News from the Artificial Intelligence Group

The chair of artificial intelligence deals with the wide field of machine learning. In particular the chair concentrates on the development and implementation of learning algorithms that solve challenging problems.

Best Paper Award of the International Conference on Spatial Information Theory (COSIT) 2017

Best Paper Award of the International Conference on Spatial Information Theory (COSIT) 2017

The joint work "On Avoiding Traffic Jams with Dynamic Self-Organizing Trip Planning" of Thomas Liebig and Maurice Sotzny received the Best Paper Award of the International Conference on Spatial Information Theory (COSIT) 2017.

Vacant Professorship(W2) Position

TU Dortmund University is seeking an outstanding scientist in the field of data mining of large datasets with a current research perspective and publications in high-ranked international venues. Applicants should complement the research activities of the Faculty for computer science and contribute to interdisciplinary collaborative research projects, especially the collaborative research centre CRC 876 “Providing Information by Resource-Constrained Data Analysis“.

Further information is given in the linked document

Introduction to Machine Learning for Users and the General Public

The Academy of Engineering has presented an online course on machine learning at CeBIT: http://www.acatech.de/de/projekte/projekte/mooc-maschinelles-lernen.html

After an overview presented by Prof. Dr. Stefan Wrobel (Fraunhofer St. Augustin), Katharina Morik introduces two basic methods with application examples from her many years of practical experience: the support vector machine (SVM) and decision trees. Kristian Kersting presents probabilistic graphical models.

Klassifikation und Regression - Stützvektormethode (Classification and Regression - SVM)

Download 120 MB [mp4]
Source: acatech

Klassifikation und Regression - Entscheidungsbäume (Classification and Regression - Decision Trees)

Download 87 MB [mp4]
Source: acatech

Probabilistische Graphische Modelle (Probabilistic Graphical Models)

Download 86 MB [mp4]
Source: acatech

Outstanding Graduates of TU Dortmund receive Hans-Uhde Award

Award Recipients

Four graduates of TU Dortmund received the Hans-Uhde Award for their outstanding theses. Niklas Haarmann (Faculty of Bio- and Chemical Engineering), Chris Kittle (Faculty of Electrical Engineering and Information Technology) and Lukas Pfahler (Faculty of Computer Science) achieved a master's degree and graduated as valedictorians. Christian Gehring (Faculty of Mechanical Engineering) received a grade of 1,0 for his bachelor's thesis. Additionally, three graduates of FH Dortmund and one employee of Uhde Inventa-Fischer GmbH were decorated by the Hans-Uhde Foundation.

The graduates of TU Dortmund were awarded a golden coin, a certificate and a monetary price by Guido Baranowsky, chairman of the Hans-Uhde foundation. In his thesis, Lukas Pfahler explored the question how to enable computers to learn German grammar. The ceremony took place at thyssenkrupp Industion Solutions AG in Dortmund. The ceremonial address — "Precision Medicine and Foundational Research; Innovation with Potential" — was delivered by Prof. Daniel Rauh. The goal of the Hans-Uhde Foundation is to promote Science, Schooling and Education. This is why it annually decorates outstanding students as well as pupuils. The award ceremony was attended by both Hans and Roswitha Uhde until 2011, when Friederich Uhde passed. The widowed Roswitha Uhde continued to attend the ceremonies up until her passing in 2017.


Hans-Uhde Award 2017

Lukas Pfahler, M.Sc.
TU Dortmund, Faculty of Computer Science
Master's Thesis: Explicit and Implicit Feature Maps for Structured Output Prediction


Marco Stolpe Defends His Dissertation at LS8

Marco Stolpes Disputation

Marco Stolpe has successfully defended his dissertation “Distributed Analysis of Vertically Partitioned Sensor Measurements under Communication Constraints”. His thesis was supervised by Katharina Morik and can be summarized (in German) as follows:

Schwerpunkt der Arbeit ist die verteilte Analyse großer Mengen vertikal partitionierter Sensordaten unter Berücksichtigung von Kommunikationsbeschränkungen. Hierbei hängt die vorherzusagende Zielgröße jeweils von an unterschiedlichen Knoten im Netzwerk gespeicherten Merkmalswerten ab. Das Szenario hat vielfältige Anwendungen im Kontext des Internet of Things und Industrie 4.0, wie etwa die Vorhersage der finalen Produktqualität anhand von an verschiedenen Bearbeitungsstationen erfassten Prozessparametern, die Vorhersage des Gesamtstromverbrauchs anhand des zuvor erfassten Verhaltens unterschiedlicher Stromabnehmer im Smart Grid oder die Vorhersage von Verkehrsflüssen in Smart Cities. Das Szenario erweist sich als besonders herausfordernd in Fällen, in denen Kommunikation oder Energie zu beschränkt sind, um alle Daten zu zentralisieren, da bereits für die Vorhersage Daten unterschiedlicher Knoten zusammengeführt werden müssen. In der Dissertation werden, motiviert durch eine Fallstudie zur Qualitätsvorhersage in verketteten Produktionsprozessen in der Stahlindustrie, kommunikationseffiziente Algorithmen für drei unterschiedliche Problemstellungen der verteilten Datenanalyse entwickelt: (1) Die lokale Reduktion von Messwerten unmittelbar dort, wo sie erfasst werden (also noch vor ihrer Übertragung), (2) die Reduktion von Messwerten, die zwischen lokalen Knoten und einem zentralen Koordinator übertragen werden und (3) die Reduktion von Informationen über vorherzusagende Zielgrößen, die zwischen Knoten übertragen werden. Die Algorithmen reduzieren die übertragene Datenmenge im Vergleich zur Übermittlung aller Daten in einem Netzwerk jeweils um ca. eine Größenordnung, bei ähnlicher Vorhersagegüte. Algorithmus (3) basiert wiederum auf einem neu entwickelten Ansatz für das relativ neuartige Problem des Lernens aus Label-Verhältnissen, dessen Lösung weitere Anwendungen im Kontext von Industrie 4.0 erschließt.


Christian Pölitz Defends His Dissertation at LS8

Christian Plitzs Disputation

Christian Pölitz has successfully defended his dissertation “Automatic Methods to Extract Latent Meanings in Large Text Corpora”. His thesis was supervised by Katharina Morik and can be summarized as follows:

This thesis concentrates on Data Mining in Corpus Linguistic. We show the use of modern Data Mining by developing efficient and effective methods for research and teaching in Corpus Linguistics in the fields of lexicography and semantics. Modern language resources as they are provided by Common Language Resources and Technology Infrastructure (http://clarin.eu) offer a large number of heterogeneous information resources of written language. Besides large text corpora, additional information about the sources or publication date of the documents from the corpora are available. Further, information about words from dictionaries or WordNets offer prior information of the word distributions. Starting with pre-studies in lexicography and semantics with large text corpora, we investigate the use of latent variable methods to extract hidden concepts in large text collections. We show that these hidden concepts correspond to meanings of words and subjects in text collections. This motivates an investigation of latent variable methods for large corpora to support linguistic research.


Merry Christmas and a Happy New Year

Christmas 2016

The secretary's office is not occupied between December 19th, 2016 and January 6th, 2017. We wish you a merry Christmas and a happy New Year!

BMVI Data-Run

On December 2nd and 3rd, the Federal Ministry of Transport and Digital Infrastructure (BMVI) hosted the second BMVI Data-Run, this time with the theme "Realtime Data in Traffic". Over the course of two days, attending teams worked on creating innovative mobility solutions based on the provided data.

Sebastian Peter and Philipp Honysz from LS8 participated with the idea of creating an app that would help commuters compensate for traffic problems. They implemented an Android app which analyses the user's commute and notifies them of impending problems, such as overloaded bicycle stations. Additionally it uses a Google API to compute routes for common means of transportation.

Graduate Students between News, Space, and Science

This summer, three of our graduate studetns were between news, space and science. They were at Google, NASA, Stanford and the Wirtschaftswoche. While it was certainly not a walk in the park, it was definitely an experience and a great success. Congratulation! 

Elena Erdmann received a Google News Lab Fellowship and worked two months at the Wirtschaftswoche. She has developed both journalistic know-how and technical skills to drive innovation in digital and data journalism. Nico Piatkowski visited Stefano Ermon at Stanford University. Together they worked on techniques for scalable and exact inference in graphical models. He also made a detour to NASA. Last but not least, Martin Mladenov got an internship at Google. Some people say this is more difficult than getting admitted to Stanford or Harvard. Who knows? But this year they accepted about 2% of applicants (1,600 people). What did he work on? We do not know it, but he visited Craig Boutilier, so very likely something related to making decisions under uncertainty.

Health: Smart Data & Data Analytics


The kick-off of the "Smart Data & Data Analytics" department of CPS.HUB took place on 23rd of November 2016 at the Leibniz Institute for Analytical Sciences (ISAS). This session focused on a variety of aspects of data and data analysis in the context of health and health economy.

After the introduction by Monika Gatzke, the topic of "health" with regard to Smart Data was further discussed:

  • Prof. Dr. Katharina Morik (Head of the Department) gave an overview and presented a detailed application in intensive care medicine.
  • Sven Löffler from T-Systems spoke about Smart Data potentials in the health care sector using the example of self-tracking data.
  • Dr. Wolfgang Thronicke of Atos C-LAB presented Big Dependable Systems. These are systems that consist of different interdependent subsystems and are the object of the project Medolution.
  • The founder of the Quantified Self Movement in Germany, Florian Schumacher, spoke about the potential for Big Data Analytics.
  • Philip Potratz from the Cluster InnovativeMedizin.NRW presented the project Smart.Health.Data.
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relNet Opening Workshop

The project partners of LS8, Dortmund and CERES, Bochum hosted an opening workshop for their new joint project relNet on "Modelling Topics and Structures in Religious Online Communication" in Bochum on May 23-24. The goal of this project is to apply methods of data analytics, network analysis and text mining to analyse how digital communication has changed religious communities and the social roles within these communities.

On to days we have presented the project, listened to talks by our invited guests and discussed the potentials of joint research in computer science and the social sciences, in this case religious studies. Click below for the full program.


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Springer Edited Volume `Compuational Sustainability' published

Katharina Morik and Kristian Kersting together wit Jörg Lässig from the University of Applied Sciences Zittau/Görlitz have published an edited volume on Computational Sustainability. Computational Sustainability is a broad field that attempts to optimize societal, economic, and environmental resources using methods from computer science, mathematics and related fields:

Springerl Jörg Lässig, Kristian Kersting, Katharina Morik, Computational Sustainability. Studies in Computational Intelligence, Volume 645 2016, Springer, ISBN: 978-3-319-31856-1, 2016.
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Best Paper Award of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) 2015

The joint work "Predicting Purchase Decisions in Free To Play Mobile Games" of Kristian Kersting with colleagues from Wooga, goedle.io, Aalborg University, and the Fraunhofer IAIS received the Best Paper Award of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE) 2015.

LS8 is an international collaborator of CompSusNet

LS8 is an international collaborator of CompSusNet. CompSustNet is a research network sponsored by the National Science Foundation through an Expeditions in Computing award. Twelve U.S. academic institutions led by Cornell University, along with many national and international collaborators, are exploring new research directions in computational sustainability. (more...  )

Morgan and Claypool Book on Statistical Relational AI

Together with colleagues from UBC, KU Leuven, and U. Indiana, Kristian Kersting published a book on Statistical Relational AI. This is the study and design of intelligent agents that act in worlds composed of individuals (objects, things), where there can be complex relations among the individuals, where the agents can be uncertain about what properties individuals have, what relations are true, what individuals exist, whether different terms denote the same individual, and the dynamics of the world:

Morgan and Claypool Luc De Raedt, Kristian Kersting, Sriraam Natarajan, David Poole, Statistical Relational Artificial Intelligence: Logic, Probability, and Computation. Morgan and Claypool Publishers,Synthesis Lectures on Artificial Intelligence and Machine Learning, ISBN: 9781627058414, 2016.
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VaVeL Project

Urban environments are flooded with data from fixed or mobile sensors that are gathering data. If these data were used successfully, European citizens could benefit in various areas like public transport or crime prevention. However, urban data is heterogenous, noisy and unlabeled, since the usability of the data is low. The VaVeL project aims towards using these data in application for increasing the living conditions in urban areas. The goal of the project is developing a general framework for managing and mining heterogenous urban data streams.

As part of the project, the functionality of current stream frameworks shall be fit on data streams from urban sensors. The access to urban data streams is not an easy task; In this project, a set of black boxes will be implemented to give easier access to the data and analysis procedures. Big data companies shall get an access to the gathered knowledge so that actual problems of an urban environment can be tackled.

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Call for Papers - Data Mining for Smart Cities

There is a Call for Papers for the journal Data Mining for Smart Cities. They are looking, for example, for the following topics:

  • Real-time nowcasting and prediction of events
  • Interactive exploration of city data
  • Feature extraction and deep learning from urban data

Submission is due July 4, 2016, 23:59.

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