La Conférence internationale sur la science des données et la statistique ICAMD2SFC’26 est un rendez-vous académique et professionnel de portée mondiale permettant de réunir des chercheurs et des industriels pour présenter les dernières avancées, méthodes et applications, en science des données et en statistique. Le programme couvre un large spectre : fondements mathématiques, apprentissage automatique, traitement des données volumineuses, intelligence artificielle, probabilités, modélisation statistique et leurs applications dans divers domaines. Les rencontres s'articulent autour de conférences plénières, tutoriels et tables rondes visant à stimuler les collaborations interdisciplinaires et à répondre aux enjeux de la prise de décision fondée sur les données.

L’édition 2026 regroupe deux manifestations scientifiques:

• La 2^ème édition de la Conférence internationale sur les mathématiques appliquées et les sciences des données (ICAMDS) et
• Les 31^ème rencontres de la Société Francophone de Classification (SFC). 

Track 1: Statistical learning and data analysis

Track 3: Data Engineering and computer vision

• Statistical inference
• Data analysis and Big data
• Clustering and discrimination
• Analysis of symbolic data
• Analysis of topological data
• Combinatorial structures for classification:
  Trees, graphs, and lattices; seriation
• Meta-heuristics in classification
• Ensemble learning and boosting
• Exploratory analysis and visualization
• Co-clustering models
• High Dimension Data (HDD) and dimension reduction
• Functional data analysis 
• Active learning and reject inference
• Unsupervised, semi-supervised, supervised learning

• Data storage and Data processing
• Data analytics and business intelligence
• Feature engineering
• Real-time data processing and streaming
• Data quality and validation
• Large-scale ETL/ELT pipelines
• Modern data architecture (Data lake,
  Data Warehouse, Lakehouse)
• Generative AI and LLMs (RAG, fine-tuning,
  prompt engineering)
• LMM (Large Multimodal Models) 
• Image processing
• Computer vision (object detection,
  segmentation, tracking, OCR)
• DataOps and MLOps integration
• Cloud-native engineering pipelines

Track 2: Graphs, Neural networks and Deep learning

Track4:  Metaheuristics and AI

• Theoretical analysis of graph algorithms
• Representation-based Reinforcement Learning
• Statistical learning theory related to graphs
• Knowledge graph applications
• Analysis of social media, chemical or biological networks
• Topological Machine Learning
• Graph Diffusion Models and Graph Generative Models
• Learning and Augmented Graphs

• Metaheuristics and optimization models
• Hyper-heuristics
• Nature inspired computing
• Local search, tabu search, simulated annealing,
• Evolutionary algorithms, swarm optimization
• Application to: logistics, transportation,
  networks, scheduling
• Quantum algorithms and machine learning

Track 5:  Stochastic processes and applications

Track6 :  Combinatorics optimization and ML

• Queuing networks
• Stochastic models in finance
• Stochastic process and learning
• Simulation and Quantitative Methods
• Time series and Forecasting
• Econometrics and statistics for financial data
• Chance-constrained programming
• Contextual stochastic programming
• Stochastic Control and Diffusion Processes
• Random walks and Q-learning
• Stochastic Reinforcement Learning

• Robust Optimization and Machine Learning
• Stochastic Optimization under Decision-Dependent
  Uncertainty
• Large-scale machine learning and optimization
• Lasso and elastic net models
• Regularization techniques

Track 7: MCDM and MOO

Track 8: Cloud Computing & Cybersecurity

• Multi-objective optimization, bi-level optimization 
• Goal programming, DEA.
• Game theory and Multicriteria decision making

• Cybersecurity and Privacy
• Internet of Things (IoT) and Smart Systems
• Emerging Technologies and Applications
• Hybrid Cloud strategies