主講人：包承龍 清華大學長聘副教授

時間：2025年5月25日10：00

地點：三號樓332室

舉辦單位：數理學院

主講人介紹：包承龍，清華大學丘成桐數學科學中心長聘副教授、北京雁棲湖應用數學研究院副研究員、清華大學膜生物學全國重點實驗室研究員。2014 年博士畢業于新加坡國立大學數學系， 2015 年至 2018 年在新加坡國立大學數學系進行博士后研究。研究興趣主要在圖像處理的建模與大規模優化算法方面，擔任SIAM Journal on Imaging Sciences編委，已在各類期刊和會議上發表學術論文50余篇。

內容介紹：A significant gap between theory and practice in imaging sciences arises from inaccuracies in mathematical models, including imperfect imaging models and complex noise. Recent advancements have seen deep neural networks directly mapping observed data to clean images using paired training data. While these approaches deliver promising results across various tasks, collecting paired training data remains challenging and resource-intensive in practice. To address this limitation, we propose a unified generative model capable of leveraging both paired and unpaired data during training. Once trained, the model can generate high-quality synthetic data for direct use in downstream tasks. Experimental results on diverse real-world datasets demonstrate the effectiveness of the proposed methods. Finally, I will present recent progress in addressing the preferred orientation problem in cryo-EM, showcasing how these tools contribute to advancing the field.