Date: Thursday, October 9, 2025
Time: 1:00–2:00 p.m. Eastern time
Location: On Zoom
Join us for “Physicists on Quantum ML” at the next NISS-CANSSI Collaborative Data Science Webinar.
The National Institute of Statistical Sciences (NISS) and the Canadian Statistical Sciences Institute (CANSSI) are pleased to present a collaborative webinar exploring the emerging field of Quantum Machine Learning (QML). This session will bring together physicists at the forefront of quantum research to share how quantum principles are reshaping machine learning and data science.
Participants will gain insight into the foundations of QML, its potential to revolutionize data-driven discovery, and the unique challenges of bridging physics, computation, and statistics. Designed for a broad audience of statisticians, data scientists, and researchers, this event will highlight both theoretical perspectives and practical applications, offering a unique opportunity to learn directly from experts working at the intersection of quantum science and machine learning.
Dr. Martin T. Wells is a prominent figure at Cornell University, specializing in statistical sciences. He has been with the Cornell faculty since 1987 and holds the title of Charles A. Alexander Professor of Statistical Sciences. Dr. Wells is also a professor of social statistics, biostatistics, and epidemiology at Weill Medical School, and an elected member of the Cornell Law School faculty. His research interests span applied and theoretical statistics, with a focus on inference questions in various fields such as credit risk, economic damages, and legal studies. Dr. Wells has published numerous articles in leading statistical journals and has served on high-level national statistical committees. He is also the Editor in Chief of ASA-SIAM Series on Statistics and Applied Probability and has contributed to the development of statistical methodologies for various scientific disciplines. See profile.
Stay tuned for the second speaker’s name and bio.
Emily Casleton is a statistician in the statistical sciences group at Los Alamos National Laboratory (LANL), and was recruited to LANL as a summer student at the 2012 Conference on Data Analysis (CoDA). She joined the Lab as a postdoc in 2014 after earning her PhD in Statistics from Iowa State University. Since converting to staff in 2015, Emily has routinely collaborated with seismologists, nuclear engineers, physicists, geologists, chemists, and computer scientists on a wide variety of cool data-driven projects. Most recently, her research focus has been on testing and evaluating large AI models. She holds a BS in Mathematics, Political Science from Washington & Jefferson College, 2003; an MS in Statistics from West Virginia University, 2006; and a PhD in Statistics from Iowa State University.
In an era where data transcends traditional boundaries, fostering interdisciplinary collaboration has never been more crucial. Together with the National Institute of Statistical Sciences (NISS), we are proud to present the NISS-CANSSI Collaborative Data Science webinar series dedicated to showcasing data scientists and domain scientists from diverse scientific fields who collaborate to advance science. This initiative celebrates the power of collaboration, demonstrating how the fusion of data science with various disciplines can drive innovation, solve complex problems, and push the frontiers of knowledge beyond the realm of statistics.
Each session features two speakers: a data scientist and a subject matter expert from another domain who have successfully partnered to achieve impactful results. Through their shared experiences and insights, attendees gain a deeper understanding of the collaborative processes that bridge gaps between different scientific landscapes. These seminars not only highlight successful partnerships but also provide a platform for exchanging ideas, methodologies, and best practices that inspire new collaborations.