由信息与通信工程学院主办的“信通论坛”活动特别邀请宾夕法尼亚州立大学Monga教授作学术交流。具体安排如下，欢迎广大师生参加。
主 题：Machine Learning for Radar Waveform Design: Retrospectives and Future Trends
时 间：2025年7月7日14:00-15:00
地 点：清水河校区科研楼C216
主讲人： Professor Vishal Monga（IEEE Fellow, The Pennsylvania State University）
主持人：信息与通信工程学院余显祥副教授
报告简介：This talk will survey recent and current research themes in the Information Processing and Algorithms Lab (iPAL) – http://signal.ee.psu.edu -- at Penn State University, directed by Prof. Vishal Monga. Inspired by approaches in statistical estimation, the talk will discuss the incorporation of prior knowledge in learning frameworks, particularly deep neural networks. We will demonstrate that informed priors and accompanying architectures can help address vexing challenges in deep learning for image processing and vision such as limited, low-resolution, and/or noisy training data. Applications will be discussed in consumer and medical imaging encompassing both discriminative and generative AI, and in healthcare and environmental domains. The overarching goal of the talk is to guard against the pitfalls of black box learning and instead advocate a more principled, domain enriched approach.
主讲人简介：
Professor Vishal Monga has been a faculty member in the Department of Electrical Engineering and Computer Science at Pennsylvania State University since Fall 2009, where he directs the Information Processing and Algorithms Laboratory (iPAL). Prior to joining Penn State, he served as an imaging scientist at Xerox Research Labs from October 2005 to July 2009. He has also held visiting positions at Microsoft Research in Redmond, WA, and the University of Rochester. He earned his Ph.D. in Electrical and Computer Engineering from the University of Texas at Austin. Professor Monga's research interests encompass signal and image processing, computational imaging, radar signal processing, and the integration of artificial intelligence and data science. His contributions have been recognized with several prestigious awards, including the 2015 National Science Foundation CAREER Award, the 2016 Joel and Ruth Spira Teaching Excellence Award, the 2019 Penn State Engineering Alumni Society Outstanding Research Award, and the 2022 PSEAS Premier Research Award. In 2022, he was inducted as a Senior Member of the National Academy of Inventors, and in 2025, he was named an IEEE Fellow, acknowledging his significant contributions to computationally efficient image analysis and restoration. Professor Monga actively participates in the IEEE Signal Processing Society, serving on technical committees such as Sensor Array and Multichannel (SAM), Computational Imaging, and Bio-Imaging and Signal Processing (BISP). He held the position of Technical Directions Chair for the IEEE Image, Video, and Multidimensional Signal Processing Technical Committee from 2017 to 2019. His editorial experience includes roles on the boards of IEEE Transactions on Image Processing (TIP), IEEE Journal of Selected Topics in Signal Processing (JSTSP), IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), and IEEE Signal Processing Letters (SPL). He is also a founding editorial board member of the open-access journal Frontiers in Signal Processing. In addition to his editorial work, Professor Monga edited the Springer publication "Handbook of Convex Optimization Methods in Imaging Science." He holds 45 U.S. patents, reflecting his innovative contributions to the field. His recent research endeavors include leading a four-year, $1.6 million project funded by the U.S. Department of Defense's Strategic Environmental Research and Development Program (SERDP), focusing on the application of artificial intelligence to analyze 3D underwater sonar imagery for the detection and remediation of military munitions. In 2024, his collaborative work on "Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing" received the IEEE Signal Processing Magazine Best Paper Award.
