Dr.  Xipeng Shen Digital Library

Based in Raleigh, NC, United States
Xipeng Shen


Xipeng Shen is an Associate Professor in the Computer Science Department at NCSU, and an IBM Center for Advanced Studies Faculty Fellow.  Prior to joining NC State in 2014, Shen was the Adina Allen Term Distinguished Associate Professor in the Computer Science Department at The College of William and Mary, USA. He was a Visiting Researcher at Massachusetts Institute of Technology (MIT) and Microsoft Research in 2012 and 2013. He has been a consultant to both Intel and Cisco. He was an assistant professor at The College of William and Mary from 2006 to 2012. He received his Ph.D. in Computer Science at University of Rochester in 2006, Master in Pattern Recognition and Intelligent Systems at Chinese Academy of Sciences in 2001, and Bachelor in Engineering at North China University of Technology in 1998.

Shen has been an ACM professional member since 2006.  He has published over 79 refereed papers, helped organize 13 major conferences and workshops, served on 48 program committees of major conference and workshops in the last 4 years, and gave 40 invited or keynote talks. Most of these conferences, workshops, and journals have been sponsored by ACM.  

Shen's research lies in the broad field of compiler and programming systems, with an emphasis on enabling data-intensive high performance computing and intelligent computing through innovations in both compilers and runtime systems.  There are two focuses in his recent research. The first is to make software effectively exploit the memory hierarchy and concurrency of heterogeneous, parallel systems. The other focus of his research is at the fundamental level of programming systems research, with the goal as to characterize large-scale, input-sensitive program behaviors.

Aiming at creating revolutionary paradigm shifts at the fundamental level while staying focused on missions critical for science and humanity development, his research has drawn recognitions from both national agencies and industry, exemplified by a US DOE Early Career Award (6% selection rate nationwide), a US NSF Career Award, Google Research Faculty Award, IBM CAS Research Faculty Fellow, and numerous industry technology transfers and fellowships.  His recent work---including a data locality model, an input-adaptive profiling-driven optimization technique, and a GPU data placement optimizer---have been adopted by IBM in their production XLC/C++/Fortran compilers.

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