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Lecturers

Francesco Bonchi is a researcher at Y! research Barcelona. He participated in the European project GeoPKDD. He is a member of several international conference program committees. He was the program co-chair of The Fourth International Workshop on Knowledge Discovery on Inductive Databases (KDID 2005).

Curriculum Vitae

Research Projects

• GeoPKDD (partner in EU project)
• KDubiq (partner in EU project)
  
  

Committees

• Tutorial Selection Committee Member of The 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006)
• PC Member of The 8th International Conference on Data Warehousing and Knowledge Discovery (DaWaK2006), ACM 21st Symposium on Applied Computing (SAC 2006) - Data Mining Track, The Fifth IEEE International Conference on Data Mining (ICDM 2005), The 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2005), The First International Conference on Advanced Data Mining and Applications (ADMA 2005), The Third International Workshop on Knowledge Discovery in Inductive Databases (KDID 2004)
• Program co-chair of The Fourth International Workshop on Knowledge Discovery in Inductive Databases (KDID 2005)
• Reviewer for the journals: Knowledge and Information Systems - An International Journal (KAIS), IEEE - Internet Computing (IEEE-IC), Data and Knowledge Engineering (DKE)
• Organizing Committee member of The 15th European Conference on Machine Learning and the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2004)
• Co-chair of The Fouth International Workshop on Knowledge Discovery in inductive Databases (KDID 2005)

Current interests

• data mining query language and optimization
• frequent pattern mining
• privacy-preserving data mining
• bioinformatics
  
  

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Philippe Bonnet is an Associate Professor for Computer Science at the University of Copenhagen leading the Sensor Networks Group. He is an Associate Editor of the International Journal of Distributed Sensor Networks.

Curriculum Vitae

Books

• Dennis Shasha and Philippe Bonnet. Database Tuning: Principles, Experiments and Troubleshooting Techniques. Morgan Kaufmann, 2002.

Research Projects

• Hogthrob (Sensor Network Infrastructure for Sow Monitoring)
• Badger (Performance of Database Systems on Modern Computer Architectures)
• EU projects: Wisents collaboration action, CRUISE Newtwork of Excellence)

Committees

• Member of the TinyOS Alliance Working Group
• Program Committee Co-chair: ACM Sensys (2003, 2004), ACM SIGMOD (2002), VLDB (2004, 2006), ICDE (2004, 2005, 2008), CIDR 2007, DCOSS 2007
• ICDE 2008 vice-area chair on sensor networks
• DMSN 2007
• IPSN 2008
• Associate Editor for ACM Transactions on Sensor Networks (TOSN)
• Member of the Editorial Advisory Board of Information Systems

Current interests

• Data Acquisition
• Database Tuning
• Autonomous systems
• Scientific Data Management

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João Gama is a researcher at the Laboratory of Artificial Intelligence and Decision Support-INESC Porto LA, one of the R&D Units of the University of Porto. He organized several international workshops on Knowledge Discovery from Stream (Sensor) Data and co-chaired ECML 2005. He is a member of the Editorial Board of the Intelligent Data Analysis Journal and New Generation Computing.

Curriculum Vitae

Books

• Learning from Data Streams -- Processing Techniques in Sensor Networks J. Gama, M. Gaber (Eds), Springer Verlag, 2007
• Machine Learning: ECML 2005, 16 European Conference on Machine Learning, Gama, J., R. Camacho, P. Brazdil, A. Jorge, L. Torgo (Eds) Porto, Portugal, October 2005, Springer
• Knowledge Discovery in Databases: PKDD 2005, 9 European Conference on Principles and Practice of Knowledge Discovery in Databases, Jorge, A., L. Torgo, P. Brazdil, R. Camacho, J. Gama (Eds), Porto, Portugal, October 2005, Springer

Research Projects

• Real Time Network Analysis and Enhancement (2005-07)
• Adaptive Learning Systems II (2004-2008)
• Adaptive Learning Systems - ALES (2003-2005)

Committees

• Member of the Editorial Board of the Intelligent Data Analysis Journal
• Member of the Editorial Board of the New Generation Computing
• Member of the APPIA (Associacao Portuguesa para a Inteligencia Artificial)
• Member of the IEEE

Current interests

• Learning from Data Streams: models and algorithms
• Machine Learning and Data Mining

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Andreas Hotho is a researcher at the University of Kassel, where he leads the development of the social bookmark and publication sharing tool BibSonomy. He was involved in organizing several workshops at machine learning as well as semantic web conferences.

Curriculum Vitae

Books

• B. Berendt and A. Hotho and D. Mladenic and G. Semeraro, editor(s). From Web to Social Web: Discovering and Deploying User and Content Profiles, Springer, 2007.
• Markus Ackermann and Bettina Berendt and Marko Grobelnik and Andreas Hotho and Dunja Mladenic and Giovanni Semeraro and Myra Spiliopoulou and Gerd Stumme and Vojtech Svatek and Maarten van Someren editor(s). Semantics, Web and Mining, 2006.
• Bettina Berendt and Andreas Hotho and Dunja Mladenic and Maarten van Someren and Myra Spiliopoulou and Gerd Stumme, editor(s). Web Mining: From Web to Semantic Web, LNAI, (3209) Springer, Heidelberg, 2004.

Research Projects

• Tagora: Semiotic Dynamics in Online Social Communities, STREP project funded by the European Commission in the framework of the FET proactive initiative “Simulating Emergent Properties in Complex Systems”
• Microsoft Live Labs: Accelerating Search in Academic Research 2006 RFP Awards
• Research Project with the Deutsche Telekom: Analysis of Losses due to Competition on the Telecommunication market and possible Actions

Committees

• Member of the Program Committee: ECML/PKDD 2005-2007, ICDM 2006-2007, DaWak 2003-2007, AWIC 2004-2007, ECAI 2006, ICFCA 2007
• workshop co-chair of the International Workshop on Data Mining in Web 2.0 Environments 2007, at ICDM 2007
• Workshop co-chair of the Bridging the Gap between Semantic Web and Web 2.0, 2007, at ESWC 2007
• Workshop co-chair of the Workshop on Web Mining 2006 (WebMine), at ECML/PKDD
• Workshop co-chair of the ICML Workshop: Learning in Web Search 2005
• Workshop co-chair of the 1st European Web Mining Forum (EWMF'03), at ECML/PKDD
• Workshop co-chair of the Semantic Web Mining at ECML/PKDD 2001/2002

Current interests

• Data, Text and Web Mining
• Analysis of Web 2.0 systems
• Information Retrieval

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Murat Kantarcioglu is an Assistant Professor for Computer Sciences at the University of Texas at Dallas since his Ph. D. in 2005. His dissertation was supervised by Chris Clifton. He has been a member of several international conference program committees such as ICDE, AAAI, and IEEE ICDM.

Curriculum Vitae

Books

• Privacy-Preserving Data Mining, Chris Clifton, Murat Kantarcioglu and Jaideep Vaidya in: Foundations and Advances in Data Mining, Wesley Chu and T.Y. Lin, eds., Studies in Fuzziness and Soft Computing vol. 180, Springer-Verlag, 2005.
• Defining Privacy for Data Mining, Chris Clifton, Murat Kantarcioglu, and Jaideep Vaidya in: Data Mining: Next Generation Challenges and Future Directions, AAAI Press, pp. 255-272, 2004

Research Projects

• “Systematic Control and Management of Data Integrity, Quality and Provenance for Command and Control Applications”, Funded by Air Force Office of Scientific Research
• “Assured Information Sharing Using Secure Coprocessors”, Funded by Department of Defense Information Assurance Scholarship Program

Committees

• 10th Asia-Pacific Web Conference (APWeb'08)
• 24th International Conference on Data Engineering, (ICDE 2008)
• 9th International Conference on Data Warehousing and Knowledge Discovery (DAWAK) 07
• First ACM SIGKDD International Workshop on Privacy, Security, and Trust in KDD (PinKDD'07)
• Workshop on Data Mining of Uncertain Data (DUNE '07
• Twenty-Second AAAI Conference on Artificial Intelligence (AAAI-07)
• Workshop on Data Mining and Business Intelligence (DMBI 2007)
• International Workshop on Privacy Aspects of Data Mining (PADM'06)
• IEEE International Conference on Data Mining, (ICDM 06)
• European Conf. on Principles of Data Mining and Knowledge Discovery (PKDD) '05

Current interests

• Security and Privacy issues raised by Data Mining
• Distributed Data Mining Techniques
• Security and Privacy issues in Databases
• Applied Cryptography & Secure Multi-Party Computation Techniques
• Use of Data-Mining for intrusion and fraud detection

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Hillol Kargupta is an Associate Professor for Computer Science and Electrical Engineering at the University of Maryland, Baltimore County and the President of Agnik. He received several awards for his pioneering work on distributed data mining. He was program chair of the 2005 SIAM ICDM 2005 and program area chair of ECML/PKDD 2005, IEEE ICDM 2005, and vice-chair of Euro-PAR 2005. He is an Associate Editor of the IEEE Transactions on Knowledge and Data Engineering among others.

Curriculum Vitae

Books

• Hillol Kargupta, Anupam Joshi, Krishnamoorthy Sivakumar, Yelena Yesha (2004) "Data Mining: Next Generation Challenges and Future Directions " AAAI/MIT Press
• Hillol Kargupta, Philip Chan (2000) "Advances in Distributed and Parallel Knowledge Discovery" AAAI/MIT Press

Research Projects

• Grants from NSF and NASA
• Projects supported by the TRW Research Foundation, American Cancer Society, US Department of Energy and Caterpillar

Committees

• Associate editor of the IEEE Transactions on Knowledge and Data Engineering and IEEE Transactions on Systems, Man, Cybernetics, Part B
• Program co-chair of the 2005 SIAM Data Mining Conference
• Program vice-chair of 2005 PKDD Conference (2005), IEEE International Data Mining Conference (2005), Euro-PAR Conference (2005)
• Associate General chair of the 2003 ACM SIGKDD Conference
• Member of the organizing committee for the 2001, 2002, 2003 & 2004 SIAM Data Mining Conference
• Member of the program committees of IEEE Data Mining Conference (2002, 2003), ACM SIGKDD Conference (2001)
• Host of the 2001/2002/2003 High Performance, Pervasive, and Data Stream Mining workshops in SIAM Data Mining Conferences
• Organization of ACM SIGKDD-2000 workshop on Distributed and Parallel Knowledge Discovery (DPKD)
• Organization of KDD-98 workshop on distributed data mining

Current interests

• His research interests include mobile and distributed data mining and computation in gene expression.

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Peter Marwedel is full professor for Computer Science at the University Dortmund, Germany, since 1989. He participated in numerous projects and has published several books on embedded systems. He received the teaching award of the University of Dortmund in 2003 and gives courses on embedded systems at leading universities. Also, he is heading ICD, a local spin-off, and is a member of the ARTIST and Hipeac networks of excellence on embedded systems.

Curriculum Vitae

Books

• P. Marwedel (1993) "Synthese und Simulation von VLSI-Systemen (Synthesis and Simulation of VLSI-Systems)"
• P. Marwedel, G. Goossens (1995) "Code generation for embedded processors" Kluwer Academic Publishers
• Rainer Leupers, Peter Marwedel (2001) "Retargetable Compiler Technology for Embedded Systems - Tools and Applications" Kluwer Academic Publishers
• Peter Marwedel (2003) "Embedded System Design" Kluwer Academic Publishers; 2nd edition: (2005) Springer
• Heiko Falk, Peter Marwedel (2004) "Source Code Optimization Techniques for Data Flow Dominated Embedded Software" Kluwer Academic Publishers
  
  

Research Projects

• Design of a "MIMOLA processor" (German)
• Design of MIMOLA high-level synthesis techniques (German)
• SALLY: Design of a comprehensive high-level synthesis environment (German)
• Generation of self-test programs for processors (German)
• LINK-project: Development of the OSCAR high-level synthesis tool (EU project)
• CHIPS-project: Design of code generation tools (EU project)
• Rapid prototyping: Development of code generation techniques for embedded processors (German)
• Low-power code generation techniques (German)
• SIMBA-project: Multimedia project "Visualization of computer architecture" (subproject RaVi) (German)
  
  

Committees

• CEO of "Informatik Centrum Dortmund e.V." (ICD), head of the embedded systems group at ICD
• Representative of DATE to ASPDAC
• Member of the editorial boards of the ACM Transactions on Embedded Computing Systems, Journal of Embedded Computing, Microelectronics Journal
• Member of the core team of the ARTIST 2 proposal for a European Union Network on Real-Time and Embedded Systems
  
  

Current interests

• Embedded software
• Low-power software
• Compilers for embedded processors
• Multimedia components for teaching computer engineering
• High-level synthesis
• Test program generation for processors
  
  

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Katharina Morik is a full professor for Computer Science at the University of Dortmund since 1991 and participated in numerous European projects on Machine Learning and Knowledge Discovery. She is a co-founder of IEEE ICDM and the program chair of IEEE ICDM 2002. She was the program chair of ECML 1989 and will be the co-chair of ECML/PKDD 2008.

Curriculum Vitae

Books

• Katharina Morik, Stefan Wrobel, Jörg-Uwe Kietz, Werner Emde (1993) "Knowledge Acquisition and Machine Learning - Theory, Methods, and Applications." Academic Press
• Katharina Morik, Michael Kaiser, Volker Klingspor (eds) (1999) "Making Robots Smarter -- Combining Sensing and Action through Robot Learning" Kluwer, Katharina Morik, Jean-Francois Boulicaut, Arno Siebes (eds) (2005) "Detecting Local Patterns", Springer
• Katharina Morik, Volker Klingspor (2005) "Informatik kompakt -- eine grundlegende Einführung mit Java", Springer

Research Projects

• HAMburg Application Oriented Natural Language System (HAM-ANS) (German)
• KIT-Lerner (German)
• Machine Learning Toolbox (partner in EU project)
• BLearn II (partner in EU project)
• Learning in Humans and Machines (partner in ESF project)
• COMRIS partner in EU project)
• AWAKE (partner of German project)
• MiningMart (coordinator of EU project)
• Member in the Networks of Excellence: Machine Learning, Inductive Logic Programming, Knowledge Discovery
• Member of the EU coordinated action KDubiq
• 2000 - 2003 Member of the Collaborative Research Centre for Computational Intelligence (SFB 531)
• 1997 - 2006 Member of the Collaborative Research Centre for Complexity Reduction in Multivariate Data Structures (SB 475)

Committees

• Organization of the "First European Summer School on Machine Learning" together with Yves Kodratoff, 1988
• Program chair of the 4th European Working Session on Machine Learning (ECML), 1989
• Organization of the "Intensive Course on Artificial Intelligence: Data Mining and Knowledge Discovery", Univ. Dortmund, 1999
• Member of the Steering Committee and Program Committee of the First IEEE International Conference on Data Mining (ICDM) 2001
• Organization of the IJCAI-Workshop "Learning from Temporal and Spatial Data" together with Mirek Kubat, Willi Klösgen, Ryszard Michalski, 2001
• Program chair of ICDM 2004 together with Rajeev Rastogi
• Organization of the Dagstuhl Workshop "Detecting Local Patterns" 2004
• Regularly member of the program committees of ICDM, ECML, PKDD, IDA, SIAM
• Member of the editorial board of "Knowledge and Information Systems", Springer

Current interests

• Interests are in numerical methods (support vector machine) as well as in logic appoaches (inductive logic programming) to knowledge discovery and also take into account cognitive aspects.

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Rasmus Ulslev Pedersen is Assistant Professor at Department of Informatics at the Copenhagen Business School, where he manages the Embedded Software Lab. He has given several tutorials on intelligent ubiquitous systems.

Curriculum Vitae

Books

• R. Pedersen. Two Book Chapters in "Learning from Data Streams: Processing Techniques in Sensor Networks" by Joao Gama (Editor), Mohamed Medhat Gaber (Editor): "Predictive Learning in Sensor Networks" and "TinyOS Education with LEGO MINDSTORMS NXT"
• R. Pedersen. Worst Case Execution Time Analysis of Sparse Kernel Learners, International Workshop on Knowledge Discovery from Data Streams, ECML 2006.
• M. Schoberl and R. Pedersen, WCET Analysis for a Java Processor, JTRES 2006.
• R. Pedersen and M. Schoeberl, Exact Roots for a RealTime Garbage Collector, JTRES 2006.
• R. Pedersen and M. Schoeberl, An Embedded Support Vector Machine, In Proceedings of the Forth Workshop on Intelligent Solutions in Embedded Systems, WISES 2006, Vienna, Austria, June 2006.
• R. Pedersen, Energy Measurements for a Support Vector Machine Classifier on Micro Java and TinyOS Nodes, 8th International Workshop on High Performance and Distributed Mining, HPDM, SDM 05, (Keynote by F. Zhao).
• R. Pedersen, Using Support Vector Machines for Distributed Machine Learning, Ph.D. thesis, Distributed systems group, Dept. of Computer Science, University of Copenhagen, Committee: P. Bonnet, J. Ghosh, and P. Koch, 2005.
• R. Pedersen, Using Support Vector Machines in Distributed and Constrained Machine Learning Contexts, Data Mining in Resource Constrained Environments Workshop at Fourth SIAM International Conference on Data Mining,SDM 2004.
• R. Pedersen, Distributed Support Vector Machine, In Proceedings of International Conference on Intelligent Agents, Web Technology and Internet Commerce, IAWTIC 2003.

Research Projects

• KDubiq: Data Types workgroup 4, co-chair

Committees

• EU5: Ubiquitous Knowledge Discovery for Ambient Inteligence (KDubiq) at European Conference on Ambient Intelligence
• Board member of Eclipse group Denmark, 2007
• Second International Workshop on High Availability of Distributed Systems, HADIS 2006, at DEXA 2006, PC member.
• First International Workshop on High Availability of Distributed Systems, HADIS 2005, at DEXA 2005, local chair.
• First International Workshop on Data Mining in Sensor Networks as part of the SIAM International Conference on Data Mining, 2005, workshop chair, S. Madden (TinyDB) and H. Kargupta (Distributed Data Mining) as keynote speakers.
• International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IAWTIC 2003, module chair.

Current interests

• Micro Information Systems
• Embedded Machine Learning
• TinyOS on LEGO MINDSTORMS NXT: http://nxtmote.sourceforge.net

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Michèle Sébag is a senior scientist of Computer Science at the Université Paris-Sud (LRI) and Head of the Inference and Learning Group. She is on the Editorial Boards of Machine Learning Journal and Genetic Programming and Evolvable Hardware, Associate Editor for Knowledge and Information System. She was co-chair of ILP 2001 and vice-chair of IEEE ICDM 2003.

Curriculum Vitae

Books

• Inductive Logic Programming, ILP 2001, Springer Verlag (co-edited with Celine Rouveirol)

Research Projects

• Member of the PASCAL, Pattern Analysis, Statistical Modelling and Computational Learning Network of Excellence.
• Manager of the Pascal CHALLENGE Programme
• Member of the Steering Committee, Manager of the Pascal CHALLENGE Programme

Committees

• Editorial Board, Machine Learning Journal
• Editorial Board, Genetic Programming and Evolvable Hardware
• Editorial Board, Knowledge and Information Systems
• President of the French Association of AI
• KDD 2007 13th Int. Conf. on Knowledge Discovery and Data Mining
• ICML 2007, 24th Annual Int. Conf. on Machine Learning
• ILP 2007, 17th Int. Conf. on Inductive Logic Programming
• GECCO 2007, Genetic and Evolutionary Computation Conference
• IJCAI 2007, 20th. Int. Conf. on Artificial Intelligence, SPC

Current interests

• Machine Learning for Numerical Engineering and Autonomic Computing. The use of ML techniques to handle the log files in the EGEE grid system will be described in the courses.
• The Exploration versus Exploitation (EvE) Dilemma is involved in many different settings, ranging from Evolutionary Computation to Reinforcement Learning and Game Theory. I am particularly interested in EvE for dynamic environments and when the number of options is large with respect to the time horizon.
• Competence Maps of Algorithms. The approach inspired from the Phase Transition paradigm, pioneered by Attilio Giordana and Lorenza Saitta, has been extended to various algorithms including C4.5 and SVMs. Using appropriate order parameters, the average behaviour of algorithms is investigated and leads to the discovery of failure regions - and eventually, to new heuristics addressing the failures which have been discovered.

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