Prof. Dr. Kristian Kersting
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

Meetings by appointment, general consultation: thursdays, 13:30-14:30 o'clock
In case of important requests, please make sure to contact my administrative assistant Ira Tesar.

Mission. My team and I in the Machine Learning group 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, i.e., novel computational methods that contain and combine for example search, logical and probabilistic techniques as well as (deep) (un)supervised and reinforcement learning methods.

Bio. Kristian Kersting is a Professor (W3) for Machine Learning at the Computer Science Department of the TU Darmstadt University, Germany, where he heads the machine learning lab. He is also a Deputy Director of the Centre for Cognitive Science. 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, where he was a member of the DFG CRC 876 "Providing Information by Resource-Constrained Data Analysis" and also a Co-Director of the Dortmund Center for Data-Based Media Analysis (DOCMA). His main research interests are statistical relational artificial intelligence (AI), probabilistic deep learning, machine learning, and data mining, as well as their applications. Kristian has published over 160 peer-reviewed technical papers and co-authored a book on statistical relational AI. He received the European Association for Artificial Intelligence (EurAI, formerly ECCAI) Dissertation Award 2006 for the best AI dissertation in Europe, a Fraunhofer Attract Research Grant with a budget of 2.5 Million Euro over 5 years (2008-2013), two best-paper awards (ECML 2006, AIIDE 2015), one best poster award (GIS 2011), one best presentation award (NC^2 2015), two outstanding PC/reviewer awards (AAAI 2013, ECCV 2016), and a Distinguished Lecturer Award from the University of Jena (2017). Kristian was also an ERCIM Cor Baayen Award 2009 finalist, gave several tutorials at top conferences, co-chaired several international workshops such as BeyondLabeler, BUDA, CMPL, CoLISD, DeLBP, DS+J, MLG, SRL, and SymInfOpt as well as the AAAI Student Abstract track and the Starting AI Research Symposium (STAIRS), and cofounded the international workshop series on Statistical Relational AI (StarAI). He regularly serves on the PC (often at senior level) for several top conference and co-chaired the PC of ECML PKDD 2013 and UAI 2017. He is the Speciality Editor in Chief for Machine Learning and AI of Frontiers in Big Data, and is/was an action editor of TPAMI, JAIR, AIJ, DAMI, and MLJ as well as on the editorial boards of KI, NGC, Information, and Big Data and Cognitive Computing.

2017 - now: Full Professor (W3) for Machine Learning at the CS Department of the TU Darmstadt, Germany
2013 - 2017: Associate Professor (W2) for Data Mining at the CS Department of the TU Dortmund, Germany
2012 - 2013: Assistant Professor (W1) 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, 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

P5 Award 2017 of the Alumni Computer Science Dortmund for the Project Group 608 "Manipulation - Development and critical examination of a framework for extracting and transferring semantic information between videos". It is given to student project groups with a high practical relevance.
Distinguished Lecturer Award of the Faculty of Mathematics and Computer Science at the Friedrich-Schiller-University Jena
Outstanding Reviewer Award of the European Conference on Computer Vision (ECCV)
Best Paper Award of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE)
Best Paper Presentation Award of the New Challenges in Neural Computations (NC2) workshop
Outstanding PC Member Award of the AAAI Conference on Artificial Intelligence (AAAI)
Best Poster Award of the ACM SIGSPATIAL Advances in Geographic Information Systems (GIS)
Fraunhofer Attract research grant (2.5 Million Euros over 5 years), the excellence stipend programme of Fraunhofer
European Association for Artificial Intelligence (EurAI, formerly ECCAI) Dissertation Award for the best AI dissertation in Europe
Best Student Paper Award of the European Conference on Machine Learning (ECML)
Wolfgang-Gentner Young Talent Award for an Outstanding Diploma Thesis at the CS Department of the University of Freiburg


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.
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.
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.
Jude Shavlik, Kristian Kersting, Sriraam Natarajan, Tushar Khot (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.
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
Kristian Kersting (2006): An Inductive Logic Programming Approach to Statistical Relational Learning. IOS Press, ISBN: 978-1-58603-674-4. This books 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.


Publications can be found at loop, DBLP, SemanticScholar, and GOOGLE Scholar Citations.

Scientific Activities

Conference Organization

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

Workshop and Symposium Organization

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-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

Selected Program Committees/Reviewing

AAAI 2019 (SPC, Senior Member Track, Demo Track), ICML 2019, NIPS 2018, ICDM 2018, BNAIC 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)

Editorial Boards

Frontiers in Big Data - Machine Learning and Artificial Intelligence
Specialty Chief Editor (2018-)

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Action Editor (2017-)
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-)

Advisory Boards and Expert Groups

BMBF Expert Platform "Lernende Systeme", 2018-

External Advisory Board, NSF Expeditions in Computing network "CompSustNet", 2018-

Technology Review Board, Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras, India, 2018-

Board of Academic Advisors, Competence Center "Consumer Research NRW", 2018-

Board of Academic Advisors, BMBF Project ABIDA - Assessing Big Data, 2015-

Scientific Advisor of, 2015-2017

Scientific Advisor,, 2016

Scientific Advisor,, 2016-2017

Scientific Advisor, GameAnalytics, 2012-2014

Invited Talks, Panels, and Training

Talks and Panels

"The Automatic Data Scientist: Making Data Science Easier using High-level Languages, Fractional Automorphisms, and Arithmetic Circuits ", Highlights of Logic, Games and Automata 2018, Session on Logic and Learning, Berlin.

"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)

"The Automatic 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
"Made in Germany – Lernende Systeme als Standortvorteil von morgen": BMBF, Expert Stage, CEBIT 2018"

"The Automated Data Scientist": 2nd Jaenaer Data Science Day 2018"
"Tractable Data Journalism": SciCAR - Where Science Meets Computer Assisted Reporting 2017
"Journalist plus Wissenschaftler: Dreamteam für post fact checking": nr.Jahreskonferenz 2017

"Datensammeln: Messies oder der Sieg der Induktion?": Stadtgespräche im Musuem 2017
"Thinking Data Science Machines":
Distinguished Lecturer Series, Jena, 2017

"Modelling Traffic Counts":
GCRI, New York, 2016

"Populisten, Autokraten, Despoten-Wie wehrhaft ist unsere Demokratie?"

"Data Mining":

"Algorithmen-Wer kontrolliert die neuen Machthaber?": nr.Jahreskonferenz 2015

"Tractable Data Journalism": Berlin Machine Learning Meetup Group, October 2017
"Thinking Machine Learning": NIPS 2016 Workshop on Neurorobotics: A Chance for New Ideas, Algorithms and Approaches
"Daten! Sind sie Leben?" Kneipengespräch der "Lust an Wissenschaft?" 2016 Serie der Mercator Global Young Faculty
"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)

Tutorials, Seminars and Training

"Statistical Relational AI", KI 2018, Berlin, Germany

"Deep Neural and Probabilistic Learning", KogWis 2018, Darmstadt, Germany

"Lifted Statistical ML: Computational modelling of complex AI systems that learn and think", ACAI 2018, Ferrara, Italy

"Statistical Relational Artificial Intelligence: Logic, Probability, and Computation", NIPS 2017

"Feeding the World with Big Data", Computational Sustainability Virtual Seminar Series, Cornell University, USA, fall 2017

Probabilistische Graphische Modelle, ACATECH Massiv Open Online Course on "Machine Learning", spring 2017

"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

"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
"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