Li, Lianlin
Research Professor
Research Interests: Electromagnetic scattering and imaging
Office Phone: 86-10-6275 4409-2
Email: lianlin.li@pku.edu.cn
Li, Lianlin is a hundred-talented professor in the School of Electronics Engineering and Computer Sciences, Peking University, since 2012. He obtained his Ph.D. from Institute of Electronics, Chinese Academy of Sciences in 2006. He worked as a Postdoctoral Researcher from 2008 to 2009, and was appointed as the research assistant professor from 2009 to 2012, at the Texas A&M University, Texas, USA, respectively. His research interests include the electromagnetic inverse problem, computational electromagnetic, microwave imaging, and electromagnetic big data.
Dr. Li has published more than 60 well-respected journal papers and more than 60 peer-reviewed conference papers, such as Adv. Sci., Light Sci. and Appl., Phys. Rev., Sci. Rep., IEEE Trans. Antenna. And Prop., IEEE Trans. Geos., and Remote Sensing, etc. He has served in the Technical Program Committee of various international conferences including URSI GASS, IEEE APRCA, PIER, and so on. He was elected as the Early Career Representative by URSI (2014- ), and served as the associate editor of Radio Science Bulletin (2014- ), and Editorial Board of Journal of Radar (2015- ) and Journal of Radio Science (2017- ). His Ph.D. thesis was nominated by the Chinese Academy of Sciences as Best PhD Thesis Award in 2007, he was also awarded URSI Young Scientist (2011, 2014) and SPE Best Paper (2011).
Dr. Li has been the Principle Investigator of more than eight research projects including NSFC, 863 project, etc. His research achievements are summarized as follows:
1. Smart electromagnetic imaging system: One major research topic in the area of electromagnetic imaging is to make the real-time and low-cost imaging architecture with the data acquisition in a dynamic and adaptive way. He proposed several novel electromagnetic methods to break the bottleneck of the time-consuming synthetic aperture system and hardware-expensive phase-array system. Such imaging mechanism also supports the far-field super-resolution imaging using a single sensor, which provides a real-time imaging paradigm without mechanical movement, near scanning and sensor array.
2. Object-oriented imaging algorithm: The electromagnetic imaging is notoriously ill-posed since the measurements are very limited compared with the unknowns to be reconstructed, which is a challenging and open problem. He for the first time introduced the sparsity-driven solution into the underground nonlinear inverse problem, which enhance significantly enhance the imaging quality. His paper was elected by the Mathematics of Planet Earth (2013) as one of significant research developments in the mathematical, physical and biological sciences.
3. Electromagnetic inverse scattering with phaseless data: Phase measurement is a challenging topic for high-frequency (like THZ, optics, etc) regime. He developed the phaseless Rytov inversion and phaseless CSI methods, by which the imaging result is comparable to that with full data. Such methods could be used to avoid the expensive phase measurement in the THz and beyond, and also for wireless tomography.
