Conference: ICSA 2025 Applied Statistics Symposium
- Institution
- University of Connecticut, CT
- Principal investigator
- Xiaojing Wang
- Funding
- $21K
- Duration
- 2025–2026
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Abstract
This award will play a pivotal role in promoting advancing the field of statistics by supporting the participation of graduate students and early-career researchers in the International Chinese Statistical Association (ICSA) 2025 Applied Statistics Symposium, which will be held in Storrs, Connecticut from June 15 to June 18, 2025. With the theme of “Insights into Complexity: Empowering Policy through Statistical Learning and Data Analytics,” the symposium will highlight the role of statistics and data science in addressing modern policy challenges. The symposium aims to bring together a global community to explore how statistical learning and data analytics can address complex decision-making in various disciplines. The symposium will unite researchers, practitioners, and students passionate about advancing statistical methods, biostatistics, data science, artificial intelligence, and their applications in various fields and foster new directions for statistical inference, facilitating discoveries from seemingly messy data to inform real-world impactful decisions. The ICSA 2025 Applied Statistics Symposium will feature an engaging format, including plenary talks, invited sessions, and contributed posters. We prioritize broadening participation across all aspects of the conference, with a special focus on encouraging the active participation of early-career researchers (those who have earned their doctorates within the past five years) in invited sessions. For students, ICSA 2025 symposium offers numerous opportunities, including a student paper competition and the chance to present posters. The symposium's website, accessible at https://symposium2025.icsa.org/, serves as a hub for seamless communication and resource sharing among participants. 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.
Research team
- Xiaojing WangPI
- Ming-Hui Chen
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