I conduct both theoretical and applied research in the areas of large
scale inverse optimization, compressive sensing, sparse learning,
image/video processing, multimedia, computer vision and medical image
analysis. I am most interested in creating efficient algorithms with
nice theoretical guarantees and practical values (especially in practical
applications involved large scale data), as well as developing novel
theoretical insights into existing algorithms and problems.
Projects on Maching
Learning and Computer Vision
Load Monitoring (NILM)
to fully exploit the inherent characteristics of each appliance
in a specific functional mode?
to fully consider both specific characteristics, working states
and consumptions together?
to derive efficient algorithms for low-sample-rate data of
each appliance to enhance model scalability for Low-frequency
to investigate effiecient inference algorithms to learn the
latent states from measured aggregation data?
face tracking with a web camera or Kinect
detection by tracking slow eyelid closure and blinking
Synchrony as a Measure of Trust and Veracity
Facial expression recognition
Sparsity: Theorems, Algorithms and Applications
sparsity theorems give the insight when known structure/sparse
greedy algorithm (StructOMP) for strcutured sparsity recovery
applications on Compressive sensing on graph structured sparse
Group Sparsity and Its Applications
coefficients in data are not only sparse but also clustered
on Video forground detection and abnormal detection
feature selection scheme for dynamic group sparse feaures
Projects on Biomedical
on Biometrics Before 2005