The Worldwide Network of the Competence Center

With its worldwide network, the Competence Center ML2R connects renowned experts in Artificial Intelligence and Machine Learning. The network is an ideal ecosystem for scientific exchange and collaborations on cutting-edge technologies and methods of Machine Learning. Get an overview of the members of the network and learn about the institutions the experts work for as well as the scientific topics they conduct research on.

The Steering Board Members

The Steering Board is the external scientific advisory body of the Competence Center. Its members are high-ranking, internationally recognized researchers from all over the world who evaluate the scientific achievements of the ML2R against the state-of-the-art and help shape the research agenda. Within the framework of annual consultations, the Steering Board members advise the ML2R researchers and provide forward-looking impulses for the scientific and strategic direction of the Competence Center.

Dr. Francesco Bonchi
Scientific Director at the ISI-Foundation, Italy

Dr. Kamalika Das
Machine Learning Engineer at VMware, United States of America

Prof. Dr. Thomas Gärtner
Professor for Machine Learning at the Technical University of Vienna

Prof. Dr. Dimitrios Gunopulos
Professor for Informatics and Telecommunications at the National and Kapodistrian University of Athens, Greece

Prof. Dr. Thorsten Joachims
Professor for Computer Science and Information Science at Cornell University, United States of America

Prof. Dr. Stan Matwin
Professor for Computer Science at Dalhousie University and Director of the Institute for Big Data Analytics, Canada

Prof. Dr. Srinivasan Parthasarathy
Professor for Computer Science and Engineering as well as Biomedical Informatics at Ohio State University, United States of America

Prof. Dr. Arno Siebes
Professor for Algorithmic Data Analysis at Utrecht University, Netherlands

Prof. Dr. Geoff Webb
Professor for Data Science and Artificial Intelligence and Research Director of the Monash Data Futures Institute at Monash University, Australia

Prof. Dr. Osmar Zaïane
Professor for Computing Science at the University of Alberta and Scientific Director of the Alberta Machine Intelligence Institute (AMII), Canada

The Scientific Supporters

Renowned scientists supported the ML2R in its initial phase and thus contributed to the establishment of the Competence Center. The scientific supporters offer the ML2R researchers the opportunity for cooperation and scientific exchange on a leading international level.

Prof. Karsten Borgwardt
ETH Zurich, Switzerland

BSD G 234
Mattenstrasse 26
4058 Basel
Switzerland

Research Interests

Graph Kernels, Pattern Mining, Bioinformatics, Medical Informatics

ML2R-related Research

  • Kernel Methods
  • Learning from Graphs

Bât. B. Pascal
5° Avenue Jean Capelle
69621 Villeurbanne
France

Research Interests

Knowledge Discovery in Databases, Constraint-based Data Mining, Unsupervised Knowledge Discovery from Data (Clustering, Co-Clustering, Association Rule Mining, Sequential Pattern Mining, Graph Mining)

ML2R-related Research

  • Learning from Graphs

Prof. Luc de Raedt
KU Leuven, Belgium

Departement Computerwetenschappen
Celestijnenlaan 200A – bus 2402
3001 LEUVEN
Belgium

Research Interests

Automating Data Science, Inductive Logic Programming, AI-assisted Data Acquisition, Probabilistic (Logic) Programming in ProbLog

ML2R-related Research

  • Integration of Knowledge- and Data-driven Approaches

Prof. Pedro Domingos
University of Washington, United States of America

Paul G. Allen School of Computer Science & Engineering
University of Washington
Seattle, WA, 98195-2350
USA

Research Interests

Learning concepts represented by sets of rules, Using examples as implicit definitions of concepts, Using probabilistic representations and analyses to address the uncertainty inherent in Learning, Automating the process of selecting representations for concepts, Learning several models and combining them to improve accuracy and atability

ML2R-related Research

  • Kernel Methods
  • Knowledge Extraction from Texts, Probabilistic Graphical Models

Prof. João Gama
University of Porto, Portugal

LIAAD-INESC Porto
Rua Dr. Roberto Frias, 378
200-378 Porto
Portugal

Research Interests

Concept Drift, Ensembles of Classifiers, Constructive Induction

ML2R-related Research

  • Data Streams

Prof. Lise Getoor
UC Santa Cruz, United States of America

University of California Santa Cruz
Engineering 2
1156 High Street
Santa Cruz, CA 95064
USA

Research Interests

Reasoning under Uncertainty, Causal Relational Learning, Data Science, Statistical Relational Learning, Entity Resolution, Probablistic Soft Logic

ML2R-related Research

  • Fairness
  • Learning from Graphs

Director Fosca Giannotti
ISTI-CNR, Italy

Area della Ricerca CNR di Pisa
Via G. Moruzzi 1
56124 Pisa
Italy

Research Interests

Smart Cities, Privacy in Social Data, Privacy/Security in Data Mining Outsourcing, Privacy-preserving Data Mining, Privacy-preserving Publication, Data Mining, Spatio-temporal Mining, Social Network Analysis, Multidimensional Social Network

ML2R-related Research

  • Privacy-preserving Data Mining, Explanation of ML-models
  • Trajectory Mining, Spatio-temporal Models

Prof. Bart Goethals
University of Antwerp, Belgium

Campus Middelheim
Middelheimlaan 1
2020 Antwerpen
Belgium

Research Interests

Big Data Analytics, Recommendation Systems, Data Cleaning

ML2R-related Research

  • Curation of Data
  • Interactive Machine Learning
  • Learning from Graphs / Integration of Knowledge- and Data-driven Approaches

Prof. Hillol Kargupta
Agnik LLC, United States of America

Department of Computer Science and Electrical Engineering
1000 Hilltop Circle
University of Maryland Baltimore County
Baltimore, MD 21250
USA

Research Interests

Data Analytics for Connected, Distributed and Ubiquitous Data Mining: Algorithm and Experimental System Development, Distributed Computation, Knowledge Discovery and how it influences society, Peer-to-peer Data Mining, Privacy Issues in Mining Multi-party Distributed Data, Data Stream Mining for Resource-constrained Devices, Distributed Computation in Gene Expression, Genetic Algorithms and Evolutionary Systems

ML2R-related Research

  • Distributed Machine Learning on the Edge

Prof. Tei-Wei Kuo
National Taiwan University, Taiwan

Department of Computer Science and Information Engineering
Graduate Institute of Networking and Multimedia
Taipeh

Research Interests

Real-time Systems, Embedded Systems, Flash-memory Storage Systems, Non-volatile Memory Storage Systems

ML2R-related Research

  • Architecture-based Machine Learning, Memory Models, Compression

Prof. Shie Mannor
Technion, Israel

The Technion
Faculty of Electrical Engineering
Fishbach Bldg
Haifa
Israel

Research Interests

Reinforcement Learning, Learning and Modeling Dynamics from Data, Systems that include Multiple Decision Makers: Multiagent/Distributed/Many Players/Adaptive Systems, Drift Detection

ML2R-related Research

  • Bandit Models
  • Learning from Graphs

Prof. Horoshi Motoda
Osaka University, Japan

Research Interests

Social Network Analysis, Artificial Intelligence, Machine Learning in General, Data Mining, Knowledge Discovery and Scientfic Discovery, Knowledge Acquisition from Experts

ML2R-related Research

  • Network Analysis, Integration of Knowledge- and Data-driven Approaches

Prof. Michèle Sebag
Paris-Sud University, France

Equipe A & O
Université Paris Saclay
91190, Gif sur Yvette
France

Research Interests

Causal Modelling, Preference Learning, Surrogate Optimization, AutoML

ML2R-related Research

  • Verification and Validation of ML-models
  • Machine Learning Applications in Social Sciences
  • Numerical Optimization
  • Regularisation, Feature Construction, Compositional Auto-encoders

PhD Katrin Tomanek
Google Translation, United States of America

763 Guerrero Street
San Francisco, CA 94110
USA

Research Interests

Science Data, Process Natural Language, Make Machines Learn, Analyse Social Media, Automatic Speech Recognition for Dysarthric Speech

ML2R-related Research

  • Knowledge Extraction from Texts

Prof. Zhi-Hua Zhou
Nanjing University, China

National Key Laboratory for Novel Software Technology
Nanjing University, Xianlin Campus
163 Xianlin Avenue, Qixia District
Nanjing 210023
China

Research Interests

Multi-label Learning, Multi-instance Learning, Multi-view Learning, Semi-supervised and Active Learning, Cost-sensitive and Class-imbalance Learning, Metric Learning, Dimensionality Reduction and Feature Selection, Ensemble Learning, Structure Learning and Clustering, Crowdsourcing Learning, Logic Learning

ML2R-related Research

  • Network Analysis, Spatio-temporal Models, Knowledge Graphs

The French Partners

Together with leading French experts, the ML2R advances Germany’s cooperation on AI research with France. Building on long-standing collaborations at the ML2R sites, Franco-German research projects are actively promoted by the Competence Center. Under the leadership of spokesperson Prof. Dr. Katharina Morik, the office of coordination at the ML2R organizes the collaboration between France’s National Artificial Intelligence Research Program and the German Network of National Centres of Excellence for AI Research.

Prof. Albert Bifet
Télécom Paris Tech

LTCI, Télécom Paris
Data, Intelligence and Graphs Team
19 place Marguerite Perey
91120 Palaiseau, France

Research Interests

Machine Learning for Big Data Streams, IoT Analytics, Open Source Software

ML2R-related Research
  • Distributed Machine Learning on the Edge

Resource-Constrained ML

Bât. B. Pascal,
5° Avenue Jean Capelle
69621 Villeurbanne, France

Research Interests

Knowledge Discovery in Databases, Constraint-based Data Mining, Unsupervised Knowledge Discovery from Data (Clustering, Co-Clustering, Association Rule Mining, Sequential Pattern Mining, Graph Mining)

ML2R-related Research
  • Learning from Graphs

ML with Complex Knowledge

Campus SophiaTech
450 Route des Chappes
06410 Biot, France

Research Interests

Tractable and Scalable Bayesian Inference Techniques for Gaussian Processes, Deep/Conv Nets with Applications in Life and Environmental Sciences

ML2R-related Research
  • Interpretation of Ensembles
  • Probabilistic Models
  • Regularisation, Bayesian (Deep) Learning

Trustworthy ML

Resource-Constrained ML

Modular ML

Prof. Elisa Fromont
Centre de recherche IRISA/INRIA Université de Rennes 1

263 Avenue du Général Leclerc
Bâtiment 12 F, Campus de Beaulieu
35042 Rennes, France

Research Interests

Large Scale Collaborative Data Mining, Object Detection, Data Intelligence, Graph Mining, Scene Analysis, Inductive Logic Programming.

ML2R-related Research
  • Explanations of Unsupervised Learning
  • Learning from Graphs

Trustworthy ML

ML with Complex Knowledge

Batiment Blaise Pascal
20 av Albert Einstein
69621 Villeurbanne cedex, France

Research Interests

Subgroup Discovery, Co-Clustering, Constraint-based Pattern Mining, Graph Modeling

ML2R-related Research
  • Learning from Graphs

ML with Complex Knowledge

PhD François Schnitzler
InterDigital R&D France

R&I Home and Enterprise
Wireless & Networking Lab InterDigital
975 Avenue des Champs Blancs
35576 Cesson-Sévigné, France

Research Interests

Modern Hardware, Communication Networks, Ensemble Methods, Time Series Modelling

ML2R-related Research
  • Distributed Machine Learning on the Edge
  • Graphical Models, Bayes

Resource-Constrained ML

ML with Complex Knowledge

Prof. Michèle Sebag
Université Paris-Sud

Equipe A & O
Université Paris Saclay
91190 Gif sur Yvette, France

Research Interests

Causal Modelling, Preference Learning, Surrogate Optimization, Auto-ML

ML2R-related Research
  • Verification and Validation of ML-Models
  • Machine Learning Applications in Social Sciences
  • Numerical Optimization
  • Regularisation, Feature Construction, Compositional Auto-Encoders

Trustworthy ML

Human-Oriented ML

Resource-Constrained ML

Modular ML

Prof. Christel Vrain
LIFO – Université d’Orléans

LIFO – University of Orléans
BP 6759
Cedex 2
45067 Orléans, France

Research Interests

Constrained Clustering using Expert Knowledge with the Help of Declarative Approaches such as Constraint Programming and Integer Linear Programming, Inductive Logic Programming, Relational Learning

ML2R-related Research
  • Memory
  • Complex Data, Text, Chemistry

Resource-Constrained ML

ML with Complex Knowledge