Ashofteh, A., Carvalho, R., & Campos, P. (2026). Designing LLM Agents for Output Checking in Research Data Centers: Disclosure Risk and Output Validation. In 2026 IEEE 50th Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 189-195). (Proceedings of the Annual Computer Software and Applications Conference). IEEE Computer Society. https://doi.org/10.1109/COMPSAC69091.2026.00035

Diachkov, D., & Ashofteh, A. (2026). High-Frequency Early Warning of Systemic Financial Stress in Europe Using Financial and Non-Financial Data with Machine Learning. In 2026 IEEE 50th Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 2023-2028). (Proceedings of the Annual Computer Software and Applications Conference). IEEE Computer Society. https://doi.org/10.1109/COMPSAC69091.2026.00296

Integrating machine learning (ML) into the official statisticians' toolset is gaining popularity as National Statistical Offices (NSOs) strive to improve their methodologies. This trend poses new challenges and implications for incorporating innovative techniques that ensure the reliability of the official statistical production process. A comprehensive literature review was conducted using Scopus and Web of Science databases to explore the contemporary applications of data science in official statistics. A total of 178 research articles were identified, focusing on areas such as big data, machine learning, and data quality. While the literature review revealed extensive proposals on utilizing alternative data and applying machine learning techniques to support official statistics production, it also identified research gaps in the post-training steps of the machine learning process. Areas requiring further investigation include machine learning operations in a production environment, data quality assurance, and governance.