Prof. Dr. Kristian Kersting

Computer Science Department and Centre for Cognitive Science, TU Darmstadt, Germany

kersting _at_ cs.tu-darmstadt.de, kristian.kersting _at_ cogsci.tu-darmstadt.de

2017 - now: | 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 University, Germany |

2012 - 2013: | Assistant Professor (W1) for Data Mining at the Faculty of Agriculture of the University of Bonn, Germany |

2008 - 2012: | ATTRACT research group leader at the Fraunhofer IAIS, Germany |

2007: | PostDoctoral Associate at MIT CSAIL, USA, working with Leslie Kaelbling, Josh Tenenbaum, and Nicholas Roy. |

2001 - 2006: | Ph.D. student at the CS Department of the University of Freiburg, Germany, working with Luc De Raedt (supervsior) and Wolfram Burgard. |

1996 - 2001: | Diploma in Computer Science at the CS Department of the University of Freiburg, Germany |

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 Paper Presentation Award of the New Challenges in Neural Computations (NC^2) 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)

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)

4th European Starting AI Researcher Symposium (STAIRS) 2012, AAAI Student Abstract and Poster Program 2012-2014

DS+J 2017, DeLBP 2017, SymInfOpt 2017, BeyondLabeler 2016, StarAI 2014, Buda 2014, SRL 2012, StarAI 2012, CoLISD 2012, CMPL 2011, MLG 2011, StarAI 2010, SRL 2009, MLG 2007, Dagstuhl Seminar 07161

SIGMOD 2018, AAAI 2018 (SPC), ECMLPKDD 2017 (Nectar, PhD), ICDM 2017, ENIC 2017, GenPlan 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 Scientic Research and
Development (GIF), Swiss National Science Foundation, The Netherlands Organisation for Scientific
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)

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

"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)

"Democratization of Optimization": 14th Conference of the Italian Association for Artificial Intelligence (AI*IA 2015)

"Lifted Probabilistic Inference": Frontiers in AI Track of the 20th European Conference on Artificial Intelligence (ECAI 2012)

"Declarative Programming for Statistical ML": The 2016 Machine Learning Confrence (MLconf 2016 Seattle)

"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)

"From Lifted Probabilistic Inference to Lifted Linear Programming": 7th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2011)

"Increasing Representational Power and Scaling Inference in Reinforcement Learning": 9th European Workshop on Reinforcement Learning (EWRL 2011)

"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 on
Neural-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)

"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 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

Elena Erdmann (PhD) currently |
Martin Mladenov (PhD) currently |
Alejandro Molina (PhD) currently |
|||

Patrick Schramowski (PhD) currently |
|||||

Babak Ahmadi (PhD) BitStar |
Fabian Hadiji (PhD) goedle.io |
Ahmed Jawad (PhD) Allianz |
|||

Marion Neumann (PhD) Washington University, St. Louis |
Mirwaes Wahabzada (PhD) University of Bonn |
Zhao Xu (PostDoc) NEC |

Project group on Manipulation "Entwicklung und kritische Beleuchtung eines Frameworks zur Extraktion und Übertragung semantischer Informationen zwischen Videos." summer-winter 2017

Ph.D. course on Statistical Relational AI at the Università degli Studi di Trento. spring 2017

Ph.D. course on Lifted Inference and Collective Attention at the Università di Bari. summer 2016

Course on Wissensentdeckung in Datenbanken. summer 2014, 2016

Course on Statistical Relational Learning together with F. Riguzzi (U. Ferrara, Italy) as part of ERASMUS+. summer 2016

Course on Foundations of Data Science. summer 2015, winter 15, 2016

Course on Mathematik fuer Informatiker 1. summer 2015

Course on Probabilistic Graphical Models. winter 2013, 2014, 2015, 2016

Proseminar on Big Data Mining. summer 2014, 2015

Project group on DeepNewsDive: Maschinen lesen Zeitungen. summer-winter 2016

Project group on Infoscreens. summer-winter 2015

Seminar on Big Data Mining. winter 2013

Co-organized a project on Across Scale Data Analysis. summer 2013

Course on Probabilistic Graphical Models. winter 2012

Co-organized course on Geoalgorithms and geo data structurs. winter 2012

Organized project lab on Data Mining and Pattern Recognition. winter 2012

Seminar on Geoinformation III. winter 2012

Course on Probabilistic Graphical Models. winter 2012

Course on Probabilistic Graphical Models. winter 2011

Co-organized practical lab with topics on Lifted Inference and IR. winter 2010

Course on Probabilistic Graphical Models. summer 2009, 2010

Co-organized seminar on Machine Learning for Computer Games. winter 2008

Course on Bayesian networks as part of the Advanced AI course. winter 2006

Deep Phenotyping (BLE)

The goal of this BLE project is the optimization and objectification of phenotyping routines for crop breeding. It combines
sensor technology, automation and deep learning. By using hyperspectral images and deep learning it will help to go beyond
a purely visually assessed disease score for phenotyping of different genotypes.

goedle.io, EXIST

The goedle.io start-up is providing an innovative machine learning technology to maximize engagement and retention for mobile apps, e-commerce, or SaaS products. It is supported by EXIST, a support programme of the German Federal Ministry for Economic Affairs and Energy (BMWi).
This programme aims at improving the entrepreneurial environment at universities and research institutes. It also aims at increasing the number and success of technology and knowledge based business start-ups.

Resource-efficient Graph Mining (SFB 876,DFG)

Linked data and networks occur often in the context of embedded systems. Sensors, RFID-chips, cameras, etc. of products of our daily life continuously produce data and communicate with each other as well as the user. A natural representation of linked data are graphs where objects correspond to the vertices of the graph and the links to its edges. In this project, we will develop new approaches and algorithms for the classification of graphs and linked data sets under resource constraints. To this aim, randomized approaches from algorithmic theory, approaches for mining and learning with graphs (in particular graph kernels) and algorithmic engineering approaches have been combined in this SFB876 research project.

Analysis and Communication for Dynamic Traffic Prognosis (SFB 876, DFG)

The goal os this SFB876 research project is the development of high precision prediction methods for the dynamic behavior of road traffic based on resource-efficient transmission of extended Floating Car Data (xFCD) and other data sources. With the help of collected data from vehicles, triggers for disturbances of the traffic flow should be detected early and countermeasures are applied in real-time. New dynamic microscopic traffic models are needed. Applying Data Mining strategies, these models are re-parameterized in real time in order to handle the heterogeneity of urban traffic.

Efficient Inference for Probabilistic Relational Models using Symmetries and Linear Programming Relaxations (GIF)

Say we know that some people in a social network are friends and some are smokers, how can we infer whether others are smokers and friends? For thousands of people this seems like a daunting computational task. However, such tasks often have strong symmetries (i.e.,repeated structures) that should intuitively translate into fewer computations. In this project, we proposed a novel approach to designing “symmetry aware” algorithms. We built on linear programming (LP) relaxations as the key underlying framework. Despite the popularity of LP relaxations in the graphical models community, they have seen very little use within the SRL literature. In this GIF project, we developed the theory and algorithms needed for applying LPs to SRL, while making effective use of symmetries.

Flexible Skill Acquisition and Intuitive Robot Tasking for Mobile Manipulation in the Real World (EU)

Flexible Skill Acquisition and Intuitive Robot Tasking for Mobile Manipulation in the Real World was a project funded by the European Commission within FP7. The goal of First-MM is to build the basis for a new generation of autonomous mobile manipulation robots that can flexibly be instructed to perform complex manipulation and transportation tasks. The project has focussed on developing a novel robot programming environment that allows even non-expert users to specify complex manipulation tasks in real-world environments. To this aim, we have built upon and extend results in robot programming, navigation, manipulation, perception, learning by instruction, and statistical relational learning to develop advanced technology for mobile manipulation robots that can flexibly be instructed even by non-expert users to perform challenging manipulation tasks in real-world environments.

Relational exploration, learning and inference (SPP 1527, DFG)

The core approach of this DFG project is to organize
exploration, learning and inference on appropriate relational representations implying strong
prior assumptions on the world structure. On these representations we can learn from uncertain
experience compact models of action effects that generalize across objects. We transfer existing
exploration theories to relational representations—leading to a novel level of explorative behavior
that decidedly aims to explore objects to which the current knowledge does not generalize. This
project was to our knowledge the first to combine statistical relational learning methods to tackle
core problems in intelligent robotics, fueling the hope for a major advance in the field. We have demonstrated
our methods on real-world robot platforms manipulating their environments.

STREAM, Fraunhofer ATTRACT

The ability to build computing systems that can observe, understand and act on
human activity has long been a goal of computing research. Such systems could
have profound conceptual and practical implications. Since the ability to reason
and act based on activity is one of the central aspects of human intelligence,
from a conceptual viewpoint such a system could cast light on computational
models of intelligence. More tangibly, perhaps, machines that reason about
human activity could aid humans in aspects of their lives that are today
considered outside the domain of machines.

Most existing activity mining approaches indeed take uncertainties into account, but they do not consider the rich relations among people and objects that exist in the real world. STREAM’s goal was to develop formalisms, models, and algorithms for effective and robust statistical relational activity mining that enables one to develop socio-cognitive aware systems and to apply them on significant real-life applications. To this aim STREAM develop statistical relational learning methods using a Fraunhofer ATTRACT fellowship.

Most existing activity mining approaches indeed take uncertainties into account, but they do not consider the rich relations among people and objects that exist in the real world. STREAM’s goal was to develop formalisms, models, and algorithms for effective and robust statistical relational activity mining that enables one to develop socio-cognitive aware systems and to apply them on significant real-life applications. To this aim STREAM develop statistical relational learning methods using a Fraunhofer ATTRACT fellowship.

APRIL 1&2, EU

One of the key open questions of artificial intelligence concerns "probabilistic logic learning", i.e.the integration of probabilistic reasoning, with first order logic representations and machine learning. The overall goal of the APrIL II project is therefore to develop a sound theoretical understanding of "probabilistic logic learning" that enables one to develop effective probabilistic logic learning systems and to apply them on significant real-life applications. To realize this aim, the APrIL II consortium will (1) develop a number of significant "show-case" applications of "probabilistic logic learning" in the area of bio-informatics, more specifically, concerning protein folding, metabolic pathways, and genetics.(2) develop the needed theory, probabilistic representations, learning algorithms and systems for learning interesting probabilistic logic models in real-life applications on the basis of data. The methodology applied is that of the field of inductive logic programming, which explains the title of the project.

Legal info / Impressum

Technische Universitaet Darmstadt

Fachbereich Informatik

Hochschulstr. 10

64289 Darmstadt

Germany

Technische Universitaet Darmstadt

Fachbereich Informatik

Hochschulstr. 10

64289 Darmstadt

Germany