Jilles Vreeken

Independent Research Group Leader (W2)
Exploratory Data Analysis group
Cluster of Excellence MMCI
Saarland University

Senior Researcher
Databases and Information Systems
Max Planck Institute for Informatics

Campus E 1.7 Room 2.04

66123 Saarbrücken, Germany

jilles@mmci.uni-saarland.de

+49 681 302 71 925

+49 681 302 70 155

Jilles at work in Saarbrücken

Since October 2013, I lead the independent research group on Exploratory Data Analysis at the DFG cluster-of-excellence on Multimodal Computing and Interaction at the University of Saarland. In addition, I'm affiliated as
Senior Researcher with the Database and Information Systems (D5) group of the Max Planck Institute for Informatics.

My research is mainly concerned with exploratory data mining. That is, I develop theory and algorithms for answering the question `this is my data, tell me what I need to know'. To identify what you need to know, i.e., what is the most interesting structure in the data, I often employ well-founded statistical methods. In particular, Information Theory — the principles of Minimum Description Length (MDL) and Maximum Entropy have proven to be highly valuable tools. Next, I develop highly efficient algorithms for extracting these interesting structures, i.e., models, from very large and complex data—as well as investigate how we can use these structures in a wide range of applications, including identifying rare diseases, e-health, bio-informatics, market analysis, product recommendation, etc.

I'm always looking for talented and motivated PhD candidates, postdocs, and HiWi's
with a strong background in data mining, machine learning, statistics, and/or mathematics.

Currently I'm investigating techniques for identifying informative local structures in large collections of complex data; how to efficiently mine good data descriptions directly such data; the theoretical and practical foundations of interactive exploration of very large data, discovering things by serendipity; how to mine large relational databases; how to mine very large graphs, including characterising influence propagation in social networks; as well as to study well-founded approaches for meaningfully comparing between, and validation of, exploratorative results.

Below, you'll find an overview of my activities, as well as a selection of my recent publications. You might further be interested in my publications, implementations, our pattern set mining tutorial (PKDD'10, ICDM'11), or our workshops on Interactive Data Exploration and Analytics (IDEA), and Outlier Detection and Description (ODD) at KDD'13.

or, in case you're looking for a bit of procrastination, consider
Research in Progress — the secret life of research, through the medium of animated GIFs.

Activities

Organisation & Invited Talks
- Sponsorship Co-Chair of ECML PKDD 2014, Nancy, France.
- Workshop Co-Chair of IEEE ICDM 2012, Brussels, Belgium.
- Organiser of the ACM SIGKDD 2013 Workshop on Interactive Data Exploration and Analytics (IDEA), Chicago, USA.
- Organiser of the ACM SIGKDD 2013 Workshop on Outlier Detection and Description (ODD), Chicago, USA.
- Organiser of the ECML PKDD 2012 Workshop on Instant Interactive Data Mining (IID), Bristol, UK.
- Organiser of the ACM SIGKDD 2010 Workshop on Useful Patterns (UP), Washington DC, USA.
- Organiser and speaker of the IEEE ICDM 2011 Tutorial on Mining Sets of Patterns, Vancouver, Canada.
- Organiser and speaker of the ECML PKDD 2010 Tutorial on Mining Sets of Patterns, Barcelona, Spain.
- Invited speaker at the IEEE ICDM 2013 PhD Forum, Dallas, Texas.
- Invited speaker at the IEEE ICDM 2011 Workshop on Data Mining for Computational Collective Intelligence.
- Invited speaker at the ECML PKDD 2008 Workshop From Local Patterns to Global Models, Antwerp, Belgium.
Awards & Grants
- KDD'11 Best Student Paper Award for 'Tell Me What I Need to Know'
- ACM SIGKDD Doctoral Dissertation Award 2010 Runner-Up
- ECML PKDD'09 Best Student Paper Award for 'Identifying the Components'
- Independent Research Group 'Exploratory Data Analysis' at the Cluster of Excellence MMCI at U.Saarland ('13–'18)
- Research Project 'Instant, Interactive & Adaptive Data Mining' of the Research Foundation – Flanders (FWO) ('12–'15)
- Post-Doctoral Fellowship of the Research Foundation – Flanders (FWO) ('10–'13)
- UA-BOF-KP Small Project (2010)
- UA-BOF-IWS Postdoctoral Researcher ('09–'10)
Editorial Board
- Member of the Guest Editorial Board for the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases – Journal Track (ECMLPKDD '13–'14)
Reviewer for
- Data Mining and Knowledge Discovery (DAMI)
- Information Systems (IS)
- Knowledge and Information Systems (KAIS)
- Maching Learning journal (MLj)
- Social Network Analysis and Mining (SNAM)
- Statistical Analysis and Data Mining (SAM)
- Transactions on Knowledge Discovery and Data Mining (TKDD)
- Transactions on Knowledge and Data Engineering (TKDE)
- Transactions on Intelligent Systems and Technology (TIST)
Program Committee Memberships (selection)
- ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '10–'14)
- European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
  in Databases (ECML PKDD '08–'14), area chair '14
- IEEE International Conference on Data Mining (ICDM '12, '14)
- SIAM Conference on Data Mining (SDM '10–'11)

Teaching

Undergraduate Courses
- Artificial Intelligence (6 ECTS) ('12–'13)
- Introduction to Artificial Intelligence (3 ECTS) ('09–'12)
- Introduction to Data Mining (3 ECTS) ('09–'11)
- Internet Programming (7.2 ECTS, assisting) ('06–'08)
- Databases (7.2 ECTS, assisting) ('05–'06)
Graduate Courses
- Advanced Data Mining (6 ECTS) ('09–'13)

Master Thesis (co-)Supervision
- Thomas Van Brussel (2012)
- Tanja Van den Eede (2011)
- Andie Similon (2010)
- Sander Schuckmann (2009)
PhD Thesis (daily) (co-)Supervision
- M. Emin Aksehirli ('11-'13)
- Sandy Moens ('10-'13)
- Thomas Van Brussel ('12-'13)

Selected Recent Publications (go here for the complete list)

In Press
	Miettinen, P & Vreeken, J mdl4bmf: Minimal Description Length for Boolean Matrix Factorization. Transactions on Knowledge Discovery from Data, pp 1-30, ACM (IF 1.68) (In press)
	Webb, G & Vreeken, J Efficient Discovery of the Most Interesting Associations. Transactions on Knowledge Discovery from Data, pp 1-32, ACM (IF 1.68) (In Press)
	Zimek, A & Vreeken, J The Blind Men and the Elephant: About Meeting the Problem of Multiple Truths in Data from Clustering and Pattern Mining Perspectives. Machine Learning, Springer (IF 1.587) (In Press)
2014
	Koutra, D, Kang, U, Vreeken, J & Faloutsos, C VoG: Summarizing and Understanding Large Graphs. In: Proceedings of the SIAM International Conference on Data Mining (SDM'14), SIAM, 2014. (fast track journal invitation, as one of the best of SDM'14; full paper with presentation, 15.4% acceptance rate)
	Prakash, BA, Vreeken, J & Faloutsos, C Efficiently Spotting the Starting Points of an Epidemic in a Large Graph. Knowledge and Information Systems vol.38(1), pp 35-59, Springer, 2014. (IF 2.225)
2013
	Akşehirli, E, Goethals, B, Müller, E & Vreeken, J Cartification: A Neighborhood Preserving Transformation for Mining High Dimensional Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'13), pp 937-942, IEEE, 2013. (19.6% acceptance rate)
	Ramon, J, Miettinen, P & Vreeken, J Detecting Bicliques in GF[q]. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'13), pp 509-524, Springer, 2013.
	Kontonasios, K-N, Vreeken, J & De Bie, T Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'13), pp 256-271, Springer, 2013.
	Nguyen, HV, Müller, E, Vreeken, J, Keller, F & Böhm, K CMI: An Information-Theoretic Contrast Measure for Enhancing Subspace Cluster and Outlier Detection. In: Proceedings of the SIAM International Conference on Data Mining (SDM'13), pp 198-206, SIAM, 2013. (oral presentation, 14.4% acceptance rate; overal 25%)
	Akoglu, L, Vreeken, J, Tong, H, Chau, DH, Tatti, N & Faloutsos, C Mining Connection Pathways for Marked Nodes in Large Graphs. In: Proceedings of the SIAM International Conference on Data Mining (SDM'13), pp 37-45, SIAM, 2013. (oral presentation, 14.4% acceptance rate; overal 25%)
	Mampaey, M & Vreeken, J Summarizing Categorical Data by Clustering Attributes. Data Mining and Knowledge Discovery vol.26(1), pp 130-173, Springer, 2013. (IF 2.877)