
Altes Hauptgebäude, Room 074, Hochschulstrasse 1, 64289 Darmstadt, Germany


Meetings by appointment, general consultation: thursdays, 13:30-14:30 o'clock
In case of important appointments and requests, please make sure to contact my administrative assistant Ira Tesar.
Mission. My team and I would like to make computers learn so much about the world, so rapidly and flexibly, as humans. This poses many deep and fascinating scientific problems: How can computers learn with less help from us and data? How can computers reason about and learn with complex data such as graphs and uncertain databases? How can pre-existing knowledge be exploited? How can computers decide autonomously which representation is best for the data at hand? Can learned results be physically plausible or be made understandable by us? How can computers learn together with us in the loop? To this end, my team and I develop novel machine learning (ML) and artificial intelligence (AI) methods to deal with uncertainty, capture causality, generate behaviour and to combine learning and reasoning.
My team and I also aim at creating value in the economy, society and culture. To this end, I am an investor of Aleph Alpha, cofounded the KI-Klub, connecting AI experts, media, public, and politicians, and published Wie Maschinen Lernen, which is one of the first German general introductory books on AI and, in particular, machine learning, to educate the promise and potential of AI to the broader society. Our research generated (social) media appearances at New York Times, ARTE, Frontiers Science Blog, Science, FAZ Digitec Podcast, KfW Podcast „Zukunft:digital“, Tagesspiegel, Heise, Frankfurter Rundschau, Spektum der Wissenschaften, Handelsblatt, among others, and an exhibition at the Nibelungen Museum in Worms, Germany. To express my point of view on AI, I am writing a monthly column in the German Sunday newspaper Welt am Sonntag.
Bio. Kristian Kersting is a Full Professor (W3) at the Computer Science Department of the TU Darmstadt University, Germany. He is the head of the Artificial Intelligence and Machine Learning (AIML) lab, a member of the Centre for Cognitive Science, a faculty of the ELLIS Unit Darmstadt, and the founding co-director of the Hessian Center for Artificial Intelligence (hessian.ai). After receiving his Ph.D. from the University of Freiburg in 2006, he was with the MIT, Fraunhofer IAIS, the University of Bonn, and the TU Dortmund University. His main research interests are statistical relational artificial intelligence (AI) as well as deep (probabilistic) programming and learning. Kristian has published over 180 peer-reviewed technical papers, co-authored a Morgan&Claypool book on Statistical Relational AI and co-edited a MIT Press book on Probabilistic Lifted Inference.
Kristian is a Fellow of the European Association for Artificial Intelligence (EurAI),
a Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), and a key supporter of the Confederation of Laboratories for Artificial Intelligence in Europe (CLAIRE).
He received the Inaugural German AI Award (Deutscher KI-Preis) 2019, accompanied by a prize of EURO100.000, several best paper and outstanding reviewer awards,
a Fraunhofer Attract research grant with a budget of 2.5 Million Euro over 5 years (2008-2013), and the
EurAI (formerly ECCAI)
Kristian co-chaired the scientific program committee (PC) of ECML PKDD 2013,
UAI 2017 as well as
ECML PKDD 2020 and
was the General Co-Chair of UAI 2018. He regularly serves on
the PC (often at senior level) of several other flagship AI and ML conferences, co-chaired several international workshops,
cofounded the international workshop series on Statistical Relational AI (StarAI) and gave
several tutorials at flagship AI and ML conferences,
He was the founding Editor-in-Chief of Frontiers in Machine Learning and AI and is (past) action editor of
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Journal of Artificial Intelligence Research (JAIR), Artificial Intelligence Journal (AIJ),
Data Mining and Knowledge Discovery (DAMI), and Machine Learning Journal (MLJ) as well as on the editorial
board of KI, the German AI Journal.
Education and Positions.
2019 - now: | Full Professor for Artificial Intelligence and Machine Learning at the CS Department of the TU Darmstadt, Germany |
2017 - 2019: | Professor for Machine Learning at the CS Department of the TU Darmstadt, Germany |
2013 - 2017: | Associate Professor for Data Mining at the CS Department of the TU Dortmund, Germany |
2012 - 2013: | Juniorprofessor for Spatio-Temporal Pattern in Agriculture at the Faculty of Agriculture of the University of Bonn, Germany |
2008 - 2012: | Research group leader at the Fraunhofer IAIS, Germany, supported by a "Fraunhofer Attract" grant of 2.5 Million Euros |
2007: | PostDoctoral Associate at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), USA, working with Leslie Kaelbling (supervisor), Josh Tenenbaum, and Nicholas Roy. |
2000 - 2006: | Ph.D. student at the CS Department of the University of Freiburg, Germany, working with Luc De Raedt (supervsior) and Wolfram Burgard. |
1996 - 2000: | Diploma in Computer Science at the CS Department of the University of Freiburg, Germany |
Research Leadership.
2021 - now: | Co-Spokesperson of the HMWK cluster project "The Third Wave of AI" (3AI), exploring Systems AI. |
2020 - now: | Founding Co-Director of the Hessian Center for Artificial Intelligence, hessian.AI |
2020 - now: | Co-Director of the ELLIS Fellowship Program Semantic, Symbolic and Interpretable Machine Learning |
2020 - now: | Co-Spokesperson of the LOEWE Focus Area WhiteBox, joining the twin disciplines AI and cognitive science to open blackbox models. |
Awards.




















Books and Edited Volumes








Publications and Essays
Selected Scientific Activities
Conference Organization
Workshop and Symposium Organization

Selected Program Committees/Reviewing
NeurIPS 2022 (AC), LoG 2022 (AC), STRL 2022, GroundedML 2022, FATIL 2022, KR 2022, IJCAI-ECAI 2022 (AC), AAAI 2022 (AC), ICLR 2022 (AC), NeurIPS 2021 (AC), TPM 2021, ProbProg 2021, AIES 2021, ICML 2021 (AC), UAI 2021 (SPC), ICLR 2021 (AC), IJCAI 2021 (Senior AC), NeSys 2020, KR 2020, AKBC 2020, FATIL 2020, ICML 2020 (AC), AIES 2020, SDM 2020 (SPC), IJCAI-PRICAI 2020 (AC), IJCAI-PRICAI 2020 Special Track on AI in FinTech (SPC), IJCAI-PRICAI 2020 Special track on AI for CompSust and Human well-being, IJCAI-PRICAI 2020 Demo Track, ICLR 2020, UAI 2020 (SPC), AAAI 2020 (SPC), ECAI 2020 (AC), StarAI 2020, PADL 2020, KR2ML 2019, NeurIPS 2019 (AC), ICDM 2019, IJCAI 2019 (SPC), GCPR 2019 (Track on pattern recognition in the life and natural sciences), DSAA 2019, KDD 2019 (SPC, Member of Best Paper Award Committee for the Applied Data Science Track), AKBC 2019, AAAI 2019 (SPC, Senior Member Track, Demo Track), ICML 2019, ICLR 2019, KI 2019, NeSys 2019 NIPS 2018, ICDM 2018, BNAIC 2018, ICLR 2018, NAMPI 2018, AIKE 2018, AIMSA 2018, ILP 2018, TPM 2018, DSAA 2018, CP 2018, MLG 2018, KI 2018, NAACL-HLT 2018, WWW 2018, CVPR 2018, IJCAI-ECAI 2018 (AC), KDD 2018 (SPC), ICRL 2018, SIGMOD 2018, AAAI 2018 (SPC, Senior Member Track), ILP 2018, ECMLPKDD 2017 (Nectar, PhD), ICDM 2017, CEx 2017, ENIC 2017, GenPlan 2017, KDML 2017, NLP/Journalism 2017, ISWC 2017, SIGMOD 2017, MLG 2017, SUM 2017, IJCAI 2017 (SPC), AAAI 2017 (SPC), MLSA 2017, KI 2017, ACML 2016 (SPC), ICDM 2016, UAI 2016, ECCV 2016, ECML PKDD 2016 (AC), ECAI 2016, IJCAI 2016, ICML 2016, KDD 2016 (AC), AAAI 2016 (SPC), DS 2016, KI 2016, MOD 2016, ICDM 2015, NIPS 2015, ECML PKDD 2015 (GEB, AC), IJCAI 2015 (SPC), MPD 2015, SUM 2015, CVPR 2015, ICML 2015, CoDS 2015, AAAI 2015 (Main, AIW), AAAI 2014 (SPC, SM, SA) , ECML PKDD 2014 (GEB, AC), ICDM 2014 (AC), ECAI 2014 (AC), PODS 2014, KDD 2014 (PC and Best Paper Award Committee), UAI 2014, NIPS 2014, SDM 2014, ACML 2014 (AC and Best Paper Award Committee), CIKM 2014 (KM Track), ESWC 2014, ILP 2014, KR 2014, PGM 2014, DS 2014, CoDS 2014, DATA 2014, LTPM 2014, Know@LOD 2014, MUSE 2014, SenseML 2014, ICML 2010 (AC and Best Paper Award Committee)
Editorial Boards
Advisory Boards and Expert Groups
Invited Talks and Panels
Keynotes at Scientific Conferences
"The Third Wave of AI: Closing the Gap between AI and the Domain Experts, using the example of Deep Plant Phenotyping",
HAICU/DKRZ Workshop on Machine Learning in Earth system science, Hamburg, spring 2020.
"Hybrid AI", ACM India Joint International Conference on
Data Science & Management of Data (CODS-COMAD 2020, 7th ACM IKDD CoDS and 25th COMAD), Hyderabad, India, spring 2020.
"Relational Quadratic Programming": 14th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming (CPAIOR 2017), Padova, Italy, summer 2017.
"Declarative Data Science Programming": 25th Belgium-Dutch Conference on Machine Learning (BeneLearn 2016), Kortrijk, Belgium, fall 2016.
"Collective Attention on the Web": 19th International Conference on Discovery Science (DS 2016), Bari, Italy, spring 2016.
"Democratization of Optimization": 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015), Ferara, Italy, fall 2015
Invited Talks at Scientific Conferences and Meetings
"Deep machines that know when they do not know", Waterloo.ai Seminar, University of Waterloo, Canada, winter 2019.
"Overcoming the Reproducibility Crisis in Sciences using AI?", 50 Years Anniversary Conference " 'The Theoretical University'in the Data Age", University of Bielefeld, Germany, winter 2019.
"The Third Wave of AI", DFG Committee on Scientific Instrumentation and Information Technology, Berlin, Germany, winter, 2019.
"Deep Machines That Know When They Do Not Know", Computer Science Colloquium, University of Hamburg, spring 2019.
"Deep Machines That Know When They Do Not Know", ZIH Colloquium, TU Dresden, summer 2019.
"Deep Machines That Know When They Do Not Know", Human-Like Computing Third Wave of AI Workshop (3AI-HLC 2019), Imperial College, London, Uk, spring 2019.
"Towards Reproducibility in Machine Learning and AI", DFG conference on "Traceability and securing of results as essential
challenges of research in the digital age", Berlin, spring 2019.
"Deep machines that know when they do not know and how to exploit symmetries for modelling and solving quadratic programs", 3rd ETAPS Workshop on Learning in Verification (LiVe), Prague, spring 2019.
"What is Artificial Intelligence?", Leibniz Convent on Artificial Intelligence, Berlin, spring 2019.
"Deep Machines That Know When They Do Not Know", DINFO, University of Florence, spring 2019.
"What is Artificial Intelligence?", EFL Joint Spring Conference 2019 on AI in the Finanical Services Industry, Frankfurt, spring 2019.
"The Automatic Data Scientist", Probabilistic Machine Learning Group, Aalto University, Helsinki, Finland, winter 2018.
"Systems AI: The computational and mathematical modeling of complex AI systems", Symposium about the beginnings, the present and the future of AI-research on the occasion of Wolfgang Bibel's 80th birthday, Darmstadt, winter 2018.
"Probabilistic Programming is great", 1st Conference on Probabilistic Programming (ProbProg), MIT, Boston, USA, fall 2018
"The Automatic Data Scientist: Making Data Science Easier using High-level Languages, Fractional Automorphisms, and Arithmetic Circuits ", Highlights of Logic, Games and Automata, Session on Logic and Learning, Berlin, fall 2018.
"Feeding the World with Big Data: Machines Uncover Spectral Characteristics and Dynamics of Stressed Plants": Plants and Animals: Bridging the Gap in Breeding Research 2018.
"Tractable Data Journalism using deep learning": "Plotting Poetry II: Bringing Deep Learning to Computational Poetry Analysis", 2018.
"Optimization for Advancing AI": Birds of a Feather "Artificial Intelligence and Performance Analysis/Optimization" at ISC High Performance 2018.
"Systems AI: Computational modeling of complex AI systems that learn and think": IJCAI-ECAI Workshop on "Learning and Reasoning" (L&R 2018)
"Relational Quadratic Programming": 14th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming (CPAIOR 2017)
"Declarative Data Science Programming": 25th Annual Machine Learning Conference of Belgium and The Netherlands (BeneLearn 2016)
"Collective Attention on the Web": 19th International Conference on Discovery Science (DS 2016)
"Declarative Programming for Statistical ML": The 2016 Machine Learning Confrence (MLconf 2016 Seattle)
"Democratization of Optimization": 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015)
"Collective Attention on the Web":
Winter Conference on Network Science (NetSci-X 2016)
"Lifted Probabilistic Inference": Frontiers in AI Track of the 20th European Conference on Artificial Intelligence (ECAI 2012)
"Increasing Representational Power and Scaling Inference in Reinforcement Learning": 9th European Workshop on Reinforcement Learning (EWRL 2011)
"Probabilistic Logic Learning and Reasoning": 14th Annual Machine Learning Conference of Belgium and the Netherlands (BeneLearn 2005)
"Thinking Machine Learning": NIPS 2016 Workshop on Neurorobotics: A Chance for New Ideas, Algorithms and Approaches
"Declarative Data Science Programming": Software Engineering and Machine Learning Workshop at the 10th Heinz Nixdorf Symposium 2016
"Lifted Machine Learning": International School on Human-Centred Computing (HCC 2016)
"Collective Attention on the Web": International School and Conference on Network Science (NetSci-X 2016)
"Democratization of Optimization" AAAI 2016 Workshop on Declarative Learning Based Programming (DeLBP 2016)
"Democratization of Optimization": 5th International Workshop on Statistical Relational AI (StarAI 2015)
"Democratization of Optimization": IJCAI 2015 Invited Sister Conference Presentations ML Track
"Poisson Dependency Networks": 2nd International Workshop on Mining Urban Data (MUD 2015)
"High Throughout Phenotyping: A Big Data Mining Challenge": 3rd Brazilian-German Frontiers of Science and Technology Symposium (BRAGFOST 2012)
"High Throughout Phenotyping: A Big Data Mining Challenge": Lernen, Wissen, Adaptivität (LWA 2012)
"From Lifted Probabilistic Inference to Lifted Linear Programming": 7th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2011)
"Statistical Relational Artificial Intelligence": 5th Sino-German Frontiers of Science Symposium (SINOGFOS 2012)
"From Lifted Probabilistic Inference to Lifted Linear Programming": 7th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2012)
"Perception and Prediction Beyond the Here and Now": 2nd International Workshop on Mining Ubiquitous and Social Environments (MUSE 2011)
"Lifted Message Passing": 6th International Workshop onNeural-Symbolic Learning and Reasoning (NeSys 2010)
"Lifted Message Passing": International Workshop on Graphical Models in Robotics (GraphBot 2010)
"Relations and Probabilities: Friends, not Foes": Lernen, Wissen, Adaptivität (LWA 2009)
"Probabilistic Logic Learning and Reasoning": 14th Annual Machine Learning Conference of Belgium and the Netherlands (BeneLearn 2005)
Other Meetings such as Meetups, Industry and Public Meetings
"Maschinelles und Tiefes Lernen sind der Motor für 'KI made in Germany'": Jahreskonferenz der BMBF Plattform "Lernende Systeme", Berlin, summer 2019
"Towards Reproducibility in Machine Learning and AI": Second Annual Merck Data Science & Analytics Days, Frankfurt, spring 2019
"A Short History of Artificial Intelligence, Machine Learning, and Deep Learning": VDE Verband der Elektrotechnik Elektronik Informationstechnik e.V., Rhein-Main, Jahreshauptversammlung, spring 2019
"A Short History of Artificial Intelligence, Machine Learning, and Deep Learning": VCI Verband der chemischen Industrie e.V., AK Digitalisierung, fall 2018
"A Short History of Artificial Intelligence, Machine Learning, and Deep Learning": VDE Verband der Elektrotechnik Elektronik Informationstechnik e.V., Rhein-Main, Vortragsreihe Informations- und Kommunikationstechnologie, fall 2018
"The Automated Data Scientist": Cologne AI and Machine Learning Meetup, fall 2018
"A Short History of Artificial Intelligence, Machine Learning, and Deep Learning": Stadtsparkasse Darmstadt, fall 2018
"Tractable Data Journalism": Berlin Machine Learning Meetup Group, October 2017
"Daten! Sind sie Leben?" Kneipengespräch der "Lust an Wissenschaft?" 2016 Serie der Mercator Global Young Faculty
Tutorials, Seminars and Training
"No more data gibberish: Design your Automatic Data Scientist and simplify your decision-making processes" Deutscher IT-Leiterkongress 2019, Duesseldorf, fall 2019
"Artificial Intelligence - Facts, Chances, Risks", Research Training Group of the German National Academic Scholarship Foundation 2017-18
"Was ist eigentlich Künstliche Intelligenz?", Children's university lecture, comprehensive school Gänsewinkel Schwerte, Germany, fall 2017
"Blade Runner und Künstliche Intelligenz": Schulkinowochen Hessen, spring 2019
"Tractable Probabilistic Graphical Models", 4th International Summer School on Resource-aware Machine Learning, Dortmund, 2017
"Statistical Relational Artificial Intelligence: Logic, Probability, and Computation", AAAI 2017
"Data-Diven Plant Phenotyping", PHENOMICS Workshop Berlin 2016
"Statistical Relational Artificial Intelligence: Logic, Probability, and Computation", HCC 2016
"60 Years of Artificial Intelligence - Where are we?", Summer Academy of the German National Academic Scholarship Foundation 2015
"Statistical (Relational) Learning and Lifted inference", MLSMA 2014
"Lifted Approximate Inference: Methods and Theory", AAAI 2014
"Combining Logic and Probability: Languages, Algorithms, and Applications", AAAI 2013
"Factorizing Gigantic Matrices", ECML PKDD 2011
"Lifted Inference in Probabilistic Logical Models", IJCAI 2011
"Statistical Relational Learning", MLSS 2010
"First-order Planning", ICAPS 2008
"SRL without Tears: An ILP Perspective on SRL", ILP 2008
"Decision-Theoretic Planning and Learning in Relational Domains", AAAI 2008
"Probabilistic Inductive Logic Learning", ECMLPKDD 2005
"Probabilistic Inductive Logic Learning", IDA 2005
"Probabilistic Logic Learning", ICML 2004






Other Meetings such as Meetups, Industry and Public Meetings
"Maschinelles und Tiefes Lernen sind der Motor für 'KI made in Germany'": Jahreskonferenz der BMBF Plattform "Lernende Systeme", Berlin, summer 2019
"Towards Reproducibility in Machine Learning and AI": Second Annual Merck Data Science & Analytics Days, Frankfurt, spring 2019
"A Short History of Artificial Intelligence, Machine Learning, and Deep Learning": VDE Verband der Elektrotechnik Elektronik Informationstechnik e.V., Rhein-Main, Jahreshauptversammlung, spring 2019
"A Short History of Artificial Intelligence, Machine Learning, and Deep Learning": VCI Verband der chemischen Industrie e.V., AK Digitalisierung, fall 2018
"A Short History of Artificial Intelligence, Machine Learning, and Deep Learning": VDE Verband der Elektrotechnik Elektronik Informationstechnik e.V., Rhein-Main, Vortragsreihe Informations- und Kommunikationstechnologie, fall 2018
"The Automated Data Scientist": Cologne AI and Machine Learning Meetup, fall 2018
"A Short History of Artificial Intelligence, Machine Learning, and Deep Learning": Stadtsparkasse Darmstadt, fall 2018
"Tractable Data Journalism": Berlin Machine Learning Meetup Group, October 2017
"Daten! Sind sie Leben?" Kneipengespräch der "Lust an Wissenschaft?" 2016 Serie der Mercator Global Young Faculty
Tutorials, Seminars and Training
"No more data gibberish: Design your Automatic Data Scientist and simplify your decision-making processes" Deutscher IT-Leiterkongress 2019, Duesseldorf, fall 2019
"Artificial Intelligence - Facts, Chances, Risks", Research Training Group of the German National Academic Scholarship Foundation 2017-18
"Was ist eigentlich Künstliche Intelligenz?", Children's university lecture, comprehensive school Gänsewinkel Schwerte, Germany, fall 2017
"Blade Runner und Künstliche Intelligenz": Schulkinowochen Hessen, spring 2019
"Tractable Probabilistic Graphical Models", 4th International Summer School on Resource-aware Machine Learning, Dortmund, 2017
"Statistical Relational Artificial Intelligence: Logic, Probability, and Computation", AAAI 2017
"Data-Diven Plant Phenotyping", PHENOMICS Workshop Berlin 2016
"Statistical Relational Artificial Intelligence: Logic, Probability, and Computation", HCC 2016
"60 Years of Artificial Intelligence - Where are we?", Summer Academy of the German National Academic Scholarship Foundation 2015
"Statistical (Relational) Learning and Lifted inference", MLSMA 2014
"Lifted Approximate Inference: Methods and Theory", AAAI 2014
"Combining Logic and Probability: Languages, Algorithms, and Applications", AAAI 2013
"Factorizing Gigantic Matrices", ECML PKDD 2011
"Lifted Inference in Probabilistic Logical Models", IJCAI 2011
"Statistical Relational Learning", MLSS 2010
"First-order Planning", ICAPS 2008
"SRL without Tears: An ILP Perspective on SRL", ILP 2008
"Decision-Theoretic Planning and Learning in Relational Domains", AAAI 2008
"Probabilistic Inductive Logic Learning", ECMLPKDD 2005
"Probabilistic Inductive Logic Learning", IDA 2005
"Probabilistic Logic Learning", ICML 2004







Tutorials, Seminars and Training



