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.
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).
What's new
July 2023: Prof. Vazirgiannis gave a talk at the International Congress of Basic Science (ICBS23). See more details here
April 2023: Prof. M. Vazirgiannis gave a keynote talk, entitled "GNNs and Graph Generative models for biomedical applications", in the Web Conference 23. See more details here
April 2022: Prof. M. Vazirgiannis was invited to teach the course "Machine Learning with Graphs and Applications" at the 5th INTERNATIONAL SCHOOL ON DEEP LEARNING. See more here
December 2021: Prof. Vazirgiannis gave a talk at the HEC Algorithmic Law and Society Symposium. See more details here
October 2021: Prof. Vazirgiannis gave a keynote talk, entitled "Towards GNN explainability: Random Walk Graph Neural Networks", in the RecSys 21, GReS Workshop on Graph Neural Networks for Recommendation and Search. See more details here
N Xu, G Nikolentzos, M Vazirgiannis, H Boström, Image Keypoint Matching using Graph Neural Networks, International Conference on Complex Networks and Their Applications, arXiv preprint arXiv:2205.14275, 2021
G Nikolentzos, G Siglidis, M Vazirgiannis, Graph Kernels: A Survey, Journal of Artificial Intelligence Research, arXiv preprint arXiv:1904.12218, 2021
G Dasoulas, G Nikolentzos, K Seaman, A Virmaux, M Vazirgiannis, Ego-based entropy measures for structural representations on graphs, ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, arXiv preprint arXiv:2102.08735, 2021