报告人：Prof. Shiwen Mao，Professor and Earle C. Williams Eminent Scholar, IEEE Fellow，Director, Wireless Engineering Research & Education Center Department of Electrical and Computer Engineering, Auburn University (奥本大学)
报告时间：2024年04月18日 (星期四) 10:00-11:30 am
报告地点：腾讯在线会议 805-608-478
报告摘要: Deep learning has shown great promise in solving many open challenges in wireless networking research and applications and intelligence has been recognized as a defining feature of the next generation wireless networks. However, deep learning is data hungry, and one of the critical obstacles towards fulfilling its promise is facilitating the acquisition of sufficient amounts of data to train and validate deep learning models. In this talk, we examine various approaches that enable wireless researchers and practitioners to acquire data more efficiently at reduced cost and to utilize existing data more effectively. In particular, we will review several effective approaches to reduce the reliance on data in deep learning-based wireless research, such as data imputation and augmentation methods with case studies. These approaches are quite effective and general, and should be helpful to researchers in this exciting field to tackle other data-drive wireless problems.
深度学习在解决无线网络研究和应用中的许多开放挑战方面显示出了巨大的前景，并且智能已被认为是下一代无线网络的定义特征。 然而，深度学习需要数据，而实现其承诺的关键障碍之一是促进获取足够数量的数据来训练和验证深度学习模型。 在本次演讲中，我们研究了各种方法，使无线研究人员和从业人员能够以更低的成本更有效地获取数据并更有效地利用现有数据。 特别是，我们将回顾几种有效的方法来减少基于深度学习的无线研究中对数据的依赖，例如数据插补和案例研究的增强方法。 这些方法非常有效且通用，应该有助于这个令人兴奋的领域的研究人员解决其他数据驱动无线问题。
报告人简介: Shiwen Mao (S'99-M'04-SM'09-F'19) is a Professor and Earle C. Williams Eminent Scholar, and Director of the Wireless Engineering Research and Education Center at Auburn University. Dr. Mao's research interest includes wireless networks, multimedia communications, and smart grid. He is the editor-in-chief of IEEE Transactions on Cognitive Communications and Networking. He received the IEEE ComSoc MMTC Outstanding Researcher Award in 2023, the 2023 SEC Faculty Achievement Award for Auburn, the IEEE ComSoc TC-CSR Distinguished Technical Achievement Award in 2019, the Auburn University Creative Research & Scholarship Award in 2018, the NSF CAREER Award in 2010, and several service awards from IEEE ComSoc. He is a co-recipient of the 2022 Best Journal Paper Award of IEEE ComSoc eHealth Technical Committee, the 2021 Best Paper Award of Elsevier/KeAi Digital Communications and Networks Journal, the 2021 IEEE Internet of Things Journal Best Paper Award, the 2021 IEEE Communications Society Outstanding Paper Award, the IEEE Vehicular Technology Society 2020 Jack Neubauer Memorial Award, the 2018 Best Journal Paper Award and the 2017 Best Conference Paper Award from IEEE ComSoc MMTC, the 2004 IEEE Communications Society Leonard G. Abraham Prize in the Field of Communications Systems, and 10 IEEE best conference paper/demo awards.
Shiwen Mao是奥本大学无线工程研究与教育中心的教授和Earle C. Williams杰出学者，也是该中心的主任。毛博士的研究兴趣包括无线网络、多媒体通信和智能电网。他是IEEE认知通信与网络期刊的主编。他曾获得多项奖项，包括2023年IEEE通信学会MMTC杰出研究员奖、2023年奥本大学SEC教职工成就奖、2019年IEEE通信学会TC-CSR杰出技术成就奖、2018年奥本大学创新研究与学术成就奖、2010年NSF CAREER奖以及IEEE通信学会的多项服务奖。他还是以下奖项的共同获得者：2022年IEEE通信学会eHealth技术委员会最佳期刊论文奖、2021年Elsevier/KeAi数字通信与网络期刊最佳论文奖、2021年IEEE物联网期刊最佳论文奖、2021年IEEE通信学会杰出论文奖、IEEE车载技术学会2020年杰克·诺伯尔纪念奖、2018年IEEE通信学会MMTC最佳期刊论文奖、2017年IEEE通信学会MMTC最佳会议论文奖、2004年IEEE通信学会Leonard G. Abraham通信系统奖以及10项IEEE最佳会议论文/演示奖。
邀请人：蒋洪波
联系人：胡靖阳
