Conference: The 9th International Workshop in Sequential Methodologies

The 9th International Workshop in Sequential Methodologies (IWSM-2026) will be held on June 1–4, 2026, at American University in Washington, DC. This conference will bring together researchers, practitioners, and students to discuss how data collected over time can be used to make better and faster decisions in areas such as healthcare, finance, security, and artificial intelligence. Sequential methods enable timely decision-making by allowing analysts to evaluate data as they are collected and to determine when sufficient information has been gathered, rather than relying on a fixed, pre-determined sample size. This flexibility can significantly reduce the cost and duration of clinical trials and increase efficiency in healthcare systems. These methods also play a key role in applications such as cybersecurity, threat detection, and industrial quality control, where rapid response to emerging patterns is critical. As modern technologies generate vast streams of real-time data, there is a growing need for methods that can adapt and respond quickly. The workshop will feature four plenary lectures, 43 invited sessions, as well as contributed and poster presentations, totaling approximately 150 presentations across four days, with more than 180 anticipated participants. The goal of this project is to support students and early-career researchers by providing opportunities to present their work, engage with leading experts, and develop professional networks. By fostering collaboration across disciplines and countries, the workshop will help advance data-driven solutions to important societal challenges and contribute to workforce development in the mathematical sciences. The conference focuses on recent advances in sequential methodologies, which are statistical and computational techniques for analyzing data observed over time or collected through adaptive, data-dependent sampling schemes. Topics include sequential testing, change-point detection, clinical trials, stochastic process control, and optimal stopping, along with modern developments in machine learning, artificial intelligence, and data stream mining. These approaches emphasize dynamic decision-making, where sampling, inference, and stopping rules are updated based on incoming data to optimize efficiency and performance. Particular emphasis is placed on methodological innovation and applications in biostatistics, cybersecurity, and AI, reflecting evolving research directions in these fields. The program includes invited and contributed sessions, poster presentations, and special sessions recognizing influential contributions to sequential analysis. The workshop will facilitate the exchange of new theoretical results, computational methods, and applied insights in sequential methodologies, while promoting interdisciplinary collaboration and the development of data-driven methodologies for real-time decision-making. Support will be provided to students and early-career researchers to encourage broad participation and dissemination of research outcomes. Updated information about the conference is on its website, https://www.american.edu/cas/iwsm2026/. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2553585 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Michael Baron | Institution: American University, WASHINGTON, DC | Award Amount: $30,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2553585 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2553585.html