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

Scope & Topics

7^th 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

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

August 22 ~ 23, 2026, Dubai, UAE

Scope & Topics

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

Paper Submission

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