Prof. Dr. Kristian Kersting, FEurAI, FELLIS

Computer Science Department and Centre for Cognitive Science, TU Darmstadt
Altes Hauptgebäude, Room 074, Hochschulstrasse 1, 64289 Darmstadt, Germany
+49-6151-16-24411 kersting (at) cs (dot) tu-darmstadt (dot) de

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) AI Dissertation Award 2006 for the best Ph.D. thesis in the field of Artificial Intelligence in Europe.

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.

2022

Best Paper Award ConPro 2022.

2022

Highlighted Area Chair of ICLR 2022.

2021

Fellow of the Forum for Interdisciplinary Research (FiF) of the TU Darmstadt (2021-2022).

2019

Faculty of the European Laboratory for Learning and Intelligent Systems (ELLIS), the key European society for machine learning.

2019

Inaugural German AI Award (Deutscher KI-Preis) 2019, accompanied by a prize of EURO100.000, for key contributions to the advancement of Artificial Intelligence.

2019

Fellow of the European Association for Artificial Intelligence (EurAI) for exceptional contributions to the field of Artificial Intelligence.

2019

Fellow of the European Laborator for Learning and Intelligent Systems (ELLIS), the key European society for machine learning.

2019

Best Paper Award at the ICML 2019 Workshop on Tractable Probabilistic Models (TPM).

2019

Named Top 100 Influential Scholar 2018 for Artificial Intelligence by AMiner. The 2018 winners are the most cited scholars in the AAAI and IJCAI conferences between 2007 and 2017, which are identified as the top venues of Artificial Intelligence.

2017

P⁵ Award of the Alumni Computer Science Dortmund for the high practical relevance of the Project Group 608 "Manipulation - Development and critical examination of a framework for extracting and transferring semantic information between videos".

2017

Distinguished Lecturer Award of the Faculty of Mathematics and Computer Science at the Friedrich-Schiller-University Jena

2016

Outstanding Reviewer Award of the European Conference on Computer Vision (ECCV)

2015

Best Paper Award of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE)

2015

Best Presentation Award of the New Challenges in Neural Computations (NC²) workshop

2013

Outstanding PC Member Award of the AAAI Conference on Artificial Intelligence (AAAI)

2011

Best Poster Award of the ACM SIGSPATIAL Advances in Geographic Information Systems (GIS)

2008

Fraunhofer Attract research grant (2.5 Million Euros over 5 years), the excellence stipend programme of Fraunhofer

2006

European Association for Artificial Intelligence (EurAI, formerly ECCAI) Dissertation Award for the best AI dissertation in Europe

2006

Best Student Paper Award of the European Conference on Machine Learning (ECML)

2000

Wolfgang-Gentner Young Talent Award for an Outstanding Diploma Thesis at the CS Department of the University of Freiburg

Books and Edited Volumes

Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan, David Poole (eds.)(2021): An Introduction to Lifted Probabilistic Inference. Neural Information Processing series, MIT Press, ISBN: 9780262542593. Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field.
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Kristian Kersting, Christoph Lampert, Constantin Rothkopf (Hrsg.)(2019): Wie Maschinen lernen - Künstliche Intelligenz verständlich erklärt. Springer, ISBN: 978-3-658-26762-9. Do you know what artificial intelligence and machine learning are? This popular science book in German explains the basic methods and procedures of machine learning in an easy to understand way and without complicated formulas. Mathematical prior knowledge is not necessary. Lisa, the book's protagonist, uses everyday situations to illustrate these in an entertaining and informative way.
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Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole (2016): Statistical Relational Artificial Intelligence: Logic, Probability, and Computation. Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, ISBN: 9781627058414. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations.
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Christian Bauckhage, Kristian Kersting (2016): Collective Attention on the Web. Foundations and Trends in Web Science 5(1-2):1-136. Understanding the dynamics of collective human attention has been called a key scientific challenge for the information age. Tackling this challenge, this monograph explores the dynamics of collective attention related to Internet phenomena such as Internet memes, viral videos, or social media platforms and Web-based businesses. We discuss mathematical models that provide plausible explanations as to what drives the apparently dominant dynamics of rapid initial growth and prolonged decline.
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Jörg Lässig, Kristian Kersting, Katharina Morik (2016): Computational Sustainability. Studies in Computational Intelligence, Vol. 645, Springer, ISBN:978-3-319-31856-1. This editorial book gives an overview of the state of the art research in Computational Sustainability as well as case studies of different application scenarios. This covers topics such as renewable energy supply, energy storage and e-mobility, efficiency in data centers and networks, sustainable food and water supply, sustainable health, industrial production and quality, etc. The book describes computational methods and possible application scenarios.
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Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude Shavlik(2015): Boosted Statistical Relational Learners: From Benchmarks to Data-Driven Medicine. SpringerBrief in Computer Science, 2015, ISBN: 978-3-319-13643-1. This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. It reviews the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems.
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Luc De Raedt, Paolo Frasconi, Kristian Kersting, Stephen Muggleton (2008): Probabilistic Inductive Logic Programming: Theory and Applications. Lecture Notes in Computer Science, Vol. 4911, Springer, ISBN: 978-3-540-78651-1. This editorial book provides an introduction to statistical relational learning with an emphasis on those methods based on logic programming principles. The question of how to combine probability and logic with learning is getting an increased attention as there is an explosive growth in the amount of heterogeneous data that is being collected in the business and scientific world. The structures encountered can be as simple as sequences and trees or as complex as citation graphs, the World Wide Web, and relational databases
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Kristian Kersting (2006): An Inductive Logic Programming Approach to Statistical Relational Learning. IOS Press, ISBN: 978-1-58603-674-4. This book addresses Probabilistic Inductive Logic Programming. The new area is closely tied to, though strictly subsumes, a new field known as ‘Statistical Relational Learning’ which has in the last few years gained major prominence in the AI community. The book makes several contributions, including the introduction of a series of definitions which circumscribe the new area formed by extending Inductive Logic Programming to the case in which clauses are annotated with probability values. Also, it introduces Bayesian logic programs and investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher kernels.
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Selected Scientific Activities

Conference Organization

Co-Chair of the ECML PKDD 2020 Program Committee, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

Co-Chair of the ICML 2018 Workshop Track, the 35th International Conference on Machine Learning

General Co-Chair of UAI 2018, the 34th International Conference on Uncertainty in AI

Co-Chair of KogWis 2018 Program Committee, the 14th Biannual Conference of the German Society for Cognitive Science

Co-Chair of the UAI 2017 Program Committee, the 33rd International Conference on Uncertainty in AI

Co-Chair of the KDD 2015 Best Paper Award Committee, the 21st ACM SIGKDD Concerence on Knoweldge Discovery and Data Mining

Co-Chair of the ECML PKDD 2013 Program Committee, the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

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Workshop and Symposium Organization

Co-organizer of a Dagstuhl Seminar on "Recent Advancements in Tractable Probabilistic Inference", 2022

Co-organizer of a Dagstuhl Seminar on "SE4ML - Software Engineering for AI-ML-based Systems", 2020

Co-organizer of a Dagstuhl Seminar on "Logic and Learning", 2019

Co-organizer of the ECML PKDD 2019 Workshop on Deep Continuous-Discrete Machine Learning, 2019

Co-organizer of the IJCAI 2019 Workshop on Declarative Learning Based Programming (DeLBP), 2019

Co-organizer of the Symposium about the beginnings, the present and the future of AI-research on the occasion of Prof. Dr. Wolfgang Bibel's 80th birthday.

Co-chair of the IROS 2018 Workshop on "Robots that learn and reason"

Co-chair of the ACAI 2018 Summer School on Statistical Relational AI

Co-Chair of the ICML 2018 workshop on Enabling Reproducibility in Machine Learning MLTrain@RML

Co-chair of the German Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML) 2018

Co-Chair of the AAAI 2018 workshop on Declarative Learning Based Programming (DeLBP)

Co-chair of the NIPS 2018 Highlights Workshop

Co-Chair of the KDD 2018 workshop on Data Science, Journalism, and Media (DSJM)

Co-Chair of the IJCAI 2017 workshop on Declarative Learning Based Programming (DeLBP)

Co-Chair of the KDD 2017 workshop on Data Science + Journalism (DS+J)

Co-Chair of the AAAI 2017 workshop on Symbolic Inference and Optimization (SymInfOpt)

Co-Chair of the IJCAI 2016 workshop "BeyondLabeler - Human is More Than a Labeler"

(Founding) Co-Chair of the international workshops on Statistical Relational AI (StarAI) 2014, 2012 , 2010

Co-Chair of the SIGMOD/PODS 2014 workshop on Big Uncertain Data (BUDA)

Co-Chair of the international workshops on Statistical Relational Learning (SRL) 2012, 2009

Co-chair of the NIPS 2012 workshop on Collective Learning and Inference on Structured Data (CoLISD)

Co-Chair of the AAAI Student Abstract and Poster Program 2012-14

Co-Chair of the 4th European Starting AI Researcher Symposium (STAIRS)

Co-Chair of the international workshops on Mining and Learning with Graphs (MLG) 2011, 2007

Co-chair of the NIPS 2011 workshop on Choice Models and Preference Learning (CMPL)

Co-organizer of the Dagstuhl Seminar 07161 "Probabilistic, Logical and Relational Learning - A Further Synthesis", 2007

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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)

German Science Foundation (DFG), European Commission, European Research Council (ERC), US National Science Foundation (NSF), German-Israeli Foundation for Scientic Research and Development (GIF), Freie und Hansestadt Hamburg - BWFG, Swiss National Science Foundation, The Netherlands Organisation for Scientific Research, Research Foundation - Flanders (FWO), The Ministry of Science and Technology of Israel, Alexander von Humboldt-Stiftung, Carl-Zeiss-Stiftung, The Royal Society of New Zealand, German Academic Exchange Service (DAAD)

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Editorial Boards

Frontiers in Machine Learning and Artificial Intelligence

Editor-in-Chief (2018-2021)

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

Action Editor (2017-2020)

Machine Learning Journal (MLJ)

Action Editor (2011-)

Data Mining and Knowledge Discovery (DAMI)

Action Editor (2011-)

Journal of Artificial Intelligence Research (JAIR)

Action Editor (2011-2017)

Artificial Intelligence Journal (AIJ)

Action Editor (2013-)

KI - Künstliche Intelligence

Editor (2017-)

New Computing Generation (EB, 2011-), Information (EB, 2016-), Big Data and Cognitive Computing (EB, 2016-)

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