Curriculum Vitae HAYTHAM ELGHAZEL. Education :

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1 Curriculum Vitae HAYTHAM ELGHAZEL Address: Université Lyon 1, UFR Informatique 43 bd du 11 novembre 1918, Villeurbanne Cedex, e Mail: elghazel@bat710.univ lyon1.fr Web: lyon1.fr/~elghazel/ Qualifications: BSc, MSc, Dr. Birth date : 27 /07/ 1980 Nationality : Tunisian Tel : (+33) Education : September 2008 February June 2008 I'm Assistant Lecturer and Researcher in IUT A of Lyon. I was a postdoc researcher at the University of Hokkaido (Meme Media Laboratory) in JAPAN, where I am working with Yuzuru TANAKA and Tetsuya Yshida. The post docoratal position was funded by the JSPS (Japanese Society for the Promotion of Science), from February 1st to May 31st, Research interests: "Machine Learning for large complex databases and sequential data analysis" Ph.D. thesis in computer science (Data mining and Machine Learning) at the Claude Bernard University Lyon 1 (UCBL) (France), under the direction of Alain DUSSAUCHOY and Véronique DESLANDRES, defended December 17th, Ph D thesis: Clustering and Prediction of Complex Data: Application to Healthcare Stays and Pathways. Research interests: My work is based on a double competence in Data Mining and Graph Theory. This work consists of the coupling between Data Mining (data clustering, time series clustering and prediction) and graph theoretical (graph coloring) approaches. Abstract: Recent years have seen the development of data mining techniques in various application areas, with the purpose of analyzing large and complex data. The medical field is one of these areas where available data are numerous and described using various attributes, classical (like patient age and sex) or symbolic (like medical treatments and diagnosis). Data mining generally includes either descriptive techniques (which provide an attractive mechanism to automatically find the hidden structure of large data sets), or predictive techniques (able to unearth hidden knowledge from datasets). In this work, the problem of clustering and prediction of heterogeneous data is tackled by a twostage proposal. The first one concerns a new clustering approach which is based on a graph coloring method, named b coloring. An extension of this approach which concerns incremental clustering has been added at the same time. It consists in updating clusters as new data are added to the dataset without having to perform complete re clustering. The second proposal concerns sequential data analysis and provides a new framework for clustering sequential data based on a hybrid model that uses the previous clustering approach and the Mixture Markov chain models. This method allows building a partition of the sequential dataset into cohesive and easily interpretable clusters, as well as it is

2 able to predict the evolution of sequences from one cluster. Both proposals have then been applied to healthcare data given from the PMSI program (French hospital information system), in order to assist medical professionals in their decision process. In the first step, the b coloring clustering algorithm has been investigated to provide a new typology of hospital stays as an alternative to the DRGs classification (Diagnosis Related Groups). In a second step, we defined a typology of clinical pathways and are then able to predict possible features of future paths when a new patient arrives at the clinical center. The overall framework provides a decision aid system for assisting medical professionals in the planning and management of clinical process. This work is partly inspired by the DOMECAD project which is supported by the Rhône Alpes area council. In addition, I was involved in the European project Tarchna based on the classification of documents for archaeologists. The objective consists of making a prototype which allows to define groups of similar documents among the whole of documents in order to accelerate the browsing task. As results, we have proposed a Keyword based SYstem for DOcument (text and image) Clustering and Retrieval. KSyDoC offers an interesting panel of tools including keywords extraction and a variety of classification methods for accelerating browsing. In order to improving the clustering accuracy, KSyDoC also offers a graphical user interface. It allows user to interact with a clustering task to incorporate Background knowledge (before clustering) and user feedback (after clustering) in the form of must link and cannot link constraints. July 2004 M.Sc. in Computer Science, INSA of Lyon. Master thesis: Textual Approach for Content based image classification and retrieval. Abstract: The Content Based Image Retrieval and many retrieval systems were conceived generally to indexing the image bases using low level features (colour, texture and shape). The search engines then calculate the distances between descriptors to find, in the base of images, the most similar to the key image. In my Master training, I have tried to profit by the benefits of the traditional methods of retrieval for textual information to adjust them with the problems of retrieval in the bases of images. On the first hand, I have made a turn of the existing textual methods. After that, I have proposed a scheme of adaptation including the choice of the descriptors, their extraction, then their use with a method considered to be most adequate (Latent Semantic Analysis and Latent Semantic Indexing). July 2003 Engineer in Computer Science, National School of Engineering, Sfax, Tunisia. Best student Award, JULY 2003, (16.04/20). Engineer thesis: Analyze and identifications of bacteria in the microscopic color images of biofilms. Abstract: The quality control of the foodstuffs seems to appear more and more like one of the new challenges of our company. For this reason, the use of the image as tool of analysis and decision in quality control in the food industry has developed considerably these last years. In the food industry, the recrudescence of the food poisonings could be due to

3 the micro organisms that remain on badly cleaned equipment. The bacteria which are able to resist to the various treatments are bacteria that developed a biofilm. Significant problems lie in the detection and the identification of this type of bacteria. My work have consisted in developing a method of segmentation adapted to the analysis of microscopic color images of biofilms and based on the forms and color characteristics June 1998 Two year preparation for the National exam allowing access to Institutes of higher education in science and technology and biology (Speciality Math Sup, Math Spe) at the Sfax Preparatory Engineering Institute (SPEI), Tunisia. Baccalaureat (high school diploma), SFAX, Tunisia. Research Interests: Data Mining techniques (clustering, classification, etc.), Graph theory approaches (graph coloring). Mining and Learning with graph (the coupling between Data Mining and graph theoretical Approaches), Unsupervised incremental approach, Statistical and Probabilistic approach for time series clustering and prediction. Publications Ph.D. Thesis Classification et Prévision des Données Hétérogènes : Application aux Trajectoires et Séjours Hospitaliers, H. Elghazel, Mémoire de thèse de Doctorat, Université Claude Bernard Lyon 1, Décembre, 2007, 169 pages. International Journals (2) Clinical Pathway Clustering Using Graph b coloring and Markov Model, H. Elghazel, V. Deslandres, K. KALLEL and A. Dussauchoy, International Journal of Biomedical Engineering and Technology Special Issue on "Warehousing and Mining Complex Data: Applications to Biology, Medicine, Behavior, Health and Environment", (to appear). A Graph b coloring Framework for Data Clustering, H. Elghazel, H. Kheddouci, V. Deslandres, and A. Dussauchoy, Journal of Mathematical Modelling and Algorithms", (to appear). International Conferences (12) An Integrated Graph and Probability Based Clustering Framework for Sequential Data, H. Elghazel, T. Yoshida, M S. Hacid. In the 11th International Conference on Discovery Science (DS 2008), Budapest, Hungary, October 13 16, Lecture Notes in Artificial Intelligence Nº5255 Springer Verlag ISBN , pages Clinical Pathway Analysis Using Graph Based Approach and Markov Models, H. Elghazel, V. Deslandres, K. KALLEL and A. Dussauchoy, In Second IEEE/ACM In Second IEEE/ACM International Conference on Digital Information

4 Management (ICDIM07), Lyon, France, October 28 31, ISBN: , pages A Partially Dynamic Clustering Algorithm for Data Insertion and Removal, H. Elghazel, H. Kheddouci, V. Deslandres and A. Dussauchoy, In the 10th International Conference on Discovery Science (DS 2007), Sendai, Japan, October 1 4, Lecture Notes in Artificial Intelligence N 4755 Springer Verlag ISBN: , pages Conference rate : 30% Clustering With Constraints Using Graph Based Approach, H. Elghazel, K. Benabdeslem and A. Dussauchoy, In the 5th International Workshop on Mining and Learning with Graphs, Florence, Italy. August 1 3, A Graph based Approach for Dynamic Clustering, H. Elghazel, H. Kheddouci, V. Deslandres and A. Dussauchoy, In the 5th International Workshop on Mining and Learning with Graphs, Florence, Italy. August 1 3, A decision support system for clinical pathways analysis, V. Deslandres, H. Elghazel, K. Kallel and A. Dussauchoy, In the 33rd conference on Operational Research Applied to Health Service (ORAHS 2OO7), Saint Etienne, France, July 15 20, Constrained Graph b coloring based Clustering Approach, H. Elghazel, K. Benabdeslem and A. Dussauchoy, In the 9th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2007), Regensburg, Germany, September 3 7, Lecture Notes in Computer Science N 4654 Springer Verlag ISBN: , pages Conference rate : 29% A New Greedy Algorithm for improving b Coloring Clustering, H. Elghazel, T. Yoshida, V. Deslandres, M S Hacid and A. Dussauchoy, In the 6th IAPR TC 15 Workshop on Graph based Representations in Pattern Recognition (GbR 2007), Alicante, Spain, June 11 13, Lecture Notes in Computer Science N 4538 Springer Verlag ISBN: , pages A New Graph Based Clustering Approach: Application to PMSI Data, H. Elghazel, H. Kheddouci, V. Deslandres, and A. Dussauchoy, In 3rd IEEE International Conference on Services Systems and Services Management (ICSSSM06), Troyes, France, October 25 27, ISBN: , pages A New Clustering Approach for Symbolic Data and its Validation: Application to the Healthcare Data, H. Elghazel, V. Deslandres, M S. Hacid, A. Dussauchoy and H. Kheddouci, In 16th International Symposium on Methodologies for Intelligent Systems (ISMIS 2006), Bari, Italie, September 27 29, Lecture Notes in Computer Science N 4203 Springer Verlag ISBN X, pages Conference rate : 35% Textual Approach for image retrieval (Approches textuelles pour la recherche d images), H. Elghazel, K. Idrissi, A. Baskurt, C. Ben Amar, In the 3rd IEEE International Conference on Sciences of Electronic, Technologies of Information and

5 Telecommunications (SETIT 05), page 124, 6 pages, Sousse, Tunisie, March 27 31, Bacteria Analysis and identifications in the microscopic color images of biofilms (Analyse et Identification de Bactéries dans les Images Microscopiques couleur de Biofilms), H. Elghazel, K. Idrissi, A. Baskurt, C. Ben Amar, In the first International Conference on Signal, System and Design (SCS'04), Monastir, Tunisie, Mars 2004, pp National Conferences (4) Une approche incrémentale de classification non supervisée par b coloration de graphes, H. Elghazel, H. Kheddouci, V. Deslandres, and A. Dussauchoy, In 9ème Conférence Francophone sur l'apprentissage automatique (CAp 2007), Plate forme AFIA, Editions Cépaduès. Grenoble, France, 2 6 Juillet Conference rate : 40% A Re Coloring Method for b coloring based Clustering, T. Yoshida, H. Elghazel, V. Deslandres, M S Hacid and A. Dussauchoy, In the 18th IEICE Data Engineering Workshop in conjunction with the 5th Annual Conference on the Database Society of Japan (DBSJ). Hiroshima, Japan. 28th February 2nd March, (En Japonais). A New Clustering Approach for Symbolic Data: Algorithms and Application to Healthcare Data (Une nouvelle approche de clustering pour les données symboliques: Algorithmes et Application aux Données Médicales), H. Elghazel, M S Hacid, H. Kheddouci and A. Dussauchoy, In 22ème journées de Bases de données Avancées (BDA 2006), Lille, France, Octobre Conference rate : 29% Analyse de données PMSI : Etude des groupes homogènes de malades et proposition d une nouvelle typologie de séjours, H. Elghazel, V. Deslandres, A. Dussauchoy, In 3ème conférence francophone en gestion et ingénierie des systèmes hospitaliers (GISEH 06), Luxembourg, Septembre, Demonstrations (1) KSyDoC: A Keyword based System for DOcument Clustering and Retrieval, M. Azzaoui, J. Legrand and H. Elghazel, In 23ème journées de Bases de données Avancées (BDA 2007), Marseille, France, Octobre Scientific activities Program committee member in conferences The Tenth Maghrebian Conference on Software Engineering and Artificial Intelligence MCSEAI 2008, Oran, Algeria. Reviewer in conferences (After a sollicitation by a program committee member) Journal of Intelligent Information Systems (JIIS). The Ninth International Conference on Discovery Science DS 2006, Barcelona, Spain.

6 Technical Skills Languages and environments C, C++, J2EE (threads, Servlet, EJB component, RMI ). SAS (Statistical Analysis System), Oracle, Oracle Data Mining. Numerical treatments (Matlab, Maple). CORBA framework, Dot Net Visual Basic, SQL GTK/GTK+ Operating Systems Networks Modelling tools Web technologies Linux. Windows NT/9X Network Administration, Network Interconnection, protocols engineering. UML.: HTML, XHTM,CSS, XML, PHP, AJAX, JavaScript Language Skills: English French Arabic Good Good Native