Call for Research Papers

Submissions are welcome

with the following topics to cover a broad spectrum within Data Science, ensuring a balanced representation across different areas of expertise.

Statistical Methods and Traditional Data Analysis 

• Time Series Analysis
  • Multivariate Time Series: Anomaly detection, Generative models
  • Time Series Forecasting and Prediction techniques
• Exploratory Data Analysis (EDA)
  • Effective data visualization strategies
  • Descriptive statistics for data interpretation
• Advanced Statistical Modeling
  • Regression and Classification methods
  • Bayesian statistics applications

Machine Learning and AI

• Core Machine Learning Methods
  • Supervised and Unsupervised Learning algorithms
  • Transfer Learning, Incremental and Adaptive Learning
• Specialized AI Techniques
  • Reinforcement Learning and Control Theory
  • Topological Data Analysis and Topological Machine Learning
  • Alternative Machine Learning Models beyond neural networks
• Imaging and Computer Vision
  • Deep Learning applications
• Representation Learning and Feature Engineering
  • Feature extraction and representation methods

Security, Privacy, and Ethical Considerations

• Security and Privacy for AI
  • Adversarial AI and Countermeasures
  • Explainable AI for Security
  • Deep Fake Detection across various data types
  • Robustness of AI Methods against disruptions and attacks
• Ethical and Legislative Aspects
  • Ethical issues in AI development and application
  • Analysis of the EU AI Act and its implications

Data Science in Industry and Production

• MLOps and Data Engineering
  • MLOps strategies for industrial and Operational Technology (OT) contexts
  • Building efficient data pipelines and workflow automation
• Co-Simulation and Interdisciplinary Applications
  • Integrating reinforcement learning with co-simulation techniques
• Dynamic Systems and Control
  • Modeling and controlling dynamic systems using machine learning

Emerging and Interdisciplinary Topics

• Semantic Technologies
  • Knowledge Graphs and Semantic Inference
  • Semantic Information Modeling
• Topical Innovations
  • Advanced Topological Data Analysis methods
  • Incremental and Adaptive Learning techniques
• Collaborative and Open Research
  • Promoting open data and collaborative research efforts
  • Encouraging interdisciplinary projects across multiple domains

Data Management and Infrastructure

• Data Governance and Sovereignty
  • Gaia-X – Data Spaces and Data Sovereignty
  • Ensuring data sovereignty and compliance with data protection regulations
• Public Data Science and Open Data
  • Open Data Initiatives
  • Management of public data by governmental bodies
• Data-driven Business Models
  • Leveraging data as a central resource in business
  • Data-driven decision-making and innovation case studies
• Data Privacy and Security
  • Privacy and Security for Federated Learning
  • General data security practices across platforms