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Our team

The Data Science and Mining (DaSciM) team is part of the Computer Science Laboratory (LIX) of École Polytechnique.

In the previous years we have conducted research in the areas of databases and data mining. More specifically in unsupervised learning (clustering algorithms and validity measures), advanced data management and indexing (P2P systems, distributed indexing, distributed dimensionality reduction), text mining (word disambiguation for classification, introduced the Graph of Words approach) and ranking algorithms (temporal extensions to PageRank).

More recently, we worked in large scale graph mining (degeneracy based community detection and evaluation), text mining and retrieval for web advertising/marketing and recommendations.

Current research interests and work include the areas:

  • machine learning for graphs (graph kernels, embedding methods, deep learning for graph classification, large scale community detection) with applications in fraud detection
  • NLP and text mining (Graph of Words, Deep learning for text classification, summarization and keyword extraction) applications
  • decision making methods, in particular: mathematical programming (mixed integer linear and nonlinear programming), combinatorial optimization, global optimization, graph theory. We are interested both in methodology and applications, with a special focus on applications in energy optimization and computational geometry.
  • event and anomaly detection in data streams and time series (applications in text streams, sensory data, personalized medicine)
  • structured output prediction (multi-label classification, multi-output and sequential/dynamical models, probabilistic models and neural networks)
  • reinforcement learning (Bayesian models, and deep learning)

The DaSciM team members have supervised fifteen completed Ph.D. theses and published chapters in books and encyclopedias, two international books and more than a 250 papers in international refereed journals and conferences. Also we have co-authored three patents and attracted significant R&D funding including national and international governmental/industrial sources. Members of our team have received the ERCIM, Marie Curie, and Google fellowships. Our team has co-organized the ECML PKDD 2011 conference in Athens, ECML/PKDD 2017 and participates in the senior organization of different AI and Data mining related events (AAAI, IJCAI).

Moreover our group has a long experience in real-world R&D projects in the area of Large Scale Data/Text/time series Mining. Currently we maintain collaborations with industrial partners (including AIRBUS, Google, BNP, Tencent, Tradelab) working on machine learning projects.

Professor Vazirgiannis leads the X/AXA Data Science Chair.
Also, check:

Another relevant research topic is decision making methods, in particular: mathematical programming (mixed integer linear and nonlinear programming), combinatorial optimization, global optimization, graph theory. We are interested both in methodology and applications, with a special focus on applications in energy optimization and computational geometry.

Visit our selected publications page.


Team leader

Michalis Vazirgiannis
Professor

mvazirg ~ lix.polytechnique.fr
lix.polytechnique.fr/~mvazirg/

 

Dr. Vazirgiannis is a Professor at LIX, Ecole Polytechnique in France and leads the Data Science and Mining group. He holds a degree in Physics and a PhD in Informatics from Athens University(Greece) and a Master degree in AI from Heriot Watt University,Edinburgh (UK). He has conducted research in Frauhofer, Max Planck MPI (Germany), in INRIA/FUTURS (Paris). He has been teaching in AUEB (Greece), Ecole Polytechnique, Telecom-Paristech, ENS (France), Tsinghua, Jiaotong Shanghai (China) and in Deusto University (Spain). His current research interests are on machine/deep learning and combinatorial methods for Graph analysis (including community detection, graph clustering, node embeddings and influence maximization), Text mining (including Graph of Words, word embeddings with applications to web advertising and marketing, event detection and summarization). He has active cooperation with industrial partners in the area of analytics and machine learning for large scale data repositories in different application domains (including recommendations, meeting summarizatiom, influence metrics for scientific and social networks, predictive maintenance and others). He has supervised fifteen completed PhD theses. He has published three books and more than a 160 papers in international refereed journals and conferences. He has organized large scale conferences in the area of Data Mining and Machine Learning (such as ECML/PKDD) while he participates in the senior PCs of AI and ML conferences – such as AAAI and IJCAI, He has received the ERCIM and the Marie Curie EU fellowships, the Tencent “Rhino-Bird International Academic Expert Award” in 2017 and since 2015 he leads the AXA Data Science chair.

Latest News

  • Professor Vazirgiannis talk @ Columbia
    April 2018
    Professor Michalis Vazirgiannis gives an invited talk on "Graph based event detection in streams" at the Data Science Institute Colloquium, Columbia University, New York. Find more info here.

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  • Software Engineer for Digital Ads-machine learning

    We are searching for a software engineer to develop a machine learning model that will predict the change in traffic on the website of the advertiser after a TV ad placement. More information here.
  • Post-doc openings

    Post doctoral positions in DaSciM team @Ecole Polytechnique open in machine learning for graphs and text. More information here.