主讲人：包承龙 清华大学长聘副教授
时间：2025年5月25日10：00
地点：三号楼332室
举办单位：数理学院
主讲人介绍：包承龙，清华大学丘成桐数学科学中心长聘副教授、北京雁栖湖应用数学研究院副研究员、清华大学膜生物学全国重点实验室研究员。2014 年博士毕业于新加坡国立大学数学系， 2015 年至 2018 年在新加坡国立大学数学系进行博士后研究。研究兴趣主要在图像处理的建模与大规模优化算法方面，担任SIAM Journal on Imaging Sciences编委，已在各类期刊和会议上发表学术论文50余篇。
内容介绍：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.