SIAM awarded the 2017 SIAM Student Paper Prize
to Shuyang Ling for his paper, “Self-Calibration and Biconvex Compressive Sensing,” co-authored with Thomas Strohmer of the University of California, Davis and published in Inverse Problems in 2015. Ling received the award and presented his winning paper at the 2017 SIAM Annual Meeting
, held July 10-14, 2017 in Pittsburgh, Pennsylvania.
The SIAM Student Paper Prize is awarded annually to the student authors of the most outstanding papers submitted to the SIAM Student Paper Competition. The 2017 prize recognized outstanding scholarship by students in applied mathematics and computing as evidenced in a paper submitted for publication in a peer-reviewed journal. The awards are based solely on the merit and content of the student’s contribution to the paper. Up to three awards are given each year.
Shuyang Ling is currently a Courant Instructor in the Courant Institute of Mathematical Sciences, New York University. He received his PhD in 2017 from the University of California, Davis under the supervision of Thomas Strohmer in the Department of Mathematics. He earned his MS in statistics at the University of California, Davis in 2016 and obtained a bachelor’s degree in mathematics and applied mathematics at Fudan University in Shanghai, China in 2012. His primary research interests include compressive sensing and convex optimization, computational harmonic analysis, and probability and random matrices.
Q: Why are you excited about winning the prize?
A: Receiving the SIAM Student Paper Prize at the SIAM Annual Meeting brought an excellent ending to my course of PhD study in 2017. I feel honored that our work is recognized by SIAM. This is definitely a great encouragement for my future career.
Q: What does your research mean to the public?
A: There are many applications of my research in wireless communication (such as 5G network and the Internet of Things), image processing (blind deconvolution), and signal processing. I hope some of my research will have real impacts in the future.
Q: Could you tell us a bit about the research that won you the prize?
A: In the past 15 years, compressive sensing and sparsity have become a game changer in the signal processing community. They have changed the way people think about sampling theory. On the other hand, many state-of-the-art algorithms won’t perform well if the sensing/sampling process is not completely known due to the lack of calibration. This problem turns out to be a challenging bilinear inverse problem. Our contribution is developing a convex approach to recover the desired signal and calibrate the sensing processing simultaneously under certain conditions. The proposed method is guaranteed by rigorous mathematical theory and can be solved efficiently.
Q: What does being a SIAM member mean to you?
A: As a junior at Fudan University in Shanghai, China, I read a few SIAM books and watched a video on tensors from the 2010 Gene Golub SIAM Summer School. This was my first time reading math textbooks in English and I learned many aspects of applied mathematics. This experience is one of the reasons I decided to pursue a PhD in math. I became a SIAM member while at UC Davis and have benefitted greatly through the past few years from reading high-quality textbooks and articles published by SIAM.