Activities
Conference and Event Organizations:
CoChair of the ICML 2018 Workshop Program
General CoChair of UAI 2018
CoOrganizer of the 14th biannual Conference of the German Society for Cognitive Science 2018
CoChair of the UAI 2017 Program Committee
CoChair of the KDD 2015 Best Paper Award Committee
CoChair of the ECML PKDD 2013 Program Committee
CoChair of ACAI 2018 Summer School on Statistical Relational AI as part of the Relational AI Days (RAID) 2018, PC CoChair of the 4th European Starting AI Researcher Symposium (STAIRS) 2012, PC CoChair of the AAAI Student Abstract and Poster Program 201214
PC CoChair of
ICML 2018 Enabling Reproducibility in Machine Learning MLTrain@RML Workshop,
KDML 2018, DeLBP 2018,
NIPS 2017 Highlights Workshop,
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
Selected Program Committees/Reviewer:
NIPS 2018, ICDM 2018, DSAA 2018, CP 2018, MLG 2018, KI 2018, NAACLHLT 2018, WWW 2018, IJCAIECAI 2018 (AC), KDD 2018 (SPC), ICRL 2018, SIGMOD 2018, AAAI 2018 (SPC, Senior Member Track), 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), GermanIsraeli Foundation for Scientic Research and
Development (GIF), Freie und Hansestadt Hamburg  BWFG, Swiss National Science Foundation, The Netherlands Organisation for Scientific
Research, Research Foundation  Flanders (FWO), The Ministry of Science and Technology
of Israel, Alexander von HumboldtStiftung, CarlZeissStiftung, 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 (20112017)
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)
Scientific Advisory Boards and Expert Groups
BMBF Platform Lernende Systeme, 2018
Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras, India, 2018
Kompetenzzentrum Verbraucherforschung NRW, 2018
BMBF Project ABIDA  Assessing Big Data, 2015
goedle.io, 20152017
heydeal.de, 2016
pflegix.de, 20162017
GameAnalytics, 20122014
Invited Talks, Panels and Training
Talks and Panels
"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 (NetSciX 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 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, DespotenWie wehrhaft ist unsere Demokratie?"
"Data Mining":
THINK BIG: nrvision.de
"AlgorithmenWer 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 HumanCentred Computing (HCC 2016)
"Collective Attention on the Web": International School and Conference on Network Science (NetSciX 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 BrazilianGerman 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 SinoGerman 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 onNeuralSymbolic 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 Artificial Intelligence: Logic, Probability, and Computation", NIPS 2017
"Feeding the World with Big Data", Computational Sustainability Virtual Seminar Series, Cornell University, USA, fall 2017
"Tractable Probabilistic Graphical Models", 4th International Summer School on Resourceaware Machine Learning 2017
"Artificial Intelligence  Facts, Chances, Risks", Research Training Group of the German National Academic Scholarship Foundation 201718
"Statistical Relational Artificial Intelligence: Logic, Probability, and Computation", AAAI 2017
"DataDiven 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
"Firstorder Planning", ICAPS 2008
"SRL without Tears: An ILP Perspective on SRL", ILP 2008
"DecisionTheoretic 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
Group Members and Alumni

Elena Erdmann currently 

Alejandro Molina currently 

Patrick Schramowski currently 

Karl Stelzner currently 

Fabrizio Ventola 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 

Martin Mladenov Google Research 

Zhao Xu (PostDoc) NEC 
Teaching
"Was ist eigentlich Künstliche Intelligenz?", Children's university lecture, comprehensive school Gänsewinkel Schwerte, Germany, fall 2017
Probabilistische Graphische Modelle, ACATECH Massiv Open Online Course on "Machine Learning", spring 2017
Extended Seminar on "Interactive Machine Learning." winter 2017
Course on "Statistical Relational Artificial Intelligence." winter 2017
Course on "Probabilistic Graphical Models." winter 2017
Project group on Manipulation "Entwicklung und kritische Beleuchtung eines Frameworks zur Extraktion und Übertragung semantischer Informationen zwischen Videos." summerwinter 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. summerwinter 2016
Project group on Infoscreens. summerwinter 2015
Seminar on Big Data Mining. winter 2013
Coorganized a project on Across Scale Data Analysis. summer 2013
Course on Probabilistic Graphical Models. winter 2012
Coorganized 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
Coorganized practical lab with topics on Lifted Inference and IR. winter 2010
Course on Probabilistic Graphical Models. summer 2009, 2010
Coorganized seminar on Machine Learning for Computer Games. winter 2008
Course on Bayesian networks as part of the Advanced AI course. winter 2006
Funding
Argumentative Machine Learning (KE 1686/31, SPP 1999, DFG)
In this DFG project, we will investigate radically novel machine learning approaches in detail and develop
the new field of “argumentative machine learning” in general: a tight integration of Computational
Argumentation and Machine Learning. This has several benefits. The use of argumentation
techniques allows to obtain classifiers, which are by design able to explain their decisions, and
therefore addresses the recent need for Explainable AI : classifications are accompanied by a
dialectical analysis showing why arguments for the conclusion are preferred to counterarguments;
this automatic deliberation, validation, reconstruction and synthesis of arguments helps in assessing
trust in the classifier, which is fundamental if one plans to take action based on a prediction.
Argumentation techniques in machine learning also allows the easy integration of additional expert
knowledge in form of arguments.
Connecting Editors and Researchers (Zeit Online, GRK 1994, DFG)
Data Journalism, News Recommendation, and Fake News Detection: more and more AI techniques
find there way into (online) journalism application. However, the use
of Data Science, Natural Language Processing and Machine Learning in Journalism
is still at the beginning. So far, there is a significant discrepancy between research and
Editor's everyday life. In this cooperation between Zeit Online and the TU Darmstadt as well as the
DFG Research Training Group GRK 1994 "Adaptive Preparation of Information from Heterogeneous Sources" (AIPHES), we aim at
closing this gap and build a bridge between the disciplines.
Deep Inference Machines (GRK 1994, DFG)
Inference machines, viewing inference computations as
trainable computation graphs, have paved the way to “deepify”
classical language models. Viewing paraphrasing and
harmonization as an inference task in datadriven relational
probabilistic models, we therefore recast relational inference
using inference machines in this project within the DFG Research Training Group GRK 1994
"Adaptive Preparation of Information from Heterogeneous Sources" (AIPHES. This allows us to lift recent advances
in deep (language) modeling and learning to relational
domains, consisting of (textual and visual) objects and relations
among them, and to explore the resulting deep relational
inference machines for datadriven textual and visual inference
over heterogeneous domains.
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 startup is providing an innovative machine learning technology to maximize engagement and retention for mobile apps, ecommerce, 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 startups.
Resourceefficient Graph Mining (SFB 876,DFG)
Linked data and networks occur often in the context of embedded systems. Sensors, RFIDchips, 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 resourceefficient 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 realtime. New dynamic microscopic traffic models are needed. Applying Data Mining strategies, these models are reparameterized 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 FirstMM 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 nonexpert users to specify complex manipulation tasks in realworld 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 nonexpert users to perform challenging manipulation tasks in realworld 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 realworld 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 sociocognitive aware systems and to apply them on
significant reallife 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 reallife applications. To realize this aim, the APrIL II consortium will (1) develop a number of significant "showcase" applications of "probabilistic logic learning" in the area of bioinformatics, 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 reallife 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.