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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.






Secretary's office

The LS8 secretary's office will not be staffed between 19.12.2018 and 04.01.2019.

Digicon 2018

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More than 400 top decision-makers from over 150 companies such as Allianz, BOSCH, Munich Airport, Generali, Google and many more are expected at Digicon 2018. International experts from business and science will present the latest trends, developments and results in the field of machine learning. End-users will talk about their success stories and analysts about the underlying methods. This year, Prof. Dr. Katharina Morik will give a presentation on "Machine Learning and Data Mining - From Theory to Practice".

 

Dortmund Data Science Center (DoDSc) ceremonially opened

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For many scientists at the TU Dortmund, handling large amounts of data is a fundamental part of their daily work. With the Collaborative Research Centre 876 ("Providing Information by Resource-Constrained Data Analysis", speaker: Prof. Dr. Morik) and the Collaborative Research Centre 823 ("Statistics of Nonlinear Dynamic Processes", speaker: Prof. Dr. Krämer), extensive research projects have already been set up at the TU Dortmund that focus on the analysis of large amounts of data. In addition, only recently, together with the University of Bonn and the Fraunhofer Institutes for Material Flow and Logistics (IML) and for Intelligent Analysis and Information Systems (IAIS), one of four competence centers for machine learning in Germany was acquired, which additionally underlines the profile area "data analysis, modelling and simulation" of the TU Dortmund.

This interdisciplinary expertise in data analysis will now be bundled in the Dortmund Data Science Center (DoDSc), to which the Faculties of Statistics, Computer Science, Mathematics and Physics belong. At the opening ceremony on 24 October, scientific lectures were held by Kevin Kröninger (Faculty of Physics, TU Dortmund) and Thomas Lengauer (Max Planck Institute for Computer Science, Saarbrücken), who pointed out further research perspectives for data science. Florian Kruse of Pont 8 was on the program for commercial applications. In addition, Trevor Hastie from Stanford University sent congratulations on the opening of the Dortmund Data Science Center.

News article of the TU Dortmund on the opening of the DoDSc (in German)

 

Photo: TU Dortmund/Martina Hengesbach

Data - who owns it, who stores it, who can access it?

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Big data, data science, machine learning - terms that refer to data and its value for many applications. Who benefits from the data? Who may use it? How are scientific progress, success in economic competition and protection of privacy achieved simultaneously? This volume presents the papers presented at a conference of the North Rhine-Westphalian Academy of Sciences and Arts, with contributions from the fields of computer science, statistics, medicine, engineering, law and economics. In this way, the Academy participates in the urgently needed discussion on how to meet the challenges posed by today's possibilities of data collection and use.

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Stellenausschreibung Sachbearbeiterin

Am Lehrstuhl für künstliche Intelligenz ist eine Stelle als Sachbearbeiterin im Sekretariat ausgeschrieben.

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Competence Center for Machine Learning Rhine-Ruhr will be launched — we are hiring!

Logo Competence Centre

Machine learning is the basis of the digital transformation. Hence, internationally outstanding research and effective transfer into applications is of the utmost importance for a society. Germany and France aim at a collaboration in machine learning research. In this context, the Competence Centre for Machine Learning Rhine-Ruhr (ML2R), funded by the Federal Ministry of Education and Research (BMBF), is now being launched in Dortmund and Bonn/Sankt Augustin.

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29.5.2018 - Chancellor Merkel discusses artificial intelligence with experts of the field

group photo with Chancellor Merkel

Katharina Morik was invited as an expert in machine learning, co-chairing a group in the German Platform for Learning Systems and head of the Competence Center for Machine Learning Rhein Ruhr.

28th of May at the Federal Chancellery, Angela Merkel was talking with experts on artificial intelligence.
Artificial intelligence is one of the central technologies of the future and currently one of the
biggest drivers of digitalization. 
The Chancellor discussed the potentials and challenges of artificial intelligence for
Germany. The Federal Government intends to bundle all measures in this area and combine
them into a national strategy to promote the use of artificial intelligence for the benefit of
the economy and society.

Merkel invited experts from universities, research institutions, and companies. In addition to her,
the Federal Government was represented by the Head of the Federal Chancellery, the Federal Ministers
of Education and Research, Economics and Energy, Labour and Social Affairs, Transport and
Digital Infrastructure, and by the Federal Government Commissioner for Digitalization.

The conversation was not public.

Photo: Federal Government / Jochen Eckel

International Conference: The Next Level of Mobility: Automation, Multi-Modal Services and the Importance of Data

Das nchste Level der Mobilitt Poster

The integrated acquisition and evaluation of data influences our day to day lives, particularly with respect to traffic and mobility. Digital technologies are used to control traffic, traffic infrastructures as well as overall traffic flow. As a result, increasingly specific products can be developed. However, data protection is always a major concern when dealing with data. These concerns will be addressed at the international conference in Dortmund on the 28th of May, 2018.

Conference agenda (in German)

Nico Piatkowski Defends His Dissertation at LS8

Nico Piatkowski

Nico Piatkowski has successfully defended his dissertation “Exponential Families on Resource-Constrained Systems” with an overall grade of summa cum laude. The committee members were Prof. Jens Teubner (chair, TU Dortmund), Prof. Katharina Morik (supervisor, TU Dortmund), Prof. Stefano Ermon (reviewer, Stanford University), Prof. Jakob Rehof (TU Dortmund).

Assistant Professor (W1) in Smart City Science

With more than 6,200 employees in research, teaching and administration and its unique profile, TU Dortmund University shapes prospects for the future: The cooperation between engineering and natural sciences as well as social and cultural studies promotes both technological innovations and progress in knowledge and methodology. And it is not only the more than 34,600 students who benefit from that. The Faculty for Computer Science at TU Dortmund University, Germany, is looking for a Assistant Professor(W1) in Smart City Science specialize in research and teaching in the field of Smart City Science with methodological focus in computer science (e.g. machine learning and/or algorithm design) and applications in the area of Smart Cities (e.g. traffic prediction, intelligent routing, entertainment, e-government or privacy).

Applicants profile:

  • An outstanding dissertation and excellent internationally recognized publications in the field of computer science methods for Smart Cities
  • Experience in raising third-party funding
  • The willingness to participate in research collaborations within and outside TU Dortmund University, such as CRC 876 "Availability of information through analysis under resource constraints"
  • Language competence in German or English are required
  • Appropiate participation in teaching in the faculty's courses of study

The TU Dortmund University aims at increasing the percentage of women in academic positions in the Department of Computer Science and strongly encourages women to apply. Disabled candidates with equal qualifications will be given preference.

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Lecture "Große Daten, Kleine Geräte" ("Big Data, Small Devices") in the Science Notes

Science Notes Poster

Intelligent fabrics, fitness wristbands, smartphones, cars, factories, and large scientific experiments are recording tremendous data streams. Machine Learning can harness these masses of data, but storing, communicating, and analysing them spends lots of energy. Therefore, small devices should send less, but more meaningful data to a central processor where additional analyses are performed.

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Katharina Morik among the leaders of Germany's "Platform Learning Systems"

WG leaders

Germany ranks among the pioneers in the field of learning systems and Artificial Intelligence. The aim of the Plattform Lernende Systeme initiated by the Federal Ministry of Education and Research is to promote the shaping of Learning Systems for the benefit of individuals, society and the economy. Learning Systems will improve people’s quality of life, strengthen good work performance, secure growth and prosperity and promote the sustainability of the economy, transport systems and energy supply.

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Merry Christmas and a Happy New Year 2018

Christmas 2017

We wish you a merry christmas and a happy new year.

We used the Deep Visualization Toolbox of Yosinski for creating a nice picture of the visit of the three holy kings.

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.

 
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