Big Data Problem

Big data analysis faces challenges in efficiently processing, interpreting, and utilizing massive datasets. Current research focuses on developing robust and scalable algorithms, including distributed and online learning methods, and improving the explainability of machine learning models used in data-driven optimization. This is crucial for addressing limitations in existing techniques, particularly for applications like anomaly detection and data science code generation, ultimately enabling more reliable and trustworthy insights across diverse fields such as healthcare and operations management. The development of new benchmarks and evaluation metrics is also a key area of focus to ensure the reliability and generalizability of new methods.