The field of streaming algorithms and system identification is rapidly advancing, with a focus on developing efficient and robust methods for processing large datasets. Researchers are exploring new techniques for maximum coverage problems, system identification under heavy-tailed noise, and moment estimation in streaming models. These innovations have the potential to significantly improve the accuracy and efficiency of various applications, including risk management and linear system solving. Notably, the development of algorithms with tight bounds and improved sample complexity is enabling the handling of more complex and noisy data. Overall, the field is moving towards more robust and efficient methods for processing and analyzing large datasets. Noteworthy papers include: Maximum Coverage in Turnstile Streams with Applications to Fingerprinting Measures, which presents the first algorithm for maximum coverage in the turnstile streaming model with polylog update time. Boosting-Enabled Robust System Identification of Partially Observed LTI Systems Under Heavy-Tailed Noise, which leverages tools from robust statistics to propose a novel system identification algorithm that achieves sample complexity bounds nearly matching those derived under sub-Gaussian noise.