International Conference on Data Science and Statistics is a global academic and professional forum that brings together researchers, practitioners, and industry experts to share recent advances, methodologies, and applications in data science and statistical analysis. The conference covers a wide range of topics, including mathematic fundaments of data science, machine learning, big data analytics, artificial intelligence, probability, statistical modelling, data mining, and applied statistics across diverse domains. It aims to foster interdisciplinary collaboration, promote the exchange of innovative ideas, and address emerging challenges in data-driven decision-making through keynote speeches, technical sessions, tutorial, and panel discussions. This edition includes two scientific events in 2026: The 2th edition of the international conference on Applied Mathematics and Data Sciences (ICAMDS), and the 31rd Annual meeting of the 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