Dongeun Lee received his Ph.D. in Electrical Engineering and Computer Science and B.S. degree in Computer Science and Engineering from Seoul National University, Korea in 2014 and 2006, respectively.
During the graduate study under guidance of Prof. Heonshik Shin, he experienced diverse research fields ranging from wireless sensor network to video coding, which eventually converged on his Ph.D. thesis, titled Analysis for Scalable Coding of Quality-Adjustable Sensor Data. His interest in machine learning led him to join SAIL at UNIST.
His passions other than research are music and ski. (One of them can be identified on the above goofy photo.) He is now jointly affiliated with Lawrence Berkeley National Laboratory as a computer systems engineer.
Dongeun Lee’s current research interests lie at the intersection of statistical signal processing and machine learning. He presented an imprecise compressive sensing (CS) framework that handles uncertainty in signal recovery when there is no guarantee of enough measurements for varying signal sparsity, which is often found in dynamic systems. He recently worked on a novel data reduction method based on statistical similarity, whose concept is implemented as a S/W release dubbed IDEALEM. IDEALEM shows compression ratios far exceeding 100x, while capturing extraordinary events in data.
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