Please join us in welcoming Yao Xie, Harold R. and Mary Anne Nash Early Career Professor, Associate Director of Machine Learning and Data Science and Associate Professor at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. She will talk about her research on Point process models for discrete events data
Discrete events are a sequence of observations consisting of event time, location, and possibly “marks” with additional event information. Such event data is ubiquitous in modern applications, including social networks, power networks, seismic activities, police reports data, neuronal spike trains, and COVID-19 data. We are particularly interested in capturing the complex dependence of the discrete events data, such as the latent influence — triggering or inhibiting effects of the historical events on future events. I will present our recent research on this topic from the continuous-time and the discrete-time approaches and introduce computationally efficient model estimation procedures with statistical guarantees, leveraging the recent advances in variational inequality for monotone operators that bypass the difficulty posed by the original non-convex model estimation problem. The performance of the proposed method is illustrated using real-world data: crime, power outage, hospital ICU, and COVID-19 data.
Yao Xie is the Harold R. and Mary Anne Nash Early Career Professor and Associate Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech.
Her research interests are in sequential statistical methods, statistical signal processing, big data analysis, compressed sensing, and optimization, and she has been involved in applications to wireless communications, sensor networks, and medical and astronomical imaging.
Dr. Xie previously served as Research Scientist in the Electrical and Computer Engineering Department at Duke University after receiving her Ph.D. in Electrical Engineering (minor in Mathematics) from Stanford University in 2011.
