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, summarisation and keyword extraction) applications
- decision making methods, in particular: mathematical programming (mixed integer linear and nonlinear programming), combinatorial optimization, global optimisation, 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, personalised 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 lead the X/AXA Data Science Chair (2015-2018) and currently leads the ANR-HELAS chair on Deep Learning for heterogeneous data (graphs,text).
- our position paper on “AI and Future challenges” article in Annals des Mines journal offering a popularized presentation of AI challenges for the future.
Another relevant research topic is decision making methods, in particular: mathematical programming (mixed integer linear and nonlinear programming), combinatorial optimisation, global optimisation, graph theory. We are interested both in methodology and applications, with a special focus on applications in energy optimisation and computational geometry.
Visit our selected publications page.
mvazirg ~ lix.polytechnique.fr