Until 31.03.2021, I was a professor for "Data Science in Agriculture" at the Institute of Computer Science in a joint appointment with the Leibniz Institute of Agricultural Engineering and Bioeconomy (ATB).  As of 01.04.2021, I have moved to a professorship for "Data Science" at the University of Hildesheim (new home page currently under construction). We therefore do not offer any courses at the University of Potsdam any more.

My research interests are in machine learning and computational data analysis. In addition to developing novel methods and studying their statistical and algorithmic properties, we also work on different application problems. We currently focus on applications of machine learning to agricultural and bioeconomic production systems. I have also worked in joint projects with partners from industry, for example on targeting marketing, IT-security, and the evaluation of ranking models in information retrieval, and with researchers in psychology (models of human eye movements) and geophysics (seismic hazard analysis).

For an up-to-date list of publications, also see my Google Scholarprofile.

• Hanna Drimalla, Tobias Scheffer, Niels Landwehr, Irina Baskow, Stefan Roepke, Behnoush Behnia, and Isabel Dziobek. Towards the Automatic Detection of Social Biomarkers in Autism Spectrum Disorder: Introducing the Simulated Interaction Task (SIT). Nature Digital Medicine 3 (25), 2020.
   
• Sabrina Hempel, Julian Adolphs, Niels Landwehr, David Janke, and Thomas Amon. How the selection of training data and modeling approach affects the estimation of ammonia emissions from a naturally ventilated dairy barn - classical statistics versus machine learning. To appear in Sustainability, 2020.
   
• Ahmed Abdelwahab and Niels Landwehr. Deep Distributional Sequence Embeddings Based on a Wasserstein Loss. ArXiv:1912.01933, 2019.
   
• Ahmed Adbelwahab and Niels Landwehr. Quantile Layers: Statistical Aggregation in Deep Neural Networks for Eye Movement Biometrics.  Proceedings of the 30th European Conference on Machine Learning (ECML-2019), Würzburg, Germany, 2019.
   
• Norman Abrahamson, Nicolas Kühn, Melanie Walling, and Niels Landwehr. Probabilistic Seismic Hazard Analysis in California Using Nonergodic Ground-Motion Prediction Equations. Bulletin of the Seismological Society of America 109 (4): 1235-1249, 2019.
   
• Hanna Drimalla, Niels Landwehr, Ursula Hess, and Isabel Dziobek. From face to face: the contribution of facial mimicry to cognitive and emotional empathy. Cognition and Emotion 33 (8):1672-1686, 2019.
   
• Susanne Theuerl, Christiane Herrmann, Monika Heiermann, Philipp Grundmann, Niels Landwehr, Ulrich Kreidenweis, and Anette Prochnow. The Future Agricultural Biogas Plant in Germany: A Vision.  Energies 2019, 12(3), 396, 2019.

• Silvia Makowski, Lena Jäger, Ahmed Abdelwahab, Niels Landwehr, and Tobias Scheffer. A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements. Proceedings of the29th European Conference on Machine Learning (ECML-2018), Dublin, Ireland, 2018.
   
• Hanna Drimalla, Niels Landwehr, Irina Baskow, Behnoush Behnia, Stefan Roepke, Isabel Dziobek, and Tobias Scheffer. Detecting Autism by Analyzing a Simulated Social Interaction. Proceedings of the 29th European Conference on Machine Learning (ECML-2018), Dublin, Ireland, 2018.
   
• Sybille Landwehr-Kenzel, Anne Zobel, Henrike Hoffmann, Niels Landwehr, Michael Schmueck-Henneresse, Thomas Schachtner, Andy Roemhild, and Petra Reinke. Ex vivo expanded natural regulatory T-cells from patients with end stage renal disease or kidney transplantation are useful for autologous cell therapy.Kidney International 93(6):1452-1464,  2018.
   
• Matthias Bussas, Christoph Sawade, Nicolas Kühn, Tobias Scheffer, Niels Landwehr. Varying-Coefficient Models for Geospatial Transfer Learning (draft).Machine Learning 106:1419--1440, 2017.
   
• Ahmed Abdelwahab, Reinhold Kliegl, and Niels Landwehr. A Semiparametric Model for Bayesian Reader Identification. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP-2016), Austin, Texas, 2016.

• Niels Landwehr, Nicolas Kühn, Tobias Scheffer, and Norman Abrahamson. A Non-Ergodic Ground-Motion Model for California with Spatially Varying Coefficients. Bulletin of the Seismological Society of America 106(6):2574-2583, 2016.

• Paul Prasse, Christoph Sawade, Niels Landwehr, and Tobias Scheffer. Learning to Identify Concise Regular Expressions that Describe Email Campaigns. Journal of Machine Learning Research 16:3687−3720, 2015.
   
• Niels Landwehr, Sebastian Arzt, Tobias Scheffer, and Reinhold Kliegl. A Model of Individual Differences in Gaze Control During Reading. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP-2014), Doha, Qatar, 2014.
   
• Michael Großhans, Christoph Sawade, Tobias Scheffer, and Niels Landwehr. Joint Prediction of Topics in a URL Hierarchy. Proceedings of the 25th European Conference on Machine Learning (ECML-2014), Nancy, France, 2014.
   
• Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, and Niels Landwehr. Active Evaluation of Ranking Functions based on Graded Relevance (Extended Abstract) . Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI-2013), Invited Track on Best Papers from Sister Conferences, Beijing, China, 2013.
   
• Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, and Niels Landwehr. Active Evaluation of Ranking Functions based on Graded Relevance . Machine Learning 92(1):41-64, 2013.
   
• Christoph Sawade, Niels Landwehr and Tobias Scheffer. Active Comparison of Prediction Models (with online appendix). Proceedings of the 26th Conference on Neural Information Processing Systems (NIPS-2012), Lake Tahoe, NV, USA, 2012. Oral spotlight presentation, top 5% out of 1467 submissions.
   
• Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, and Niels Landwehr. Active Evaluation of Ranking Functions based on Graded Relevance . Proceedings of the 23rd European Conference on Machine Learning (ECML-2012), Bristol, England, 2012. Best Paper Award.
   
• Paul Prasse, Christoph Sawade, Niels Landwehr and Tobias Scheffer. Learning to Identify Regular Expressions that Describe Email Campaigns (with online appendix). Proceedings of the 29th International Conference on Machine Learning (ICML-2012), Edinburgh, Scotland, 2012.
   
• Elisa Cilia, Niels Landwehr, and Andrea Passerini. Relational Feature Mining with Hierarchical Multitask kFOIL.Fundamenta Informaticae 113: 1-26, 2011.
   
• Ingo Thon, Niels Landwehr, and Luc De Raedt. Stochastic Relational Processes: Efficient Inference and Applications. Machine Learning 82 (2): 239-277, 2011.
   
• Christoph Sawade, Niels Landwehr, and Tobias Scheffer. Active Estimation of F-measures (with Online Appendix). Proceedings of the 24th Annual Conference on Neural Information Processing Systems (NIPS-2010), Vancouver, Canada, 2010.
   
• Christoph Sawade, Niels Landwehr, Steffen Bickel, and Tobias Scheffer. Active Risk Estimation. Proceedings of the 27th International Conference on Machine Learning (ICML-2010), Haifa, Israel, 2010.
   
• Niels Landwehr, Andrea Passerini, Luc De Raedt, and Paolo Frasconi. Fast learning of relational kernels. Machine Learning 78(3): 305-342, 2010.
   
• Ingo Thon, Niels Landwehr, and Luc De Raedt. Applications of stochastic relational processes. Workshop on Analysis of Complex Networks at the 21st European Conference on Machine Learning (WS@ECML-2010), Barcelona, Spain, 2010.
   
• Niels Landwehr. Trading Expressivity for Efficiency in Statistical Relational Learning (Ph.D. Thesis Abstract). SIGKDD Explorations 11(2): 59-60, 2009.
   
• Ingo Thon, Bernd Gutmann, Martijn van Otterlo, Niels Landwehr, and Luc De Raedt. From non-deterministic to probabilistic planning with the help of statistical relational learning. Proceedings of the Workshop on Planning and Learning, in conjunction with the 19th International Conference on Automated Planning and Scheduling (WS@ICAPS-2009), Thessaloniki, Greece, 2009.
   
• Elisa Cilia, Niels Landwehr, and Andrea Passerini. Mining Drug Resistance Relational Features with Hierarchical Multitask kFOIL.Proceedings of the Bio-Logical (Logic-based approaches in Bioinformatics) Workshop, Reggio Emilia, Italy, 2009.
   
• Laura Antanas, Ingo Thon, Martijn van Otterlo, Niels Landwehr, and Luc De Raedt. Probabilistic Logical Sequence Learning for Video. Proceedings of the 19th International Conference on Inductive Logic Programming (ILP-2009), Leuven, Belgium, July 2009.
   
• Niels Landwehr. Trading Expressivity for Efficiency in Statistical Relational Learning. Ph.D. Thesis, Katholieke Universiteit Leuven, February 2009.
   
• Luc De Raedt, Bart Demoen, Daan Fierens, Bernd Gutmann, Gerda Janssens, Angelika Kimmig, Niels Landwehr, Theofrastos Mantadelis, Wannes Meert, Ricardo Rocha, Vitor Santos Costa, Ingo Thon, Joost Vennekens. Towards Digesting the Alphabet-Soup of Statistical Relational Learning. In Proceedings of the 1st Workshop on Probabilistic Programming: Universal Languages, Systems and Applications, at the 22nd Annual Conference on Neural Information Processing Systems (WS@NIPS-2008).
   
• Andreas Karwath, Kristian Kersting, and Niels Landwehr. Boosting Relational Sequence Alignments . In Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008) Pisa, Italy, December 2008.
   
• Ingo Thon, Niels Landwehr, and Luc De Raedt. A Simple Model for Sequences of Relational State Descriptions. In Proceedings of the 19th European Conference on Machine Learning (ECML-2008), Antwerp, Belgium, September 2008.
   
• Niels Landwehr. Modeling Interleaved Hidden Processes. In Proceedings of the 25th International Conference on Machine Learning (ICML-2008), Helsinki, Finland, July 2008.
   
• Niels Landwehr, Bernd Gutmann, Ingo Thon, Matthai Philipose, and Luc De Raedt. Relational Transformation-based Tagging for Activity Recognition. In Fundamenta Informaticae 89 (1): 1-19, 2008.
   
• Kristian Kersting, Luc De Raedt, Bernd Gutmann, Andreas Karwath, and Niels Landwehr. Relational Sequence Learning. In Luc De Raedt, Paolo Frasconi, Kristian Kersting, and Stephen Muggleton (Eds.): Probabilistic Inductive Logic Programming. Springer, 2008.
   
• Niels Landwehr and Taneli Mielikäinen. Probabilistic Logic Learning from Haplotype Data. In Luc De Raedt, Paolo Frasconi, Kristian Kersting, and Stephen Muggleton (Eds.): Probabilistic Inductive Logic Programming. Springer, 2008.
   
• Niels Landwehr, Bernd Gutmann, Ingo Thon, Matthai Philipose, and Luc De Raedt. Relational Transformation-based Tagging for Human Activity Recognition. In D. Malerba, A. Appice, and M. Ceci (Eds.): Working Notes of the 6th Workshop on Multi-Relational Data Mining at the 24th European Conference on Machine Learning (WS@ECML-2007), Warsaw, Poland, September 2007.
   
• Matti Kääriänen, Niels Landwehr, Sampsa Lappalainen and Taneli Mielikäinen. Combining Haplotypers. CoRR abs/0710.5116, Technical Report C-2007-57, University of Helsinki, Department of Computer Science, 2007.
   
• Niels Landwehr, Kristian Kersting, and Luc De Raedt. Integrating Naive Bayes and FOIL. In Journal of Machine Learning Research 8 (1) 481-507, 2007.
   
• Niels Landwehr, Taneli Mielikinen, Lauri Eronen, Hannu Toivonen, Heikki Mannila. Constrained Hidden Markov Models for Population-based Haplotyping. In BMC Bioinformatics 8 (Suppl 2): S9. 2007.
   
• Niels Landwehr and Luc De Raedt. r-grams: Relational Grams . In Proceedings of the Twentieth Joint International Conference on Artificial Intelligence (IJCAI-2007), Hyderabad, India, January 2007.
   
• Niels Landwehr, Andrea Passerini, Luc De Raedt, and Paolo Frasconi. kFOIL: Learning Simple Relational Kernels. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-2006), Boston, MA, USA, July 2006.
   
• Niels Landwehr, Taneli Mielikainen, Lauri Eronen, Hannu Toivonen, and Heikki Mannila. Constrained Hidden Markov Models for Population-based Haplotyping (Extended Abstract). In Juho Rouso, Samuel Kaski, and Esko Ukkonen (Eds.): Proceedings of the Workshop on Probabilistic Modeling and Machine Learning in Structural and Systems Biology (PMSB), Helsinki, Finland, June 2006.
   
• Niels Landwehr, Kristian Kersting, and Luc De Raedt. nFOIL: Integrating Naive Bayes and FOIL. In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-2005), Pittsburgh, PA, USA, July 2005.
   
• Niels Landwehr, Mark Hall, and Eibe Frank. Logistic Model Trees . In Machine Learning 59 (1-2) 161-205, 2005.
   
• Niels Landwehr, Mark Hall, and Eibe Frank. Logistic Model Trees. In Proceedings of the 14th European Conference on Machine Learning (ECML-2003), Cavtat (Dubrovnik), Croatia, September 2003.
  Best student paper award.
   
• Niels Landwehr. Logistic Model Trees. Diplomarbeit, Albert-Ludwigs-Universität Freiburg, Institut für Informatik, July 2003.
   
• Kristian Kersting and Niels Landwehr. Scaled Conjugate Gradients for Maximum Likelihood: An Empirical Comparison with the EM Algorithm . In J. A. Gámez, S. Moral, and A. Salmerón (Eds.): Advances in Learning Bayesian Networks. Studies in Fuzziness and Soft Computing 146, 2002.

• Program Co-Chair, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKKD) 2016.
• Workshop Co-Chair, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKKD) 2013.
• Programme Committee Memberships:
  • AAAI, Conference of the Association for Advancement of Articial Intelligence ['10,'12,'18,'20]
  • ACML, Asian Conference on Machine Learning ['14]
  • AISTATS, International Conference on Articial Intelligence and Statistics ['09]
  • ECML, European Conference on Machine Learning ['07,'09,'20]
  • ECAI, European Conference on Articial Intelligence ['12]
  • ICML, International Conference on Machine Learning ['09,'10,'11,'12,'13,'14,'15]
  • IJCAI, International Joint Conference on Articial Intelligence ['09,'11,'19,'20]
  • ILP, International Conference on Inductive Logic Programming ['09,'10]
  • NIPS, Neural Information Processing Systems (reviewer) ['08,'09,'12,'13,'14,'15]
  • StarAI, Statistical Relational AI Workshop ['13,'14]
  • SRL, Workshop on Statistical Relational Learning ['09,'12]
• Editorial Board, Machine Learning Journal (since 2010).

I obtained a M.Sc. in computer science ("Diplom Informatik", scl) from the Albert-Ludwigs-Universität Freiburg, Germany, in July 2003; and a Ph.D. in machine learning ("Doctoraat", scl) from the Katholieke Universiteit Leuven, Belgium, in February 2009. From February 2009 to January 2013 I was a post-doctoral researcher at the University of Potsdam, Germany. In November 2012 I was awarded an Emmy Noether Fellowship of the German Research Foundation (DFG) for leading a junior research group on Machine Learning and Scientific Data Analysis. Since November 2017 I have led a junior research group at the Leibniz Institute of Agricultural Engineering and Bioeconomy (ATB), and since November 2019 I am a professor for Data Science in Agriculture at the Institute for omputer Science in a joint appointment with the ATB.

• ECMLPKDD-2013 Test of Time Award
• ECML-2012 Best Paper Award
• 2009 Scientific Prize IBM Belgium for Informatics ("best Ph.D. thesis in computer science in Belgium")
• 2009 ECCAI Artificial Intelligence Dissertation Award ("best Ph.D. thesis in artificial intelligence in Europe")
• VDI Förderpreis ("outstanding M.Sc. thesis")
• ECML-2003 Best Student Paper Award