title: Building Next Generation Massive Data Centers Abstract: Data center infrastructure design has recently been receiving significant research interest both from academia and industry, in no small part due to the growing importance of data centers in supporting and sustaining the rapidly growing web-based applications including search (e.g., Google, Bing), video content hosting and distribution (e.g., YouTube, NetFlix), social networking (e.g., facebook, twitter), and large-scale computations (e.g., data mining, bioinformatics, indexing). Today's data centers may contain tens of thousands of computers with significant aggregate bandwidth requirements. For example, the Microsoft Live online services are supported by a Chicago-based data center, which is one of the largest data centers ever built, spanning more than 700,000 square feet, and Google has more than 1 Million servers. As a result, the architecture of the network interconnecting the servers has a significant impact on the agility and reconfigurability of the data center infrastructure to respond to changing application demands and service requirements. Traditionally data center networking was based around top of rack (ToR) switches interconnected through end of rack (EoR) switches, and these in turn are being connected through core switches. This approach, besides being very costly, leads to significant bandwidth oversubscription towards the network core. This prompted several researchers to suggest alternate approaches for scalable cost-effective network infrastructures, based on topologies including Fat-Tree, DCell, BCube, MDCube, and Clos network. In this talk, we detail the trends and challenges in designing massive data centers. We will highlight the research efforts being undertaken by the academic and industrial communities to address these challenges. Finally, we present some of our own solutions by leveraging the key data traffic patterns and web-applications in achieving scalable and cost effective solutions to the design of massive data centers infrastructures. Mounir Hamdi is a Chair Professor at the Hong Kong University of Science and Technology, and the Head of the Department of Computer Science and Engineering. He is an IEEE Fellow for contributions to design and analysis of high-speed packet switching. He received the B.S. degree in Electrical Engineering - Computer Engineering minor (with distinction) from the University of Louisiana in 1985, and the MS and the PhD degrees in Electrical Engineering from the University of Pittsburgh in 1987 and 1991, respectively. He has been a faculty member in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology since 1991, where he was a founding member of the University and the Department. He is now the Head and Chair Professor of the Department that has around 800 undergraduate and graduate students. He was the Director of the Computer Engineering Program (for 10 years) that has around 330 undergraduate students, Director of the Master of Science in Information Technology (for 3 years) which has more than 150 graduate students, and Director of the Computer Engineering research Lab. He is/was a member of the University Senate and University Council. In 1999 to 2000 he held visiting professor positions at Stanford University, USA, and the Swiss Federal Institute of Technology, Lausanne, Switzerland. His general area of research is in high-speed wired/wireless networking in which he has published more than 300 research publications, received numerous research grants, and graduated more 30 graduate students. In addition, he has frequently consulted for companies and governmental organizations in the USA, Europe and Asia on high-performance Internet routers and switches as well as high-speed wireless LANs. He is a frequent keynote speaker in International Conferences and Forums. Prof. Hamdi is/was on the Editorial Board of various prestigious journals and magazines including IEEE Transactions on Communications, IEEE Communication Magazine, Computer Networks, Wireless Communications and Mobile Computing, and Parallel Computing as well as a guest editor of IEEE Communications Magazine, guest editor-in-chief of two special issues of IEEE Journal on Selected Areas of Communications, and a guest editor of Optical Networks Magazine. He has chaired more than 15 international conferences and workshops including The IEEE International High Performance Switching and Routing Conference, the IEEE GLOBECOM/ICC Optical networking workshop, the IEEE ICC High-speed Access Workshop, and the IEEE IPPS HiNets Workshop, and has been on the program committees of more than 200 international conferences and workshops. He was the Chair of IEEE Communications Society Technical Committee on Transmissions, Access and Optical Systems, and Vice-Chair of the Optical Networking Technical Committee, as well as member of the ComSoc technical activities council. He received the best paper award at the IEEE International Conference on Communications in 2009 and the IEEE International Conference on Information and Networking in 1998. He also supervised the best PhD paper award amongst all universities in Hong Kong. In addition to his commitment to research and professional service, he is also a dedicated teacher and renowned quality-assurance educator. He received the best 10 lecturers award (through university-wide student voting for all university faculty held once a year), the distinguished engineering teaching appreciation award from the Hong Kong University of Science and Technology, and various grants targeted towards the improvement of teaching methodologies, delivery and technology.
		Title: Networked Control of Autonomous Systems Abstract: In this talk I will discuss the challenges of building brains and bodies to create mobile autonomous systems that can interact in new ways with the physical world, on the ground, in water, and in the air. I will focus on recent progress in Autonomous Mobile Networks, which are distributed ad-hoc networks of robots that can sense, actuate, compute and communicate with each other using point-to-point multi-hop communication. The nodes in such networks include static sensors, mobile sensors, robots, animals, and humans. Such systems combine the most advanced concepts  in perception, communication and control to create computational systems capable of large-scale interaction with the environment, extending the individual capabilities of each network component to encompass a much wider area, range of data, and control capabilities. Daniela Rus  is a Professor of Electrical Engineering and Computer Science, where she is associate director of MIT's Computer Science and Artificial Intelligence Lab (CSAIL) and co-directs the MIT Center for Robotics at CSAIL. Her research interests include distributed robotics and mobile computing and her application focus includes transportation, security, environmental modeling and monitoring, underwater exploration, and agriculture. Rus is the recipient of the NSF Career Award and an Alfred P. Sloan Foundation Fellow.  She is a Class of 2002 MacArthur Fellow and a fellow of AAAI and IEEE. Before receiving her appointment at MIT, Rus was a professor in the Computer Science Department at Dartmouth, where she founded and directed two laboratories in robotics and mobile computing. Rus earned her PhD in Computer Science from Cornell University.
		Title: Personalization, Socialization, Contextualization: Preferences and Attitudes for Advanced Information Provision Abstract: Human actions in real life are often influenced by several characteristics of the individual human involved in the actions. These characteristics can be broadly classified into three categories: those that are unique to the individual, those of the social environment of the individual, and those of the overall context or situation in which the individual is found while performing the actions. Usability of various types of information systems, e.g., database systems, digital libraries, or the Web, increases dramatically if the information they provide and their overall behavior is customized to these characteristics. Such personalization, socialization, and contextualization of information provision touches upon a broad spectrum of technical and other challenges. This talk describes the general problem and its associated challenges, hints upon a general framework for modeling a large number of cases, and offers some examples of systems and techniques that address related challenges in various application environments. Yannis Ioannidis   is currently a Professor at the Department of Informatics and Telecommunications of the University of Athens. He received his Diploma in Electrical Engineering from the National Technical University of Athens, his MSc degree in Applied Mathematics from Harvard University, and his Ph.D. degree in Computer Science from the University of California at Berkeley. He was on the faculty of the Computer Sciences Department of the University of Wisconsin at Madison, where he became a Professor. His research interests include database and information systems, electronic infrastructures, digital libraries, personalization, scientific systems, and human-computer interaction, topics on which he has published over one hundred articles in leading journals and conferences and holds three patents. Dr. Ioannidis is an ACM and IEEE Fellow and has received the Presidential Young Investigator (PYI) award, the VLDB "10-Year Best Paper Award", the nation-wide "Xanthopoulos-Pnevmatikos Award for Outstanding Academic Teaching" in Greece, and several other teaching awards. He has been a program (co-)chair of several conferences and a (co-)principal investigator in over thirty research projects funded by various government agencies (Europe, Greece, USA) or private industry. Dr. Ioannidis currently serves a 4-year term as the ACM SIGMOD Chair (following a 4-year term as Vice-Chair) and is or has been a member of several other executive bodies of professional organizations (VLDB Endowment, IEEE TCDE Executive Committee, EDBT Endowment) and Scientific Advisory Boards (Max Planck Institute for Informatics, Greek National Science & Technology Council, Information Technology advisor to the Greek Minister of Health). He has recently been elected to serve a five-year term as the Director of the ATHENA Research Center.
		Title: The Physarum Computer Abstract: Physarum is a slime mold. It was observed over the past 10 years that the mold is able to solve shortest path problems and to construct good Steiner networks [2, 4]. In a nutshell, the shortest path experiment is as follows: A maze is built and the mold is made to cover the entire maze. Food is then provided at two positions s and t and the evolution of the slime is observed. Over time, the slime retracts to the shortest s-t-path.     A mathematical model of the slime's dynamic behavior was proposed in [3]. The network is modelled as an undirected graph G = (V,E) with two special vertices s and t. Each edge e has a diameter De and a length Le. The length is fixed and the diameter changes over time. A flow of one is sent from s to t according to the following rule. The network is interpreted as an electrical network where the edge e has resistance Le / De. Each vertex v has a potential pv; pt = 0. For an edge e = (u, v), we have a flow Qe = (pu - pv)De / Le. If Qe < 0, the flow is in the direction from v to u. We have flow conservation in all vertices except for s and t. The net flow out of s is one and the net flow into t is one. The edge diameters develop according to the rule                                                         D'e = |Qe| - De, where D'e is the derivative of De with respect to time. If the current flow is larger (smaller) than the current diameter, the diameter increases (decreases).     Extensive computer simulations confirm the experimental findings. For the edges on the shortest s-t-path, the diameter converges to one, and for the edges off the shortest path, the diameter converges to zero.     For some simple graphs, convergence proofs are available. In particular, if G is a planar graph with s and t being on the boundary of the same face, convergence is shown in [1].     We review the work on the Physarum computer. References T. Miyaji and Isamu Ohnishi. Physarum can solve the shortest path problem on riemannian surface mathematically rigourously. International Journal of Pure and Applied Mathematics, 47:353-369, 2008. T. Nakagaki, H. Yamada, and Á. Tóth. Maze-solving by an amoeboid organism. Nature, 407:470, 2000. A. Tero, R. Kobayashi, and T. Nakagaki. A mathematical model for adaptive transport network in path finding by true slime mold. Journal of Theoretical Biology, pages 553-564, 2007. A. Tero, S. Takagi, T. Saigusa, K. Ito, D. Bebber, M. Fricker, K. Yumiki, R. Kobayashi, and T. Nakagaki. Rules for biologically inspired adaptive network design. Science, 327:439-442, 2010. Kurt Mehlhorn Prof. Dr. Director at the Max-Planck-Institute for Computer Science. Born 1949, Ph.D. (Cornell) in 1974, professor in Saarbrücken since 1975. Awards: Leibniz-Award (1986), Humboldt-Award (1989), Karl-Heinz-Beckurts-Award (1994), EATCS-Award (2010), Member of Academia Europaea (1995), of German National Academy of Science and of Acatech (German Academy of Engineering) Konrad-Zuse-Award (1995), ACM Fellow (1999), Honorary doctorate degrees from Magdeburg, Waterloo and Aarhus. Research interests: data structures, graph algorithms, computational geometry, parallel algorithms, implementation of algorithms, algorithm engineering, natural computation, software libraries.  Publications: Kurt Mehlhorn has published 6 books and more than 200 articles in scientific journals and conferences.
		Title: Algorithms for handling massive datasets Abstract: The pervasive use of computers, as well as tremendous advances in the ability to acquire, store and process data, has resulted in a spectacular increase in the amount of data being collected. The availability of high-quality data has in turn lead to many new applications, as well as many new scientific discoveries. Unfortunately, the increased dataset sizes has also revealed scalability problems in many applications. Often these problems are a result of the underlying algorithms not taking the hierarchical structure of modern memory systems into account (and especially the large difference in the access time of main memory and disk). In this talk we will discuss these problems further and describe some of the massive data algorithms research being performed in Center for Massive Data Algorithms (MADALGO) funded by the Danish National Research Foundation. We will especially focus on the centers work on algorithms that minimize the number of accesses to disk - so-called I/O-efficient algorithms - and give examples of massive terrain data applications (especially flood risk modeling applications) where the use of I/O-efficient algorithms has led to large practical runtime improvements. Lars Arge is a Professor of Computer Science at Aarhus University and director of the Danish National Research Foundation Center for Massive Data Algorithmics (MADALGO). He received his Ph.D. in Computer Science from Aarhus University in 1996 and until August 2004 he was on the faculty (as Assistant, Associate and full Professor) at Duke University. His main research interests lie in the area of memory-hierarchy efficient algorithms, especially I/O-efficient algorithms for problems involving massive datasets. He has worked on massive dataset problems in many fundamental areas, but much of his work has been on I/O-efficient algorithms and data structures for problems with practical applications in geographic information systems and spatial databases. He has investigated the practical merits of theoretically developed I/O-efficient algorithms in projects such as TPIE, TerraFlow, STREAM and TerraStream. Arge has published more than 80 refereed papers and 5 book chapters. He has served on numerous program committees (including as chair of ICALP, ESA and SWAT), and serves on the editorial boards of Algorithmica, Journal of Discrete Algorithms, Journal of Graph Algorithms and Applications, and Journal of Experimenteal Algorithmics. He is an elected member of the Royal Danish Academy of Science and Letters, and the recipient of the Danish Minister of Research Elite Research Award, a Ole Roemer Scholarship from the Danish National Research Council, and a CAREER award from the US National Science Foundation. He is the co-founder and chairman of the company Scalable Algorithmics (SCALGO) established to commercialize the TerraStream software package for efficient processing of massive terrain data.

For more information, contact Panos Kalnis