Qi graduated with his PhD in Spring 2026 and then started a position as a postdoctoral researcher in the Department of Biostatistics and Bioinformatics at Emory University. His dissertation work was on development of models for dependent data that utilize various deep learning architectures to improve prediction. He recently presented his work on Spatial Deep Convolutional Neural Networks at the 2024 Joint Statistical Meetings.

Awards

• 2026 UCSC Dissertation Year Fellow
• UCSC First Generation Graduate Group Research and Travel Award, Spring 2025
• UCSC Graduate Dean’s Research Travel Grant, Winter 2025

Publications

• Wang, Q., Parker, P.A., Lund R., and Woody, J. (2026+) A Spatio-Temporal Study of Planetary Snow Presence Trends.
• Kei, Y.L., Wang, Q., Parker, P.A., Ribalet, F., Hyun, S. (2026+) Change Point Detection for Cell Populations Measured via Flow Cytometry.
• Wang, Q., Parker, P.A., and Lund R. (2025) Hierarchical Count Echo State Network Models with Application to Graduate Student Enrollments. Data Science in Science, Special Issue on ``Data Science in the Federal Government”, 4(1), https://doi.org/10.1080/26941899.2025.2565243.
• Wang, Q., Parker, P.A., and Lund R. (2025) Spatial Deep Convolutional Neural Networks. Spatial Statistics, 66, 100883.

Conference Presentations

• “Hierarchical Count Echo State Network Models with Application to Graduate Student Enrollments” (Topic-contributed Session) 2025 Joint Statistical Meetings, Nashville, TN, USA
• “Hierarchical Count Echo State Network Models with Application to Graduate Student Enrollments” 2025 International Symposium on Forecasting, Beijing, China
• “Spatial Deep Convolutional Neural Networks” (Contributed Session) 2024 Joint Statistical Meetings, Portland, OR, USA