This track focuses on the latest methodologies in statistical inference, emphasizing both theoretical developments and practical applications. Researchers are encouraged to present innovative approaches that enhance the understanding of uncertainty in data analysis.

This session will explore the statistical principles underpinning various machine learning algorithms, including regression, classification, and clustering techniques. Contributions that bridge the gap between statistical theory and machine learning practice are particularly welcome.

This track is dedicated to the application of Bayesian methods in data science, highlighting their advantages in handling uncertainty and incorporating prior knowledge. Papers that demonstrate innovative Bayesian approaches in real-world scenarios are encouraged.

This session will delve into the development and evaluation of predictive modeling techniques across various domains. Participants are invited to share their insights on model selection, validation, and performance metrics.

This track addresses the challenges and solutions in computational statistics when dealing with big data. Contributions that showcase efficient algorithms and computational techniques for large-scale data analysis are highly sought after.

This session will examine the statistical underpinnings of neural networks, focusing on their interpretability and performance evaluation. Researchers are encouraged to present studies that integrate statistical theory with neural network applications.