About the Author
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Stanley H. Chan Contact Information: Phone: +1 (765) 496 0230 Follow me at:
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Bio Sketch
Stanley H. Chan is currently an Elmore Associate Professor in the School of
Electrical and Computer Engineering and the Department of Statistics at
Purdue University, West Lafayette, IN.
Dr. Chan received the Ph.D. degree in Electrical Engineering and the M.A.
degree in Mathematics from the University of California at
San Diego, in 2011 and 2009, respectively, and the B.Eng. degree (with first
class honor) in Electrical Engineering from the University of Hong Kong in 2007. He was a postdoctoral research fellow at
Harvard John A. Paulson School of Engineering
and Applied Sciences from 2012 to 2014. He joined Purdue University in 2014
as an Assistant Professor of Electrical and Computer Engineering, and an
Assistant Professor Statistics.
Dr. Chan is the recipient of the Best Paper Award in IEEE International
Conference on Image Processing 2016 (with his PhD student Omar Elgendy). He
has also won numerous teaching awards, including the Ruth and Joel Sipra
Outstanding Teaching Award in 2019, Purdue College of Engineering Exceptional
Early Career Teaching Award in 2019, Purdue Teaching for Tomorrow Fellow,
2018, Eta Kappa Nu (HKN) Purdue Outstanding Professor Award in 2018, Purdue
College of Engineering Outstanding Graduate Mentor Award in 2016, and Eta
Kappa Nu (HKN) Purdue Outstanding Teaching Award 2015. Prior to joining
Purdue, he was a recipient of the Croucher Foundation Postdoctoral
fellowship, and the Croucher Foundation PhD Scholarship.
He is currently serving as an Associate Editor of the IEEE Transactions on Computational Imaging, and was a former Associate Editor of the OSA Optics Express. In 2015-2021, Dr. Chan was an elected member of the IEEE Signal Processing Society Technical Committee on Computational Imaging. He was also an area chair for IEEE ICIP and IEEE ICASSP for many years.
Dr. Chan does research in computational imaging. He is particularly interested in photon-limited imaging, imaging through atmospheric turbulence, and robust machine learning. His research is supported by the National Science Foundation, Air Force Research Lab, Army Research Office, and other federal and industrial sponsors.