(Recording coming soon!)
Date: Tuesday, October 28, 2025, 12pm-1pm ET/9-10am PT
The webinar, moderated by Dr. Bei Jiang, Associate Professor at the University of Alberta, introduced the COPSS Leadership Academy and its webinar series with NISS, which aims to recognize and share insights from leaders in statistics and data science. Featuring panelists Dr. Annie Chu and Dr. Qian Xi Tsai, the event highlighted their significant contributions to statistics and AI through research in statistical learning and biomarker evaluation. Both panelists shared their academic journeys and current research, discussing experiences with data collection, clinical collaborations, and AI applications. The conversation emphasized how statistics can enhance AI’s reliability and trustworthiness while underscoring the evolving role of statisticians in AI-driven research—focusing on curiosity, domain expertise, and collaboration, as well as the need to adapt education to prepare students for the AI era.
Academic Journey and Research Impact
Dr. Annie Qu shared her academic journey from her PhD at Penn State to her current role at UCSB, highlighting key experiences including her work at Oregon State and UIUC, where she developed a data science course and collaborated on research projects involving imaging data and DNA methylation. She discussed her challenges in accessing clinical data and her eventual success in collaborating with CHOC Children's Hospital for mental health research. Annie concluded by describing her current work at UCSB, including the establishment of a new statistics foundation for AI center and the development of an Apple app for tracking daily emotions and well-being.
Statisticians' Role in Real-World Data
Dr. Tianxi Cai shared her journey from biostatistics at Harvard to working with EHR data, highlighting how her experiences in Botswana and collaborations with clinical teams transformed her understanding of public health and data analysis. She emphasized the importance of statisticians being proactive in data management and working closely with teams to address clinical questions, particularly during the pandemic when she led a consortium studying COVID across 300 hospitals in seven countries. Tianxi concluded that statisticians should focus on understanding and solving real-world problems rather than just analyzing clean data, and she admired Annie's approach to data collection and AI applications in handling complex data.
AI's Impact on Statistical Practice
The statisticians discussed how the field has evolved with the rise of AI and complex data, emphasizing the importance of embracing a broader definition of statistics that focuses on data analysis and interpretation rather than adhering to rigid methodologies. Annie and Tianxi highlighted the need for statisticians to balance theoretical understanding with practical problem-solving, encouraging curiosity and collaboration across disciplines. They also discussed the role of statisticians in data collection and processing, suggesting that statisticians contribute to design, quantification, and causality at every stage. The conversation concluded with a discussion on how to adapt education and curriculum to prepare students for the new challenges posed by the AI era.
Statisticians' Role in AI Research
The panel discussed the evolving role of statisticians in AI-driven research, emphasizing the importance of curiosity, domain expertise, and collaboration. Annie and Tianxi highlighted that statisticians should view themselves as data scientists, focusing on solving scientific problems rather than limiting themselves to traditional statistical roles. They discussed the need to adapt curricula to prepare students for the AI era, while also leveraging AI tools to enhance research capabilities. The panelists agreed that statisticians bring unique value through their ability to handle complex, noisy data and develop tailored models, which AI systems currently struggle with.
Thank You and Acknowledgements
The National Institute of Statistical Sciences (NISS) and the Committee of Presidents of Statistical Societies (COPSS) extend their sincere thanks to Dr. Bei Jiang for skillfully moderating this engaging and thought-provoking discussion. We also express our deep appreciation to Dr. Annie Qu and Dr. Tianxi Cai for sharing their insights, research experiences, and leadership perspectives on how statisticians can guide the responsible advancement of AI.
Special thanks go to the COPSS Leadership Academy for its ongoing collaboration with NISS in organizing this webinar series, which continues to highlight the achievements and influence of leaders across the statistical sciences. Finally, we thank all attendees for their participation and thoughtful questions, which enriched the dialogue and reinforced the importance of statisticians’ voices in shaping the future of AI and data science.