Jilles Vreeken

Faculty
Helmholtz Center for Information Security

Senior Researcher
Max Planck Institute for Informatics

Professor
Saarland University

Saarland Informatics Campus
Building E 1.7 Room 3.22

66123 Saarbrücken, Germany

jv@cispa.saarland

+49 681 302 71 925

Jilles at work in Miami

I lead the research group on Exploratory Data Analysis at the Helmholtz Center for Information Security. In addition, I'm affiliated as Senior Researcher with the Database and Information Systems (D5) group of the Max Planck Institute for Informatics, and as Professor with the Department of Computer Science of Saarland University.

My research is mainly concerned with data mining and machine learning. In particular, I enjoy developing theory and algorithms for answering exploratory questions about data, such as `what are the causal dependencies in my data' or `this is my data, tell me what I need to know'. To identify what is worthwhile structure I often employ well-founded statistical methods based on information theory. On the basis of such scores we can then develop efficient algorithms that can extract useful and insightful results from large and complex data.

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 and ideally causal structures in large collections of complex data; how to efficiently mine easily interpretable summaries from data; how to determine and discover causal dependencies from observational 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, explorative 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 our recent workshop on Learning and Mining for Cybersecurity (LEMINCS) at KDD'19, our tutorial on Modern MDL meets Data Mining at KDD'19, or our tutorial on Summarizing Graphs at Multiple Scales at ICDM'18.

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 more ▾

Organisation & Invited Talks
- Member of the Best Paper Award Committee of ECMLPKDD 2020, Ghent, Belgium.
- Panel Chair of SIAM SDM 2019, Calgary, Canada.
- Tutorial Chair of SIAM SDM 2017, Houston, USA.
- Program Co-Chair of ECML PKDD 2016, Riva del Garda, Italy.
- Publicity Co-Chair of ACM IUI 2015, Atlanta, USA.
- Sponsorship Co-Chair of ECML PKDD 2014, Nancy, France.
- Workshop Co-Chair of IEEE ICDM 2012, Brussels, Belgium.
- Organiser of the Max Planck BiGmax Summer School on Big-Data Driven Materials Discovery, Platja d'Aro, Spain.
- Organiser of the ACM SIGKDD 2019 Workshop on Learning and Mining for Cybersecurity (LEMINCS), Anchorage.
- Organiser of the ACM SIGKDD 2017 Workshop on Interactive Data Exploration and Analytics (IDEA), San Francisco.
- Organiser of the ACM SIGKDD 2016 Workshop on Interactive Data Exploration and Analytics (IDEA), San Francisco.
- Organiser of the ACM SIGKDD 2015 Workshop on Interactive Data Exploration and Analytics (IDEA), Sydney, AU.
- Organiser of the ACM SIGKDD 2014 Workshop on Interactive Data Exploration and Analytics (IDEA), NYC, USA.
- Organiser of the International Workshop Data Mining: Beyond the Horizon, November 2014, Bristol, UK.
- 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.
- Lecturer of the ACM SIGKDD 2019 Tutorial on Modern MDL meets Data Mining, Anchorage, Alaska.
- Lecturer of the IEEE ICDM 2018 Tutorial on Summarizing Graphs at Multiple Scales, Singapore.
- Lecturer of the SIAM SDM 2015 Tutorial on Information Theoretic Methods in Data Mining, Vancouver, Canada.
- Lecturer of the ECML PKDD 2014 Tutorial on Information Theoretic Methods in Data Mining, Nancy, France.
- Lecturer of the IEEE ICDM 2011 Tutorial on Mining Sets of Patterns, Vancouver, Canada.
- Lecturer of the ECML PKDD 2010 Tutorial on Mining Sets of Patterns, Barcelona, Spain.
- Keynote Lecturer at the EuADS Summer School on Explainable Data Science, Sep 10-13 2019, Luxemburg.
- Keynote speaker at the NOMAD Summer School on Materials Discovery, Sep 24-27 2018, Laussane, Switzerland.
- Keynote speaker at the 3rd Workshop on Formal Reasoning about Causation, Responsibility, and Explanation in Science and Technology at ETAPS 2018, April 20 2018, Thessaloniki, Greece.
- Keynote speaker at the Symposium on Managing and Exploiting the Raw Material of the 21st Century at the Spring Meeting of the German Physical Society, Mar 11-16 2018, Berlin, Germany.
- Keynote speaker at the CECAM Big-Data Driven Materials Science workshop, Sep 11-13 2017, Laussane, Switzerland.
- Keynote speaker at the International Conference on Formal Concept Analysis, June 14-16 2017, Rennes, France.
- Keynote speaker at the IRISA PEPS Prefute Symposium, October 26 2016, Rennes, France.
- Keynote speaker at the ECML PKDD 2016 PhD Forum, September 19 2016, Riva del Garda, Italy.
- Keynote speaker at the LORIA Mathematics for Decision and Discovery symposium, May 11 2016, Nancy, France.
- Keynote speaker at the SFB 876 Graduate School Lecture Series, April 14 2016, Dortmund, Germany.
- Keynote lecturer at the Estonian Summer School on Computer and System Science (ESSCaSS'15).
- Keynote speaker at the SFB 1102 Scientific Retreat, Dagstuhl, Germany, June 28 2015.
- Keynote speaker at the opening of GradUS, the Saarland University Graduate Centre, June 15 2015, Saarbrücken.
- Keynote speaker at the SFB 1102 Workshop on Data Mining for Linguistic Analysis, March 13 2015, Saarbrücken.
- Keynote speaker at the IEEE ICDM 2013 PhD Forum, Dallas, Texas.
- Keynote speaker at the IEEE ICDM 2011 Workshop on Data Mining for Computational Collective Intelligence.
- Keynote speaker at the ECML PKDD 2008 Workshop From Local Patterns to Global Models, Antwerp, Belgium.
Awards & Grants
- IEEE ICDM'18 Tao Li Award for Excellence in Research
- IEEE ICDM'18 Best Paper Award for 'Discovering Reliable Dependencies from Data'
- UdS-CS Busy Beaver Teaching Award for 'Topics in Algorithmic Data Analysis (TADA)' SS'15.
- ACM SIGKDD'11 Best Student Paper Award for 'Tell Me What I Need to Know'
- ACM SIGKDD'10 Doctoral Dissertation Runner-Up Award for 'Making Pattern Mining Useful'
- ECML PKDD'09 Best Student Paper Award for 'Identifying the Components'
- Deputy-PI for 'Discovering Interpretable Patterns, Correlations, and Causation' within the BigMax project.
- 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)
Editorial Board Memberships
- Data Mining and Knowledge Discovery (DAMI) since '15
- Guest Editorial Board for the ECML PKDD Journal Track '13–'19
- Guest Editor of the Special Issue on `Interactive Data Exploration and Analytics' of
  Transactions on Knowledge Discovery and Data Mining (TKDD)
Journal Reviewing
- Transactions on Knowledge Discovery and Data Mining (TKDD)
- Transactions on Knowledge and Data Engineering (TKDE)
- Journal of Maching Learning Research (JMLR)
- Statistical Analysis and Data Mining (SAM)
- Maching Learning journal (MLj)
- Information Systems (IS)
- Knowledge and Information Systems (KAIS)
- Social Network Analysis and Mining (SNAM)
- Transactions on Intelligent Systems and Technology (TIST)
Program Committees
- ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) '10–'20, area chair '19–'20
- European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
  in Databases (ECML PKDD) '08–'20, program chair '16, area chair '14,'18–'20
- IEEE International Conference on Data Mining (ICDM) '12–'20, area chair '20
- SIAM Conference on Data Mining (SDM) '10–'21, area chair '19–'21
- Neural Information Processing Systems (NeurIPS) '17–'20
- AAAI International Conference on Artificial Intelligence (AAAI), area chair '20
- IEEE International Conference on Data Science and Advanced Analytics (DSAA), area chair '19
- International Conference on Machine Learning (ICML) '18
- International World Wide Web Conference (WWW) '16

Teaching and Advising more ▾

Graduate Courses
- Topics in Algorithmic Data Analysis (SS'20)
- Elements of Statistical Learning (WS'19)
- Topics in Algorithmic Data Analysis (SS'19)
- Elements of Statistical Learning (WS'18)
- Topics in Algorithmic Data Analysis (SS'18)
- Information Retrieval and Data Mining (WS'17)
- Topics in Algorithmic Data Analysis (SS'17)
- Information Theory (WS'16)
- Topics in Algorithmic Data Analysis (SS'16)
- Information Retrieval and Data Mining (WS'15)
- Time Series Analytics (WS'15)
- Topics in Algorithmic Data Analysis (SS'15)
- The Information Theory Seminar (WS'14)
- Topics in Algorithmic Data Analysis (SS'14)
Undergraduate Courses
- Artificial Intelligence (SS'13)
- Introduction to Artificial Intelligence (SS'10–'12)
- Introduction to Data Mining (WS'09–'10)
- Internet Programming ('06–'08)
- Databases ('05–'06)

Researchers and Assistants
- Corinna Coupette
- Joscha Cueppers
- Sebastian Dalleiger
- Jonas Fischer
- Janis Kalofolias
- David Kaltenpoth
- Panagiotis Mandros
- Alexander Marx
- Osman Ali Mian
- Boris Wiegand
- Muneeb Aadil
- Ekaterina Arkhangelskaya
- Abraham Ezema
- Edith Heiter
- Daniel Heller
- Jana Hess
- Frauke Hinrichs
- Anna Olah
- Sandra Sukarieh
- Sascha Xu

Former Postdoctoral Researchers
- Dr. Mario Boley
- Dr. Hoang Vu Nguyen

Former PhD Students
- Kailash Budhathoki (3 July 2020)
- Dr. Roel Bertens (27 May 2017)
- Dr. Koen Smets (16 May 2012)
- Dr. Michael Mampaey (21 Oct 2011)

Former MSc Thesis Students
- Joscha Cueppers (2019)
- Divyam Saran (2019)
- Osman Ali Mian (2019)
- Simina Ana Cotop (2019)
- Magnus Halbe (2018)
- Maha Aburahma (2018)
- Iva Farag-Baykova (2018)
- Yuliia Brendel (2018)
- Maike Eissfeller (2018)
- Boris Wiegand (2018)
- Tatiana Dembelova (2018)
- Robin Burghartz (2017)
- Henrik Jilke (2017)
- Benjamin Hättasch (2017)
- Amirhossein Baradaranshahroudi (2016)
- Apratim Bhattacharyya (2016)
- Beata Wójciak (2016)
- Margarita Salyaeva (2016)
- Manan Gandhi (2016)
- Kathrin Grosse (2016)
- Kailash Budhathoki (2015)
- Panagiotis Mandros (2015)
- Thomas Van Brussel (2012)
- Tanja Van den Eede (2011)
- Sandy Moens (2010)
- Andie Similon (2010)
- Sander Schuckmann (2008)

Former BSc Students
- Frauke Hinrichs (2017)
- Magnus Halbe (2016)
- Stefan Bier (2014)

Former Research Assistants
- Khánh Hiep Tran
- Grégoire Pacreau
- Michael Hedderich
- Patrick Ferber
- Shweta Mahajan
- Tobias Heinen
- Cristian Caloian
- David Ziegler
- Stefan Neumann
- Andrea Fuksova
- Eustace Ebhotemhen
- Shilpa Garg
- Sinan Bozca
- Michael Wessely

Selected Recent Publications (go here for the complete list)

In Press
	Mandros, P, Boley, M & Vreeken, J Discovering Dependencies with Reliable Mutual Information. Knowledge and Information Systems, Springer (IF 2.397)
	Sutton, C, Boley, M, Ghiringhelli, L, Rupp, M, Vreeken, J & Scheffler, M Identifying Domains of Applicability of Machine Learning Models for Materials Science. Nature Communications, Nature Research (IF 11.88)
2020
	Dalleiger, S & Vreeken, J The Relaxed Maximum Entropy Distribution and its Application to Pattern Discovery. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'20), IEEE, 2020 (19.7% acceptance rate).
	Cueppers, J & Vreeken, J Just Wait For It... Mining Sequential Patterns with Reliable Predictions. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'20), IEEE, 2020 (full paper, 9.8% acceptance rate; overall 19.7%).
	Fischer, J & Vreeken, J Discovering Succinct Pattern Sets Expressing Co-Occurrence and Mutual Exclusivity . In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'20), ACM, 2020 (16.8% acceptance rate).
	Mandros, P, Kaltenpoth, D, Boley, M & Vreeken, J Discovering Functional Dependencies from Mixed-Type Data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'20), ACM, 2020 (16.8% acceptance rate).
	Penerath, F, Mandros, P & Vreeken, J Discovering Approximate Functional Dependencies using Smoothed Mutual Information . In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'20), ACM, 2020 (16.8% acceptance rate).
	Zhang, Y, Humbert, M, Surma, B, Manoharan, P, Vreeken, J & Backes, M Towards Plausible Graph Anonymization. In: Proceedings of the Network and Distributed System Security Symposium (NDSS), The Internet Society, 2020 (17.4% acceptance rate).
	Dalleiger, S & Vreeken, J Explainable Data Decompositions. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'20), AAAI, 2020 (oral presentation 4.5% acceptance rate; overall 20.6%).
	Belth, C, Zheng, X, Vreeken, J & Koutra, D What is Normal, What is Strange, and What is Missing in a Knowledge Graph. In: Proceedings of the Web Conference (WWW), ACM, 2020 (oral presentation; overall acceptance rate 19.2%).
2019
	Kalofolias, J, Boley, M & Vreeken, J Discovering Robustly Connected Subgraphs with Simple Descriptions. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), IEEE, 2019 (18.5% acceptance rate).
	Mandros, P, Boley, M & Vreeken, J Discovering Reliable Correlations in Categorical Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'19), IEEE, 2019 (18.5% acceptance rate).
	Fischer, J & Vreeken, J Sets of Robust Rules, and How to Find Them. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Data (ECMLPKDD), Springer, 2019 (17.7% acceptance rate).
	Marx, A & Vreeken, J Identifiability of Cause and Effect using Regularized Regression. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'19), ACM, 2019 (oral presentation 9.2% acceptance rate; overall 14.2%).
	Mandros, P, Boley, M & Vreeken, J Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms (Extended Abstract). In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), IJCAI, 2019. (Invited contribution to the IJCAI Sister Conference Best Paper Track)
	Kaltenpoth, D & Vreeken, J We Are Not Your Real Parents: Telling Causal From Confounded by MDL. In: SIAM International Conference on Data Mining (SDM), SIAM, 2019 (22.9% acceptance rate).
	Marx, A & Vreeken, J Testing Conditional Independence on Discrete Data using Stochastic Complexity. In: Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2019 (31% acceptance rate).
	Marx, A & Vreeken, J Telling Cause from Effect by Local and Global Regression. Knowledge and Information Systems vol.60(3), pp 1277-1305, IEEE, 2019. (IF 2.397)