The Michigan Engineer News Center

Pin-Yu Chen receives Rackham Chia-Lun Lo Fellowship

Chen's work can be used in community detection in social networks, network vulnerability assessment in communication systems, and more.| Short Read
EnlargePin-Yu Chen
IMAGE:  

Pin-Yu Chen, a graduate student in the Electrical Engineering:Systems program, received a Rackham International Student Fellowship to continue his studies in big data. Mr. Chen is a doctoral student in Prof. Al Hero‘s research group.

Mr. Chen applies signal processing techniques to big data analysis to discover valuable information hidden in massive data sets as efficiently as possible. His approach can be applied to community detection in social networks, network vulnerability assessment in communication systems, and functional imaging in biology, to name a few.

Mr. Chen received his bachelor’s and master’s degrees from National Chiao Tung University and National Taiwan University (NTU) in 2009 and 2011, respectively. His master’s thesis was awarded the Best Master Thesis Award from NTU, and he received the Graduates of the Last Decade (GOLD) Best Paper Award in 2010 for the paper, “Information Epidemics in Complex Networks with Opportunistic Links and Dynamic Topology,” at the 2010 IEEE Global Communications Conference (GLOBECOM). He has already co-authored 14 journal and conference papers.

Pin-Yu serves as a mentor to the Michigan Taiwanese Student Association, and is a member of the national engineering honor society, Tau Beta Pi.

The Rackham Chia-Lun Lo Fellowship assists outstanding international students who have earned a previous degree from a university in Taiwan. To be eligible, international graduate students must have successfully completed one year of graduate study as a master’s or precandidate student. Nominees must have a strong academic record, be making good progress toward the degree, and demonstrate outstanding academic and professional promise.

Pin-Yu Chen
Portrait of Catharine June

Contact

Catharine June
ECE Communications and Marketing Manager

Electrical Engineering and Computer Science

(734) 936-2965

3301 EECS

Sound wave visualization. Getty Images.

Mining soundwaves: Researchers unlock new data in sonar signals

“Acoustic fields are unexpectedly richer in information than is typically thought.” | Medium Read