7th International Conference on Data Science and Machine Learning (DSML 2026)

7th International Conference on Data Science and Machine Learning (DSML 2026)

August 22 ~ 23, 2026, Dubai, UAE

https://bibc2026.org/dsml/index

Hybrid -- Registered authors can present their work

Scope & Topics

7th International Conference on Data Science and Machine Learning (DSML 2026) ) serves as a premier global forum for presenting innovative ideas, cutting edge research, practical developments and emerging trends in the rapidly evolving fields of Data Science and Machine Learning. As data driven technologies continue to transform science, industry and society, DSML 2026 brings together leading researchers, practitioners and industry experts to exchange knowledge, discuss challenges and explore breakthroughs shaping the future of intelligent systems.

The conference aims to foster collaboration between academia and industry by providing a platform to share novel methodologies, theoretical advancements, applied research and real world deployments across the full spectrum of data science and machine learning. DSML 2026 encourages contributions that push the boundaries of what is possible—from foundational algorithms and scalable systems to generative AI, autonomous agents and domain specific applications that address pressing global challenges.

Authors are invited to submit high quality articles describing original research results, innovative projects, comprehensive surveys and industrial case studies that demonstrate significant advances in the field. Submissions may address any of the conference tracks, including but not limited to:

Topics of interest include, but are not limited to, the following:

Machine Learning Foundations & Theory

• Learning theory, generalization, optimization
• Probabilistic modeling & Bayesian methods
• Self supervised, semi supervised & weak supervision
• Meta learning, few shot learning
• Continual & lifelong learning
• Federated & distributed learning
• Trustworthy ML (robustness, fairness, explainability)
• Data centric AI & dataset optimization

Deep Learning, Architectures & Efficient ML

• Transformers & attention mechanisms
• Graph neural networks & graph transformers
• Efficient ML (quantization, pruning, distillation)
• Neural architecture search (NAS)
• Physics informed neural networks (PINNs)
• Multimodal deep learning
• Long context models & efficient attention

Foundation Models, Generative AI & Alignment

• Large language models (LLMs)
• Multimodal foundation models
• Diffusion models & generative modeling
• Retrieval augmented generation (RAG)
• Synthetic data generation & governance
• AI safety, alignment, red teaming
• Scalable oversight & constitutional AI

Agentic AI, Autonomous Systems & Planning

• Autonomous LLM agents
• Multi agent LLM systems
• Tool using agents & API calling models
• Planning + LLM hybrid systems
• Agent evaluation, safety & emergent behaviors

Data Mining, Knowledge Discovery & Causal Inference

• Large scale & high dimensional data mining
• Graph, network & social network mining
• Temporal, spatial & spatio temporal mining
• Streaming & real time analytics
• Text, web, multimedia & video mining
• Causal discovery & causal inference
• Knowledge graphs & reasoning
• Automated data cleaning & feature engineering
• Interactive data exploration & visualization

Data Engineering, Databases & Scalable Systems

• Cloud native data architectures (lakes, lakehouses)
• Modern database systems & query optimization
• Scalable data management & storage
• Stream processing & real time systems
• Distributed computing (Spark, Flink, Ray)
• Data integration, governance & lineage
• Data security, privacy & compliance

MLOps, Evaluation, Reproducibility & ML Systems

• ML lifecycle management & deployment
• Experiment tracking & model versioning
• Benchmarking & evaluation
• Reproducible ML pipelines
• Responsible dataset creation
• ML compilers & hardware acceleration

Reinforcement Learning, Decision Making & Control

• RL theory & algorithms
• Offline & batch RL
• Multi agent RL
• RLHF (reinforcement learning from human feedback)
• RL for robotics, control & autonomous systems

AI for Code, Embodied AI & Intelligent Agents

• Code LLMs & program synthesis
• Automated debugging & program repair
• Formal verification + LLMs
• Embodied LLMs
• Vision language action (VLA) models
• Robotic manipulation with foundation models
• Simulation to real transfer

AI Applications, Scientific Discovery & Societal Impact

• Bioinformatics, genomics & computational biology
• Healthcare AI & medical imaging
• Finance, risk modeling & algorithmic trading
• Climate science, sustainability & environmental modeling
• Smart cities, IoT & edge intelligence
• Cybersecurity & threat intelligence
• Social computing & behavioral modeling
• Geospatial AI & remote sensing
• AI for scientific discovery (physics, chemistry, materials, mathematics)
• Digital twins & AI driven simulation
• Human AI interaction & co creative AI
• Societal, cultural & economic impacts of AI

Paper Submission

Authors are invited to submit papers through the conference Submission System by July 25, 2026 . Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedings in Computer Science & Information Technology (CS & IT) series (Confirmed).

Selected papers from DSML 2026, after further revisions, will be published in the special issues of the following journals

·        Machine Learning and Applications: An International Journal (MLAIJ)

·        International Journal of Data Mining & Knowledge Management Process ( IJDKP )

·        International Journal of Database Management System (IJDMS)

·        International Journal of Web & Semantic Technology (IJWesT)

Important Dates

·        Submission Deadline : July 25, 2026

·        Authors Notification : August 15, 2026

·        Registration & Camera-Ready Paper Due : August 17, 2026

Contact Us

Here's where you can reach us : [email protected] or [email protected]