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Beilei Xu
Affiliation
Rochester Data Science Consortium, University of Rochester, Rochester, NY, USA
Topic
Deep Learning,Convolutional Neural Network,Long Short-term Memory,Model Calibration,Model Parameters,Phasor Measurement Units,Power System,Calibrated Model Parameters,Deep Learning Models,Recurrent Neural Network,Actor Network,Convolutional Neural Network Architecture,Convolutional Neural Network Model,Critic Network,Deep Learning Approaches,Feed-forward Network,Gated Recurrent Unit,Latent Space,Neural Network,Random Search,Simulated Data,Training Data,Training Set,3D Convolution,3D Kernel,Age Estimation,Age Groups,Age Prediction,Age Regression,Alternative Models,Amount Of Change,Amount Of Smoothing,Apparent Age,B-type Natriuretic Peptide,Basic Architecture,Basis For Further Exploration,Bayesian Inference,Bayesian Optimization,Breast Cancer Screening,Calibration Approach,Calibration Method,Chronological Age,Complex Models,Computation Time,Continuous Action Space,Convolutional Layers,Data Augmentation,Data Augmentation Approach,Data Distribution,Data Model,
Biography
Beilei Xu received the Ph.D. degree in medical physics from the University of Chicago. She is a currently Senior Research Scientist with the Rochester Data Science Consortium, University of Rochester. Prior to joining the University of Rochester, she was a Senior Research Scientist with the Palo Alto Research Center, A Xerox Company, Webster, NY, USA, where she led and contributed to a range of projects in the areas of managed print services, subsystem modeling, image-based defect detection, computer vision, and machine and deep learning in healthcare and transportation. She has published 39 papers, a book chapter, and holds more than 120 U.S. patents. Her research interests include image and video analytics, machine and deep learning, and statistical modeling in healthcare and energy. She is a certified Design for Lean Six Sigma Black Belt.