Jilles Vreeken

Faculty
CISPA Helmholtz Center for Information Security

ELLIS Faculty
Saarbrücken Unit on AI and ML

Professor of Computer Science
Saarland University

Stuhlsatzenhaus 5, Room 1.08

66123 Saarbrücken, Germany

vreeken@cispa.de

+49 681 87083 2173

Jilles at work in Hiroshima

I am faculty (W3, tenured) at the CISPA Helmholtz Center for Information Security, where I lead the research group on Exploratory Data Analysis. I'm Honorary Professor of Computer Science at Saarland University and ELLIS Faculty of the Saarbrücken Unit on Artificial Intelligence and Machine Learning.

My research is concerned with causality and unsupervised learning. In particular, I enjoy developing theory and algorithms for answering fundamentally exploratory questions, such as 'what is going on in my data?', 'what causes what and how?', 'what can we learn from this model?' without having to make unnecessary or unjustified assumptions. To identify what is worth knowing, I like to take a principled approach, such as based on information theory, and then proceed to develop efficient algorithms for extracting useful interpretable results. I like all data types equally much.

I am interested in causal inference under realistic conditions, such as e.g. under hidden confounding or selection bias, when the i.i.d. assumption does not hold, or while making use of background knowledge. I am always interested in how to summarize the essence of complex data and models in easily understandable and actionable terms, and using these to obtain better, more robust, and more useful models. I find the combination of these two threads especially exciting.

If you are 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
- 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 CECAM Workshop AI for Materials Science, June 2021, online.
- Organiser of the Max Planck BigMax Summer School 2019 on 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), Halifax.
- 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 at the BigMax Summer School 2023 on Artificial Intelligence for Materials Science, Platja d'Aro, Spain.
- 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 at the SciCAR conference Science Meets Computer Assisted-Reporting, Nov 2-3 2020, Dortmund.
- Keynote at the DSN workshop on Data-Centric Dependability and Security, Jun 29 2020, Valencia.
- Keynote at the EuADS Summer School on Explainable Data Science, Sep 10-13 2019, Luxemburg.
- Keynote at the NOMAD Summer School on Materials Discovery, Sep 24-27 2018, Laussane, Switzerland.
- Keynote at the ETAPS workshop Causation, Responsibility, and Explanation, April 20 2018, Thessaloniki, Greece.
- Keynote at the DPG symposium Exploiting the Raw Material of the 21st Century, Mar 15 2018, Berlin, Germany.
- Keynote at the CECAM Big-Data Driven Materials Science workshop, Sep 11-13 2017, Laussane, Switzerland.
- Keynote at the International Conference on Formal Concept Analysis, June 14-16 2017, Rennes, France.
- Keynote at the IRISA PEPS Prefute Symposium, October 26 2016, Rennes, France.
- Keynote at the ECML PKDD 2016 PhD Forum, September 19 2016, Riva del Garda, Italy.
- Keynote at the LORIA Mathematics for Decision and Discovery symposium, May 11 2016, Nancy, France.
- Keynote at the SFB 876 Graduate School Lecture Series, April 14 2016, Dortmund, Germany.
- Keynote at the Estonian Summer School on Computer and System Science (ESSCaSS'15).
- Keynote at the SFB 1102 Scientific Retreat, Dagstuhl, Germany, June 28 2015.
- Keynote at the GradUS opening event on June 15 2015, Saarbrücken.
- Keynote at the SFB 1102 Workshop on Data Mining for Linguistic Analysis, March 13 2015, Saarbrücken.
- Keynote at the IEEE ICDM 2013 PhD Forum, Dallas, Texas.
- Keynote at the IEEE ICDM 2011 Workshop on Data Mining for Computational Collective Intelligence.
- Keynote 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'15 Busy Beaver Teaching Award for 'Topics in Algorithmic Data Analysis'.
- 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'.
- PI of Neuro-Explicit Models of Language, Vision and Action (RTG, DFG)
- PI of Crushing Antimicrobial Resistance using Explainable AI (HAICU, Helmholtz Association)
- PI of Trusted Federated Data Analytics (Pilot Project, Helmholtz Association) ('20–'23)
- Deputy-PI of BigMax (MaxNet, Max Planck Society) ('16–'23)
- 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-IWS Postdoctoral Fellowship ('09–'10)
Editorial Board Memberships
- Data Mining and Knowledge Discovery (DAMI) since '15.
- Knowledge and Information Systems (KAIS) since '20.
- Guest Editorial Board for the ECML PKDD Journal Track '13–'22.
- 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–'23, area chair '19–'20
- AAAI International Conference on Artificial Intelligence (AAAI), '20–'24, area chair '20,'23
- IEEE International Conference on Data Mining (ICDM) '12–'22, area chair '21–'22
- International Conference on Machine Learning (ICML) '18–'23
- Neural Information Processing Systems (NeurIPS) '17–'23
- European Conference on Machine Learning and Principles and Practice of Knowledge Discovery
  in Databases (ECML PKDD) '08–'23, area chair '14,'18–'23, program chair '16
- SIAM Conference on Data Mining (SDM) '10–'22, area chair '19–'22
- Artificial Intelligence and Statistics (AISTATS), '22–'24
- Uncertainty in Artificial Intelligence (UAI), '21
- IEEE International Conference on Data Science and Advanced Analytics (DSAA), area chair '19
- International World Wide Web Conference (WWW) '16

Teaching and Advising more ▾

Courses
- Elements of Machine Learning (WS'23)
- Topics in Algorithmic Data Analysis (SS'23)
- Don't Panic – or – How to Survive a PhD (SS'23)
- Elements of Machine Learning (WS'22)
- Topics in Algorithmic Data Analysis (SS'22)
- Elements of Machine Learning (WS'21)
- Topics in Algorithmic Data Analysis (SS'21)
- Elements of Machine Learning (WS'20)
- Topics in Algorithmic Data Analysis (SS'20)
- Elements of Statistical Learning (WS'19)
- Don't Panic – or – How to Survive a PhD (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)
- Don't Panic – or – How to Survive a PhD (SS'17)
- Information Theory (WS'16)
- Topics in Algorithmic Data Analysis (SS'16)
- Information Retrieval and Data Mining (WS'15)
- Topics in Algorithmic Data Analysis (SS'15)
- Information Theory (WS'14)
- Topics in Algorithmic Data Analysis (SS'14)
- Artificial Intelligence (SS'13)
- Introduction to Artificial Intelligence (SS'10–'12)
- Advanced Data Mining (SS'10–'13)
- Database Security (WS'11)
- Project Databases (WS'10)
- Introduction to Data Mining (WS'09–'10)
- Internet Programming ('06–'08)
- Databases ('05–'06)

Postdoctoral Researchers
- Dr. Janis Kalofolias
Doctoral Researchers
- Joscha Cueppers
- Sebastian Dalleiger
- David Kaltenpoth
- Sarah Mameche
- Osman Ali Mian
- Nils Walter
- Boris Wiegand
- Sascha Xu
Research Assistants
- Tim Bauerschmidt
- Rafailia-Maria Chatzianastasiou
- Felix Falkenberg
- Ahmed Musa
- Luis Paulus
- Marco Schuster
- Aleena Siji
- Hendrik Suhr
- Anton Voran
- Matthias Wilms
- Ozlem Yalcin

Former Postdoctoral Researchers

Former PhD Students
- Dr. Janis Kalofolias (8 December 2022)
- Dr. Jonas Fischer (28 July 2022)
- Dr. Alexander Marx (29 June 2021)
- Dr. Panagiotis Mandros (4 March 2021)
- Dr. 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
- Marco Schuster (2023)
- Nisha George (2023)
- Ravil Gasanov (2023)
- Jyotsna Singh (2023)
- Paul Krieger (2023)
- Muneeb Aadil (2023)
- Mohammad Yaseen
- Martin Gassner (2022)
- Saif Ali Khan (2022)
- Ekatarina Arkhangelskaya (2022)
- Abraham Ezema (2021)
- Tim Bruxmeier (2021)
- Frauke Hinrichs (2021)
- Sarah Mameche (2021)
- Jana Hess (2021)
- Anna Oláh (2020)
- Edith Heiter (2020)
- Sandra Sukarieh (2020)
- 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 Thesis Students
- Rafailia-Maria Chatzianastasiou (2023)
- Matthias Wilms (2021)
- Daniel Kindler (2021)
- Frauke Hinrichs (2017)
- Magnus Halbe (2016)
- Stefan Bier (2014)

Former Research Assistants
- Kiet Vo
- 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)

2023
	Cueppers, J & Vreeken, J Below the Surface: Summarizing Event Sequences with Generalized Sequential Patterns. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2023. (22.1% acceptance rate)
	Kaltenpoth, D & Vreeken, J Causal Discovery with Hidden Confounders using the Algorithmic Markov Condition. In: Proceedings of the International Conference on Uncertainty in Artificial Intelligence (UAI), AUAI, 2023. (31.2% acceptance rate)
	Kaltenpoth, D & Vreeken, J Nonlinear Causal Discovery with Latent Confounders. In: Proceedings of the International Conference on Machine Learning (ICML), PMLR, 2023. (27.9% acceptance rate)
	Mameche, S, Kaltenpoth, D & Vreeken, J Learning Independent Causal Mechanisms. In: Proceedings of the 2nd ICML Workshop on Spurious Correlations, Invariance and Stability (SCIS), 2023. (extended abstract)
	Mian, O, Kaltenpoth, D, Kamp, M & Vreeken, J Nothing but Regrets — Privacy-Preserving Federated Causal Discovery. In: Proceedings of the 26nd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2023. (29% acceptance rate)
	Wiegand, B, Klakow, D & Vreeken, J Why Are We Waiting? Discovering Interpretable Models for Predicting Sojourn and Waiting Times. In: SIAM International Conference on Data Mining (SDM), SIAM, 2023. (27.4% acceptance rate)
	Kamp, M, Fischer, J & Vreeken, J Federated Learning from Small Datasets. In: Proceedings of the International Conference on Representation Learning (ICLR), OpenReview, 2023. (31.8% acceptance rate)
	Mian, O, Kamp, M & Vreeken, J Information-Theoretic Causal Discovery and Intervention Detection over Multiple Environments. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp 9171-9179, AAAI, 2023. (19.6% acceptance rate)
	Kaltenpoth, D & Vreeken, J Identifying Selection Bias from Observational Data. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), pp 8177-8185, AAAI, 2023. (oral presentation, 10.8% acceptance rate; 19.6% overall)
2022
	Dalleiger, S & Vreeken, J Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent. In: Proceedings of Neural Information Processing Systems (NeurIPS), PMLR, 2022. (25.7% acceptance rate)
	Mameche, S, Kaltenpoth, D & Vreeken, J Discovering Invariant and Changing Mechanisms from Data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 1242-1252, ACM, 2022. (15.0% acceptance rate)
	Dalleiger, S & Vreeken, J Discovering Significant Patterns under Sequential False Discovery Control. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 263-272, ACM, 2022. (15.0% acceptance rate)
	Hedderich, M, Fischer, J, Klakow, D & Vreeken, J Label-Descriptive Patterns and their Application to Characterizing Classification Errors. In: Proceedings of the International Conference on Machine Learning (ICML), PMLR, 2022. (21.9% acceptance rate)
	Xu, S, Mian, O, Marx, A & Vreeken, J Inferring Cause and Effect in the Presence of Heteroscedastic Noise. In: Proceedings of the International Conference on Machine Learning (ICML), PMLR, 2022. (21.9% acceptance rate)
	Wiegand, B, Klakow, D & Vreeken, J Mining Interpretable Data-to-Sequence Generators. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2022. (15.0% acceptance rate)
	Coupette, C, Dalleiger, S & Vreeken, J Differentially Describing Groups of Graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2022. (oral presentation 5.5% acceptance rate; overall 15.0%)
	Kalofolias, J & Vreeken, J Naming the most anomalous cluster in Hilbert Space for structures with attribute information. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2022. (15.0% acceptance rate)
	Cueppers, J, Kalofolias, J & Vreeken, J Omen: Discovering Sequential Patterns with Reliable Prediction Delays. Knowledge and Information Systems vol.64(4), pp 1013-1045, Springer, 2022. (IF 2.822)
2021
	Fischer, J & Vreeken, J Differentiable Pattern Set Mining. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 383-392, ACM, 2021. (15.4% acceptance rate)
	Coupette, C & Vreeken, J Graph Similarity Description: How Are These Graphs Similar?. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 185-195, ACM, 2021. (15.4% acceptance rate)
	Fischer, J, Oláh, A & Vreeken, J What's in the Box? Explaining Neural Networks with Robust Rules. In: Proceedings of the International Conference on Machine Learning (ICML), PMLR, 2021. (21.4% acceptance rate)
	Budhathoki, K, Boley, M & Vreeken, J Discovering Reliable Causal Rules. In: Proceedings of the SIAM International Conference on Data Mining (SDM), SIAM, 2021. (21.2% acceptance rate)
	Wiegand, B, Klakow, D & Vreeken, J Mining Easily Understandable Models from Complex Event Data. In: SIAM International Conference on Data Mining (SDM), SIAM, 2021. (21.2% acceptance rate)
	Kalofolias, J, Welke, P & Vreeken, J SUSAN: The Structural Similarity Random Walk Kernel. In: Proceedings of the SIAM International Conference on Data Mining (SDM), SIAM, 2021. (21.2% acceptance rate)
	Mian, OA, Marx, A & Vreeken, J Discovering Fully Oriented Causal Networks. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), AAAI, 2021. (21.3% acceptance)