Learning from the collective wisdom of crowds parallels the statistical concept of fusion learning from multiple data sources or studies. However, integrating inferences from diverse sources poses significant challenges due to cross-source heterogeneity and data-sharing limitations. Studies often rely on varied designs and modeling techniques, and stringent data privacy norms can prohibit even the sharing of summary statistics. In this talk, I will discuss the construction of "synthetic statistics" that mimic the summary statistics used for inference, enabling the fusion of inference results from multiple sources.
Speaker: Dr. Bowen GANG
Date: 28 February 2025 (Friday)
Time: 2:30pm – 3:30pm
Zoom: Link
Poster: Click here
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Biography
Dr. Bowen Gang is an Assistant Professor at the School of Management, Fudan University. He holds a Bachelor's degree from McGill University (2014) and a Ph.D. from the University of Southern California (2020). Dr. Gang's research expertise lies in multiple testing, sequential inference, and model-free inference. His work has been published in leading journals including the Journal of the American Statistical Association, Biometrika, and the Journal of Machine Learning Research.