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X-ORIGINAL-URL:https://canssiontario.utoronto.ca/
BEGIN:VEVENT
UID:MEC-2649b36f54ee6080dd7e2c057585bce6@canssiontario.utoronto.ca
DTSTART:20250131T193000Z
DTEND:20250131T203000Z
DTSTAMP:20240802T114000Z
CREATED:20240802
LAST-MODIFIED:20241219
SUMMARY:CAST Seminar: Xinwei Deng
DESCRIPTION:\nJoin us at the CANSSI Ontario STatistics Seminars (CAST) with\n\n\n\nDr. Xinwei Deng\n\n\n\nProfessor, Data Science Faculty FellowDepartment of Statistics, Virginia Tech\n\n\n\n\n\n\n\nFree Hybrid (On Line/In Person) Event | Registration Required\n\n\n\nTalk Title\n\n\n\nNew Songs for Old Stories: Interface between Experimental Design and Machine Learning\n\n\n\nAbstract\n\n\n\nExperimental design and machine learning techniques have been widely used in engineering and data science applications. However, these two major areas have not been well integrated, especially on using experimental design thinking to enhance machine learning and using machine learning ideas to improve data collection. In this talk, we will present several recent research on the interface between experimental design and machine learning to facilitate data collection, modeling, and decision making in the era of data science and AI. Specifically, two research works will be presented. The first part will introduce an active learning approach, called QS-learning, to enable effective modeling and efficient optimization for a new type of data with quantitative-sequence (QS) factors. The QS factor involves a sequence of multiple components associated with their quantities, widely used in health care, logistics, and many other disciplines. The second part will present a variational mutual information (MI) estimator for data and model parameters, leading to a simple and powerful contrastive MI estimator for Bayesian optimal experimental design. The performance of the proposed methods is evaluated by both numerical examples and real applications.\n\n\n\nSpeaker Profile\n\n\n\nXinwei Deng is Professor of Statistics and Data Science Faculty Fellow at Virginia Tech. He is also a co-director of VT Statistics and Artificial Intelligence Laboratory (VT-SAIL). Dr. Deng received his PhD degree in industrial engineering from Georgia Tech in 2009. His research interests focus on statistical modeling of complex data, design and analysis of experiments, uncertainty quantification and digital twin, and the interface between experimental design and machine learning. Dr. Deng research development has produced over 120 publications in top statistics journals and machine learning conferences. He has been has been associate editors for seversl top-tier statistical journals.\n
URL:https://canssiontario.utoronto.ca/event/cast-xinwei-deng/
ORGANIZER;CN=CANSSI Ontario:MAILTO:esther.berzunza@utoronto.ca
CATEGORIES:CANSSI Ontario Statistics Seminars
LOCATION:48 University Avenue, Kingston, ON
ATTACH;FMTTYPE=image/jpeg:https://canssiontario.utoronto.ca/wp-content/uploads/2024/08/Deng-Xinwei-Calendar-scaled.jpg
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