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Michalis Vlachos

Coordonnées

Professeur ordinaire
Département des systèmes d'information


Contact
Michalis.Vlachos@unil.ch
Internef, bureau 135
Tél 021.692.33.00

Adresse postale
Université de Lausanne
Quartier UNIL-Chamberonne
Bâtiment Internef
1015 Lausanne

Enseignements

master Data Mining and Machine Learning
Formation concernée
Maîtrise universitaire ès Sciences en systèmes d'information

Assistants

Ahmad Ajalloeian
Ahmad.Ajalloeian@unil.ch



page personnelle
  James Tyler
JAMES.TYLER@UNIL.CH



page personnelle
 

Publications

23 dernières publications classées par: type de publication  -  année

: Revue avec comité de lecture

2019

Fusco Francesco, Vlachos Michalis, Vasileiadis Vasileios , Wardatzky Kathrin ; Schneider Johannes (2019, Jan). RecoNet: An Interpretable Neural Architecture for Recommender Systems. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence.


Schneider Johannes , Vlachos Michalis (2019). Mass Personalization of Deep Learning.


Vlachos Michalis, Duenner Celestine, Heckel Reinhard , Vassiliadis Vassilios G. , Parnell Thomas P. ; Atasu Kubilay (2019). Addressing Interpretability and Cold-Start in Matrix Factorization for Recommender Systems. IEEE Transactions on Knowledge and Data Engineering.


2018

Atasu K., Parnell T., Dunner C., Sifalakis M., Pozidis H., Vasileiadis V. et al. (2018). Linear-complexity relaxed word Mover’s distance with GPU acceleration. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, 2018-January, 889-896.


Vlachos Michail (2018). Indexing and Similarity Search. Encyclopedia of Database Systems.


Zouzias Anastasios , Vlachos Michalis (2018). Very-Low Random Projection Maps.


2017

Atasu K., Parnell T., Dunner C., Vlachos M. ; Pozidis H. (2017). High-Performance Recommender System Training Using Co-Clustering on CPU/GPU Clusters. Proceedings of the International Conference on Parallel Processing, 372-381.


Heckel Reinhard , Vlachos Michalis (2017). Private and Right-Protected Big Data Publication: An Analysis. Proceedings of the 2017 SIAM International Conference on Data Mining (pp. 660-668). Society for Industrial and Applied Mathematics.


2016

Vlachos M., Vassiliadis V.G., Heckel R. ; Labbi A. (2016). Toward interpretable predictive models in B2B recommender systems. IBM Journal of Research and Development, 60.


2015

Vlachos Michalis, Freris Nikolaos M. ; Kyrillidis Anastasios (2015). Compressive mining: fast and optimal data mining in the compressed domain. The VLDB Journal, 24, 1-24.


Vlachos Michalis, Schneider Johannes ; Vassiliadis Vassilios G. (2015). On Data Publishing with Clustering Preservation. ACM Trans. Knowl. Discov. Data, 9, 1-30.


2014

Schneider J. , Vlachos M. (2014). On randomly projected hierarchical clustering with guarantees. SIAM International Conference on Data Mining 2014, SDM 2014, 1, 407-415.


Schneider Johannes, Bogojeska Jasmina ; Vlachos Michail (2014, Jan). Solving Linear SVMs with Multiple 1D Projections. Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM 14. Association for Computing Machinery (ACM).


Zoumpoulis Spyros I., Vlachos Michail, Freris Nikolaos M. ; Lucchese Claudio (2014). Right-Protected Data Publishing with Provable Distance-Based Mining. IEEE Trans. Knowl. Data Eng., 26, 2014-2028.


2013

Schneider Johannes , Vlachos Michail (2013, Jan). Fast parameterless density-based clustering via random projections. Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM 13. ACM Press.


2012

Freris N.M., Vlachos M. ; Turaga D.S. (2012). Cluster-aware compression with provable k-means preservation. Proceedings of the 12th SIAM International Conference on Data Mining, SDM 2012, 82-93.


Vlachos Michail, Wieczorek Aleksander ; Schneider Johannes (2012, Jan). Right-protected data publishing with hierarchical clustering preservation. Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM 12. Association for Computing Machinery (ACM).


2010

Lucchese Claudio, Vlachos Michail, Rajan Deepak ; Yu Philip S. (2010). Rights protection of trajectory datasets with nearest-neighbor preservation. The VLDB Journal, 19, 531-556.


Svonava Daniel , Vlachos Michail (2010, Déc). Visualizing Graphs Using Minimum Spanning Dendrograms. 2010 IEEE International Conference on Data Mining. Institute of Electrical & Electronics Engineers (IEEE).


Vlachos Michail, Kozat Suleyman S. ; Yu Philip S. (2010). Optimal distance bounds for fast search on compressed time-series query logs. ACM Trans. Web, 4, 1-28.


2009

Ratanamahatana Chotirat Ann, Lin Jessica, Gunopulos Dimitrios, Keogh Eamonn, Vlachos Michail ; Das Gautam (2009). Mining Time Series Data. Data Mining and Knowledge Discovery Handbook (pp. 1049-1077). Springer Science $\mathplus$ Business Media.


2008

Vlachos Michalis, Anagnostopoulos Aris, Verscheure Olivier ; Yu Philip S. (2008). Online pairing of VoIP conversations. The VLDB Journal, 18, 77-98.


2007

Vlachos Michail, Wu Kun-Lung, Chen Shyh-Kwei ; Yu Philip S. (2007). Correlating burst events on streaming stock market data. Data Mining and Knowledge Discovery, 16, 109-133.


Curriculum

Expériences professionnelles

Research Staff Member: Team Lead on Enterprise Recommender Systems
IBM Research - Zurich

Research Staff Member
IBM Research - New York, USA
Research on time-series analytics, data mining and machine learning

Visiting Researcher
Microsoft Research, Machine Learning and Applied Statistics

Prix et distinctions scientifiques

ERC Starting Grant
"Exact Mining from InExact Data"
Année : 2011

Récipiendaire : Michalis Vlachos


Best Paper Runner Up: SIAM Data Mining International Conference
For work on scalable density-based clustering.
Année : 2014


Outstanding Technical Achievement Award: "Efficient Indexing and Searching on Big Data", IBM
Année : 2015


Best Paper Award, IEEE International Conference in Data Engineering
"Best of ICDE 2017" paper, IEEE International Conference in Data Engineering. For research work on interpretable recommender systems.
Année : 2017


Distinguished Alumnus Award, Informatics Dept., Aristotle University Thessaloniki
Année : 2017


Research Division Award: "Watson Company Analyzer (WCA)", IBM
Année : 2017


Member: IBM Academy of Technology
Année : 2018


IBM Corporate Award
"Data-Driven IBM Sales Transformation"
Année : 2018


Mots-clés

  • data science, machine learning, recommender systems, information retrieval

 
 
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