Jesse Davis

Professor

Address:
Dept. of Computer Science
KU Leuven
Celestijnenlaan 200A
3001 Heverlee
Belgium

Telephone:
Fax: +32 16 32 79 96
Email: first name + dot + last name at cs dot kuleuven dot be
Office: 1.06

About Me

I joined K.U. Leuven in October 2010. I am a member of the Machine Learning group within the Declarative Languages and Artificial Intelligence Lab (DTAI).

In general, my research interests span machine learning, data mining, big data, artificial intelligence, and sports analytics.

I did a post-doc with Pedro Domingos at the University of Washington. I worked on Markov logic networks, which are publicly available as the Alchemy system. I was involved with the MURI project entitled A Unified Approach to Abductive Inference. I did my Ph.D at the University of Wisconsin-Madison under the supervision of David Page.

Highlights

Our research on soccer analytics was recently profiled by Leander Schaerlaeckens in The Guardian

I was member of the AI working group for the International Olympic Committee that produced their AI Agenda

I am involved with an initiative to develop a Common Data Format (CDF) for data arising from football (soccer) matches

Our work on passing creativity was covered by ESPN.com

Our MIT Sloan Sports Analytics Conference paper Leaving Goals on the Pitch was covered by fivethirtyeight

I am the co-founder of the spinoff Runeasi that launched in December 2020

Our paper on valuing on-the-ball actions in soccer received the Best Paper Award in the ADS track at KDD 2019. This work has discussed numerous times in the news such as by The Economist and ESPN

Education

Ph.D in Computer Sciences from the University of Wisconsin-Madison. (August 2007)

M.S. in Computer Sciences from the University of Wisconsin-Madison. (May 2005)

B.A. in Computer Science from Williams College. (June 2002)

Current Team

Jose Manual Alvarez, post-doctoral researcher (Joint with Wouter Verbeke, Jente Van Belle)

Sieglinde Bogaert (Promoter: Benedicte Vanwanseele)

Lotte Bransen

Lorenzo Cascoili

Thomas Daniels

Enrique Dehaerne

Timo Martens

Murat Kocack

Loren Nuyts

Pieter Robberechts

Wei Sun

Luca Stradiotti

Maaike Van Roy, post-doctoral researcher

Allen Wang

Alumni

Jessa Bekker, PhD in December 2018, Currently a research manager in DTAI

Luca Bindini (visiting PhD student)

Arne De Brabandere, PhD October 2022, Currently at KBC

Tom Decroos, PhD October 2020, Currently at Meta

Laurens Devos, PhD in October 2025, Currently a post-doc with Mathias Verbeke

Thomas Dierckx, PhD in November 2022 (Promoter: Wim Schoutens)

Sarah ElShal, PhD in October 2016 (Primary promoter: Yves Moreau)

Kilian Hendrickx, PhD in Marcvh 2022, Current Position: Wahoo (Co-promoter: Konstantinos Gryllias, Bram Cornelis from Siemens Industry Software)

Marc Mertens (joint with Bart Vanrumste)

Hugo Neto-Rios (visiting masters student), Currently at Orlando City SC

Tim Op De Beeck, PhD June 2019, Currently CTO of runeasi

Lorenzo Perini, PhD March 2024, Currently at Meta

Andrea Pugnana (visiting PhD student)

Irma Ravkic, PhD in October 2016

Jonas Schouterden, PhD in June 2022, Currently at Google Zurich (Promoter: Hendrik Blockeel)

Kurt Schutte, PhD in November 2017, Currently CEO of runeasi (Promoter: Benedicte Vanwanseele, co-promoter: Daniel Berckmans)

Nima Taghipour, PhD in March 2013 (Promoter: Hendrik Blockeel)

Pietro Totis, PhD in March 2023 (Other Promoters: Luc De Raedt and Angelika Kimmig)

Jan Van Haaren, PhD in December 2016, Currently Data Scientist at Club Brugge

Vincent Vercruyssen, PhD in December 2020 followed by a post-doc (Co-Promoter: Wannes Meert). Currently: Co-founder predikt.tech

Dries Van der Plas, PhD in May 2025 (Co-promoter: Wannes Meert, Johan Verbraecken)

Guillermo Vinue (post-doc)

Martin Znidarsic, post-doctoral research from 2012-2013 (Joint with Jan Ramon)

Kaja Zupanc (visiting PhD student)

Software

We maintain a number of sports-related software packages

Many of our papers have open source implementations availabe on the ML group's github account.

I've contributed to several packages for learning the structure of a Markov network: BLM and GSSL

The source code for our TODTLER system for transfer learning is also publicly available. See our AAAI 2015 paper for more details.

Mark Goadrich and I have written a java package for generating ROC and Precision Recall curves and computing AUC It can also calculate the area under the curve. For more information about ROC and PR curves, see our paper at ICML 2006.

Selected Publications

The best source for the papers are lirias, Google Scholar, or my (somewhat) update chronological list. Below is a representative subset of highlights.

Journals

Loren Nuyts, Lorenzo Perini, and Jesse Davis (2025). TSelect: selecting relevant and non-redundant channels for multivariate time series classification. Data Mining and Knowledge Discovery 39 (6), 1-62

Sigrid Olthof, and Jesse Davis (2025). Perspectives on data analytics for gaining a competitive advantage in football: computational approaches to tactics. Science and Medicine in Football, 1-13

Jesse Davis, Lotte Bransen, Laurens Devos, Arne Jaspers, Wannes Meert, Pieter Robberechts, Jan Van Haaren and Maaike Van Roy (2024). Methodology and evaluation in sports analytics: challenges, approaches, and lessons learned. Machine Learning 113 (9), 6977-7010

Maaike Van Roy, Pieter Robberechts, Wen-Chi Yang, Luc De Raedt, and Jesse Davis (2023). A Markov framework for learning and reasoning about strategies in professional soccer. Journal of Artificial Intelligence Research 77, 517-562

Dries Van der Plas, Johan Verbraecken, Marc Willemen, Wannes Meert, and. Jesse Davis (2021). Evaluation of Automated Hypnogram Analysis on Multi-Scored Polysomnographies. Frontiers in Digital Health.

Kilian Hendrickx, Wannes Meert, Yves Mollet, Johan Gyselinck, Bram Cornelis, Konstantinos Gryllias and Jesse Davis (2020). A general anomaly detection framework for fleet-based condition monitoring of machines. Mechanical Systems and Signal Processing

Tim Op De Beéck, Arne Jaspers, Michel S. Brink, Wouter G. P. Frencken, Filip Staes, Jesse Davis, and Werner Helsen (2019). Predicting future perceived wellness in professional soccer: the role of preceding load and wellness. International Journal of Sports Physiology and Performance.

Arne Jaspers, Tim Op De Beéck, Michel S. Brink, Wouter G. P. Frencken, Filip Staes, Jesse Davis, and Werner Helsen (2018). Relationships between the external and internal training load in professional soccer: what can we learn from machine learning? International Journal of Sports Physiology and Performance.

Arne De Brabandere, Tim Op De Beéck, Kurt H. Schötte, Wannes Meert, Benedicte Vanwanseele, and Jesse Davis (2018). Data fusion of body-worn accelerometers and heart rate to predict VO2max during submaximal running. Plos One.

Daniel Lowd and Jesse Davis (2014). Improving Markov Network Structure Learning Using Decision Trees. Journal of Machine Learning Research.

Nima Taghipour, Daan Fierens, Jesse Davis and Hendrik Blockeel (2013). Lifted Variable Elimination: Decoupling the Operators from the Constraint Language. Journal of Artificial Intelligence Research. PDF

Elizabeth S. Burnside, Jesse Davis, Jagpreet Chhatwal, Oguzhan Alagoz, Mary J. Lindstrom, Berta M. Geller, Benjamin Littenberg, Katherine A. Shaffer, Charles E. Kahn, Jr, and C. David Page (2009). Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings. In Radiology.

Conferences

Thomas Daniels, Maarten Bosteels, Pieter Robberechts, Jesse Davis (2025). RegCheck: A Real-Time Approach for Flagging Potentially Malicious Domain Name Registrations. Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Laurens Devos, Timo Martens, Deniz Can Oruc, Wannes Meert, H Blockeel, Jesse Davis (2025). Compressing tree ensembles through Level-wise Optimization and Pruning. Proceedings of the Forty-second International Conference on Machine Learning.

Pawel Teisseyre, Timo Martens, Jessa Bekker, Jesse Davis (2025). Learning from biased positive-unlabeled data via threshold calibration. Proceedings of AISTATS 2025

Loren Nuyts and Jesse Davis (2025). The When and How of Target Variable Transformations. Proceedings of the International Symposium on Intelligent Data Analysis, 113-126

Lorenzo Cascioli, Laurens Devos, Ondrej Kuzelka, and Jesse Davis (2024). Faster repeated evasion attacks in tree ensembles. Advances in Neural Information Processing Systems 37, 24762-24794

Laurens Devos, Lorenzo Cascioli, Jesse Davis (2024). Robustness verification of multi-class tree ensembles. Proceedings of the AAAI Conference on Artificial Intelligence 38 (19)

Chaahat Jain, Lorenzo Cascioli, Laurens Devos, Marcel Vinzent, Marcel Steinmetz, Jesse Davis, Joerg Hoffmann (2024). Safety verification of tree-ensemble policies via predicate abstraction. Proceedings of 27th European Conference on Artificial Intelligence.

Luca Stradiotti, Lorenzo Perini, Jesse Davis (2024). Combining active learning and learning to reject for anomaly detection. Proceedings of the 27th European Conference on Artificial Intelligence.

Luca Stradiotti, Lorenzo Perini, Jesse Davis (2024). Semi-supervised isolation forest for anomaly detection Proceedings of the 2024 SIAM International Conference on Data Mining.

Lorenzo Perini, Jesse Davis (2023). Unsupervised anomaly detection with rejection. Advances in Neural Information Processing Systems 36, 69673-69691

Laurens Devos, Lorenzo Perini, Wannes Meert, Jesse Davis (2023). Detecting evasion attacks in deployed tree ensembles. Proceedings of ECML/PKDD 2023

Pieter Robberechts, Maaike Van Roy, Jesse Davis (2023). un-xPass: Measuring Soccer Player's Creativity. Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and Knowledge Discovery and Data Mining.

Kilian Hendrickx, Wannes Meert, Bram Cornelis, Jesse Davis (2022). Know your limits: Machine learning with rejection for vehicle engineering. Proceedings of the International Conference on Advanced Data Mining and Applications, 273-288. Best Application Paper.

Maaike Van Roy, Pieter Robberechts, Wen-Chi Yang, Luc De Raedt, and Jesse Davis (2021). Leaving Goals on the Pitch: Evaluating Decision Making in Soccer In the Proceedings of the MIT Sloan Conference on Sports Analytics

Laurens Devos, Wannes Meert and Jesse Davis (2021). Verifying Tree Ensembles by Reasoning about Potential Instances. To appear in the Proceedings of the SIAM International Conference on Data Mining (SDM21)

Lorenzo Perini, Vincent Vercruyssen and Jesse Davis (2020). Quantifying the Confidence of Anomaly Detectors in Their Example-Wise Predictions. To appear in the Proceedings of ECML/PKDD 2020

Tom Decroos, Lotte Bransen, Jan Van Haaren and Jesse Davis (2019). Actions Speak Louder than Goals: Valuing Player Actions in Soccer. In the Proceedings of the 25th ACM Conference on Knowledge Discovery and Data Mining (KDD 2019). Publicly available code. Best Paper Award.

Lotte Bransen, Pieter Robberechts, Jan Van Haaren and Jesse Davis (2019). Choke or Shine? Quantifying Soccer Players' Abilities to Perform Under Mental Pressure. In the Proceedings of the MIT Sloan Conference on Sports Analytics

Jessa Bekker, Pieter Robberechts and Jesse Davis (2019). Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data. In the Proceedings of ECML/PKDD 2019

Tom Decroos and Jesse Davis (2019). Player Vectors: Characterizing Soccer Players' Playstyle from Match Event Streams. In the Proceedings of ECML/PKDD 2019

Tom Decroos, Jan Van Haaren and Jesse Davis (2018). Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data. In the Proceedings of the 24th ACM Conference on Knowledge Discovery and Data Mining (KDD 2018)

Tim Op De Beeck, Wannes Meert, Kurt Schutte, Benedicte Vanwanseele and Jesse Davis (2018). Fatigue Prediction in Outdoor Runners Via Machine Learning and Sensor Fusion. In the Proceedings of the 24th ACM Conference on Knowledge Discovery and Data Mining (KDD 2018)

Jessa Bekker and Jesse Davis (2018). Estimating the Class Prior in Positive and Unlabeled Data through Decision Tree Induction. In the Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)

Ondrej Kuzelka, Yuyi Wang, Jesse Davis, and Steven Schockaert (2018). Relational Marginal Problems: Theory and Estimation. In the Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)

Anton Dries, Angelika Kimmig, Jesse Davis, Vaishek Belle and Luc De Raedt (2017). Solving Probability Problems in Natural Language,. In the Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017)

Jan Van Haaren, Jesse Davis, Horesh Ben Shitrit, and Pascal Fua (2016). Analyzing Volleyball Match Data from the 2014 World Championships Using Machine Learning Techniques. In the Proceedings of 22nd ACM Conference on on Knowledge Discovery and Data Mining (KDD 2016)

Jessa Bekker, Jesse Davis, Arthur Choi, Adnan Darwiche, and Guy Van Den Broeck (2015). Tractable Learning for Complex Probability Queries. In the Proceedings of Neural Information Processing Systems (NIPS 2015)

Jan Van Haaren, Vladimir Dzyuba, Siebe Hannosset and Jesse Davis (2015). Automatically Discovering Offensive Patterns in Soccer Match Data. In the Proceedings of the Fourteenth International Symposium on Intelligent Data Analysis.

Tim Op De Beeck, Arjen Hommersom, Jan Van Haaren, Maarten van der Heijden, Jesse Davis, Peter Lucas, Lucy Overbeek and Iris Nagtegaal (2015). Mining Hierarchical Pathology Data using Inductive Logic Programming. In the Proceedings of 15th Conference on Artificial Intelligence in Medicine.

Jan Van Haaren, Andrey Kolobov and Jesse Davis (2015). TODTLER: Two-Order-Deep Transfer Learning. In the Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015).

Nima Taghipour, Jesse Davis, and Hendrik Blockeel (2013). First-order Decomposition Trees. In the Proceedings of Neural Information Processing Systems (NIPS 2013).

Jesse Davis, Bess Berg, David Page, Vitor Santos Costa, Peggy Peissig and Michael Caldwell (2012). Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events. In the Proceedings of the Twenty-Ninth International Conference on Machine Learning (ICML 2012). Available from lirias.

Kendrick Boyd, David Page, Vitor Santos Costa and Jesse Davis (2012). Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation. In the Proceedings of the Twenty-Ninth International Conference on Machine Learning (ICML 2012).

Jan Van Haaren and Jesse Davis (2012). Markov Network Structure Learning: A Randomized Feature Generation Approach. In the Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2012).

Guy Van Den Broeck, Nima Taghipour, Wannes Meert, Jesse Davis and Luc De Raedt (2011). Lifted Probabilistic Inference by First-Order Knowledge Compilation. Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI).

Daniel Lowd and Jesse Davis (2010). Learning Markov Network Structure with Decision Trees. Proceedings of IEEE International Conference on Data Mining (ICDM 2010). Available as PDF.

Stefan Schoenmackers, Jesse Davis, Oren Etzioni, and Daniel S. Weld (2010). Learning First-Order Horn Clauses from Web Text. Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP 2010). Available as PDF

Jesse Davis and Pedro Domingos (2010). Bottom-Up Learning of Markov Network Structure. In the Proceedings of the 27th International Conference on Machine Learning (ICML). Available as PDF.

Jesse Davis and Pedro Domingos (2009). Deep Transfer via Second-Order Markov Logic. In the Proceedings of the 26th International Conference on Machine Learning (ICML). Available as PDF.

Jesse Davis, Vitor Santos Costa, Soumya Ray and David Page (2007). An Integrated Approach to Feature Construction and Model Building for Drug Activity Prediction. In the Proceedings of the 24th International Conference on Machine Learning (ICML). Available as PDF.

Jesse Davis and Mark Goadrich (2006). The Relationship Between Precision-Recall and ROC Curves. In the Proceedings of the 23rd International Conference on Machine Learning (ICML).

Jesse Davis, Elizabeth Burnside, Ines Dutra, David Page, Raghu Ramakrishnan, Vitor Santos Costa and Jude Shavlik (2005). View Learning for Statistical Relational Learning: With an Application to Mammography. In the Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).

Jesse Davis, Elizabeth Burnside, Ines Dutra, David Page, Vitor Santos Costa (2005). An Integrated Approach to Learning Bayesian Networks of Rules. In the Proceedings of the European Conference of Machine Learning (ECML).

Workshops

Michiel Schepers, Pieter Robberechts, Jan Van Haaren and Jesse Davis (2025). What Makes a Dribble Successful? Insights From 3D Pose Tracking Data. In Proceedings of the Machine Learning and Data Mining for Sports Analytics Workshop at ECML/PKDD 2025

Jesse Davis and Pieter Robberechts (2023). Expected Metrics as a Measure of Skill: Reflections on Finishing in Soccer. In Proceedings of the Machine Learning and Data Mining for Sports Analytics Workshop at ECML/PKDD 2023

Simon Merckx, Pieter Robberechts, Yannick Euvrard and Jesse Davis (2021). MeasuringMeasuring the Effectiveness of Pressing in Soccer. In Proceedings of the Machine Learning and Data Mining for Sports Analytics Workshop at ECML/PKDD 2021

Jonas Soenen, Elia Van Wolputte, Lorenzo Perini, Vincent Vercruyssen, Wannes Meert, Jesse Davis, Hendrik Blockeel (2021). The effect of hyperparameter tuning on the comparative evaluation of unsupervised anomaly detection methodsProceedings of the KDD'21 Workshop on Outlier Detection and Description

Pieter Robberechts and Jesse Davis (2018). Forecasting the FIFA World Cup – Combining result- and goal-based team ability parameters. In Proceedings of the Machine Learning and Data Mining for Sports Analytics Workshop at ECML/PKDD 2018

Tom Decroos, Jan Van Haaren, Vladimir Dzyuba, and Jesse Davis (2017). STARSS: A spatio-temporal action rating system for soccer. In Proceedings of the Machine Learning and Data Mining for Sports Analytics Workshop at ECML/PKDD 2017

Vincent Vercruyssen, Luc De Raedt and Jesse Davis (2016). Qualitative spatial reasoning for soccer pass prediction. In Proceedings of the Machine Learning and Data Mining for Sports Analytics Workshop at ECML/PKDD 2016

Pre-prints

Gabriel Anzer, Kilian Arnsmeyer, Pascal Bauer, Joris Bekkers, Ulf Brefeld, Jesse Davis, Nicolas Evans, Matthias Kempe, Samuel J Robertson, Joshua Wyatt Smith, Jan Van Haaren. Common Data Format (CDF): A Standardized Format for Match-Data in Football (Soccer)

Biases in Expected Goals Models Confound Finishing Ability

Teaching
I teach a classes on Programming for Big Data and Data Mining as a part of the Master in Artificial Intelligence program.
I coteach a class called Principles of Machine Learning that is taken by Masters of Computer Science students and Master Ir AI students.
I teach a class called Introduction to Data Mining as part of the Master of Digital Humanties program.
I also coordinate the DTAI seminar. Click here for the schedule.
Seminars
When I was at the Univeristy of Washington, I ran the machine learning reading group.
In the spring of 2008, I ran a seminar on Biomedical Applications of Artificial Intelligence.
Selected Professional Service

Workshop Co-Chair AAAI 2025
PC Co-Chair for ECML/PKDD 2024
Workshop Co-Chair AAAI 2024
Journal Track Co-Chair AAAI 2023
Program Co-Chair for the 14th International Conference on Scalable Uncertainty Management (2020)

Journal Track Co-Chair for the ECML/PKDD 2018 and Principles and Practice of Knowledge Discovery in Databases (2018)
Program Co-Chair for the 24th International Conference on Inductive Logic Programming (2014)
Action Editor for the Machine Learning Journal (2015--present)
Action Editor for the European AI Journal (2021--present)
Member of the Editorial Board for the Machine Learning Journal (2011--2015)
Member of the Editorial Board for Data Mining and Knowledge Discovery (2017-present)
Member of the Editorial Board for Artificial Intelligence Journal (2023-present)
Senior Program Committee for AAAI (2016, 2017, 2018)
Senior Program Committee for IJCAI (2015, 2016, 2017, 2018)
Senior Program Committee for the ACM SIGKDD Conference on Knowledge Discovery and Data Mining: Research Track (2018)
Area Chair for the ACM SIGKDD Conference on Knowledge Discovery and Data Mining: Applied Data Science Track (2015, 2017)
Area Chair for ECML/PKDD (2013, 15)
Program Committee for ICML (2008, 2010-2015, 2016, 2017)
Program Committee for AAAI (2008, 2010-2014)
Program Committee for IJCAI (2009, 2011)
Program Committee for ECML/PKDD (2012, 2014, 2016, 2017)
Program Committee for ACM SIGKDD Conference on Knowledge Discovery and Data Mining: Research Track (2015, 2016)
Program Committee for ACM SIGKDD Conference on Knowledge Discovery and Data Mining: Applied Data Science Track (2016)
Program Committee for the European Conference on Artificial Intelligence (ECAI-12)
Program Committee for ICDM (2008, 2010)
Program Committee for Discovery Science (2009, 2010)
Program Committee for the 19th Brazilian Symposium on Artificial Intelligence (SBIA-08)
Reviewer for the Artificial Intelligence Journal (2010)
Reviewer for the Machine Learning Journal (2007,2008,2009,2010)
Reviewer for Transactions on Pattern Analysis and Machine Intelligence (2009)
Reviewer for the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) (2009)
Reviewer for the SRL Workshop (2009)
Reviewer for the ACM Transactions on Knowledge Discovery from Data (2007,2008)
Reviewer for the Journal of Artificial Intelligence Research (2007,2008)
Reviewer for the Annals of Mathematics and Artificial Intelligence (2008)
Reviewer for the 15th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB-07)
Reviewer for the 20th International Joint Conference on Artificial Intelligence (IJCAI-07)
Reviewer for the ACM Transactions on Computational Logic (2006)