Weiqi Yue | Case School of Engineering

PhD Student in Computer & Data Science, SDLE Research Center

My research focus lies in the realm of 2D X-ray diffraction data analysis, where I leverage computer vision and deep learning techniques to extract complex features. Additionally, I am actively involved in the development of federated learning frameworks, ensuring collaborative model training across multiple clients while prioritizing privacy protection. My overarching goal is to delve into the theoretical foundations of deep learning algorithms, identifying and optimizing models tailored to specific tasks in diverse areas.

Education

B.S., Mathematics, University of Miami

M.S., Applied Statistics, Johns Hopkins University

Research Interests

XRD Analysis, Computer vision and deep learning, Federated Learning

Publications

Yue, W., Mehdi, M., Tripathi, P. K., Willard, M. A., Ernst, F. A., & French, R. H. (2024). Exploring 2D X-ray diffraction phase fraction analysis with convolutional neural networks: Insights from kinematic-diffraction simulations. MRS Advances, 9 , 921-928.

Yue, W., Tripathi, P. K., Ponon, G. K., Ualikhankyzy, Z. K., Brown, D. W., Clausen, B. W., Stranza, M. W., Pagan, D. C., Willard, M. A., Ernst, F. A., Ayday, E. A., Chaudhary, V. A., & French, R. H. (2024). Phase Identification in Synchrotron X-ray Diffraction Patterns of Ti–6Al–4V Using Computer Vision and Deep Learning. Integrating Materials and Manufacturing Innovation, 13 (1), 36-52.