I am an Associate Professor (Maître de conférences), HDR of Computer Science at TELECOM Nancy at the University of Lorraine. I am member of Capsid research team (Inria – CNRS) at the Lorraine Laboratory of Research in Computer Science and its Applications (LORIA). Previously, I served as a Visiting Researcher at the Montreal Institute for Learning Algorithms (Mila) in Montreal, Canada. I also worked as a postdoctoral researcher at Aalto University in Finland and as a research fellow at the University of Trento in Italy. I received my Ph.D. in Computer Science from the Blaise Pascal University, France in 2013 and my Habilitation to Conduct Research (HDR) from the University of Lorraine, France in 2023. My research interests include Big Data Management and Analytics, Data Mining, machine learning and Bioinformatics.
News!
December 2024 Our paper Knowledge graph representation learning: a comprehensive and experimental overview has been accepted for publication in Computer Science Review (IF = 13.3).
December 2024 We are editing a special issue of Supercomputing journal on Scalable and Deep Graph Learning and Mining . Please submit your paper here
October 2024 Our paper Empirical Analysis of Knowledge Graph Representation Learning Techniques has been accepted at the 13th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2024, Istanbul, Turkey
August 2024 I am giving a talk on « Knowledge graph embedding and link prediction for explainable drug repurposing » at the Meharry School of Applied Computational Sciences, Nashville, TN, USA
July 2024 I am giving a talk on « Ensemble knowledge graph embedding and link prediction for drug repurposing » in the Healthcare & AI reading group at Mila, Montreal, QC, Canada
July 2024 I am visiting the Montreal Institute for Learning Algorithms (Mila) in Montreal, QC, Canada
May 2024 Our workshop proposal « International workshop on Scalable and Deep Graph Learning and Mining » has been officially accepted as part of IEEE Big Data 2024 conference.
December 2023 I have defended my habilitation (HDR) thesis entitled « Knowledge discovery from large biological graphs« at the University of Lorraine [Manuscript] [Slides]
May 2023 Ph.D. Thesis on machine learning and graph-based techniques for the prediction of long-term bacterial community structure [More informations] [PDF]
April 2023 Our paper « Accuracy and Diversity-Aware Multi-Objective Approach for Random Forest Construction« has been accepted for publication in Expert Systems with Applications, Elsevier, 2023!
Febrary 2023 Our paper « Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding » has been accepted for publication in Scientific Reports, Nature ! (IF = 4.99).
May 2022 Our paper « Negative Sampling and Rule Mining for Explainable Link Prediction in Knowledge Graphs » has been accepted for publication in Knowledge Based Systems, Elsevier ! (IF = 8.03).
April 2022 Our paper « A Distributed and Incremental Algorithm for Large-Scale Graph Clustering » has been accepted for publication in Future Generation Computer Systems, Elsevier ! (IF = 7.18).
April 2022 I joined the program committee of the Workshop on Computational Biology (WCB) @ ICML 2022 Join us for another exciting workshop!
March 2022 Our paper « A semi-supervised graph deep neural networks for automatic protein function annotation » has been accepted at the 10th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO) 2022.
I’m co-organizing the ICML 2021 Workshop on Computational Biology (WCB) @ ICML 2021 Join us for another exciting workshop!
July 2021 Our paper « Towards big services: a synergy between service computing and parallel programming » has been accepted for publication in Computing, Springer ! (IF = 2.22).
August 2020 I joined the program committee of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020).
July 2020 Our paper « A comparative study of similarity-based and GNN-based link prediction approaches » has been accepted at the international Workshop on Graph Embedding and Mining (GEM) in conjunction with ECML-PKDD 2020.
March 2020 Our paper « GrAPFI: Predicting Enzymatic Function of Proteins From Domain Similarity Graphs » has been accepted for publication in BMC Bioinformatics (IF = 2.511).
Febrary 2020 Our paper « Graph Based Automatic Protein Function Annotation Improved By Semantic Similarity » has been accepted at the 8th International Work-Conference on Bioinformatics and Biomedical Engineering (IWBBIO) 2020.
September 2019 Our paper « Multiple instance learning for sequence data with across bag dependencies« has been accepted for publication in International Journal of Machine Learning and Cybernetics (IF = 3.844)!
September 2019 Our paper « The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens« has been accepted for publication in Genome Biology (IF = 13.58)!
August 2019 Our paper « Application of Artificial Intelligence to Gastroenterology and Hepatology« has been accepted for publication in Gastroenterology (IF = 22.68)!
May 2019 Our paper « A Structure Based Multiple Instance Learning Approach for Bacterial Ionizing Radiation Resistance Prediction« has been accepted at the 23rd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES) 2019!
I’m co-chairing the third edition of the International workshop on Advances in Mining and Managing Large Evolving Graphs (LEG) in conjunction with ECML-PKDD 2019
March 2019 Our paper « A Data Sampling and Attribute Selection Strategy for Improving Decision Tree Construction« has been accepted for publication in Expert Systems with Applications, Elsevier, 2019!
October 2018 Our paper « Exploiting Complex Protein Domain Networks for Protein Function Annotation » has been accepted for publication at Complex Networks 2018 in Cambridge !
October 2018 Our paper « The Uncertain Cloud: State of the Art and Research Challenges » has been accepted for publication in the International Journal of Approximate Reasoning, Elsevier!
August 2018 We are editing a special issue of Future Generation Computer Systems (FGCS) journal, Elsevier on « Advances in Managing and Mining time varying and highly dynamic graphs at scale ». Please submit your paper here!
July 2018 Our paper « A Comparative Study on Streaming Frameworks for Big Data » has been accepted for publication at LaDAS@VLDB 2018 in Rio De Janeiro !
April 2018 Our paper « An Experimental Survey on Big Data Frameworks » has been accepted for publication in Future Generation Computer Systems, Elsevier !
I’m co-chairing the second edition of the International workshop on Advances in Mining Large-Scale Time Dependent Graphs (TD-LSG) in conjunction with VLDB 2018
I gave a talk on « Dynamic Graph Analysis with BLADYG » at « the GDRI workshop on Web Science » in Rio De Janeiro.
Our paper « An Evolutionary Scheme for Decision Tree Construction » received a Honourable Mention in the 2017 Algerian Paper of the Year Award.
Our paper « An Experimental Survey on Big Data Frameworks » has been accepted at the 10th Extremely Large Databases Conference (XLDB) 2017 for a lightning talk.
Our paper « A Distributed Framework for Large-Scale Time-Dependent Graph Analysis » has been accepted at TDLSTG@ECML-PKDD 2017.
Our paper « BLADYG: A Graph Processing Framework for Large Dynamic Graphs » has been accepted for publication in Big Data Research, Elsevier!
Our paper « MR-SimLab: Scalable Subgraph Selection with Label Similarity for Big Data » has been accepted for publication in Information Systems, Elsevier!
My IEEE MOOC on « Big Data for Smart Cities » is out !
- Institution: IEEEx
- Instructors: Dr. Sabeur Aridhi and Pr. Yannis Velegrakis
- Length: 4 weeks
My Book on Big Data is out !
Title: Bases de données NoSQL et Big Data : Concevoir des bases de données pour le Big Data.
Editeur : Ellipses, ISBN : 2340002613, Décembre 2014.
I’m making the NEWS with my book on Big Data (the article is in french!)
