Date: Thursday, October 9, 2025
Time: 1:00–2:00 p.m. Eastern time
Location: On Zoom
Join us for “Working with 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.
Dr. Luca Candelori is a mathematician and currently Director of Research at Qognitive, Inc. He received a BA in mathematics from Harvard University in 2008 and a PhD in mathematics from McGill University in 2014, specializing in number theory and algebraic geometry. In 2018 he joined the Department of Mathematics at Wayne State University (WSU), where he is now an Associate Professor (currently on leave). While at WSU, he developed new ways of measuring quantum entanglement using geometric invariant theory, as co-PI of a U.S. Department of Energy grant. Since 2023 he has been working with Qognitive, Inc., first as a consultant and then full-time as Director of Research, developing new machine learning models based on the mathematical formalism of quantum mechanics. Qognitive, Inc., is a startup founded in 2023 by Dario Villani and Kharen Musaelian, with the goal of developing and deploying models based on Quantum Cognition Machine Learning (QCML). QCML is a new form of machine learning that is inspired by quantum cognition. QCML models learn a representation of the input data into quantum states, and the outputs of the models reflect the outcomes of quantum measurements. QCML is highly effective on datasets with a large number of input features and a large number of classes (for classification) or targets (for regression). Qognitive has developed products for analyzing similarity of complex financial instruments, as well as analyzing similarity between patients using medical insurance claims data. See profile.
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.