University of Hawaii

Electrical Engineering

Talks

Conference presentations

[19] “Light-field reconstruction and depth estimation from focal stack images using convolutional neural networks,”
Lecture session on Learning based inversion,
IEEE Intl. Conf. on Acoust., Speech, and Signal Process. (ICASSP), May 2020. (Invited lecture)
[18] “Incorporating handcrafted filters in convolutional analysis operator learning for ill-posed inverse problems,”
Special session on Computational biomedical imaging,
IEEE Intl. Workshop on Comput. Adv. in Multi-Sensor Adaptive Process. (CAMSAP), Dec. 2019. (Invited poster)
[17] “BCD-Net for low-dose CT reconstruction: Acceleration, convergence, and generalization,”
Med. Image Compt. and Computer Assist. Interven. (MICCAI), Oct. 2019. (Selected poster)
[16] “Application of trained Deep BCD-Net to iterative low-count PET image reconstruction,”
IEEE Nuclear Science Symposium (NSS) and Medical Imaging Conference (MIC), Nov. 2018.
[15] “Signal recovery using trained CNNs: Relation to compressed sensing and application to sparse-view CT,”
Special session on Machine Learning advances in medical imaging,
Asilomar Conf. on Signals, Syst., and Comput., Oct. 2018. (Invited talk)
[14] “Convergent iterative signal recovery using trained convolutional neural networks,”
Special session on Computational imaging and inverse problems,
Annual Allerton Conf. on Commun., Control, and Comput., Oct. 2018. (Invited talk)
[13] “From convolutional analysis operator learning (CAOL) to convolutional neural network (CNN),”
Minisymposium on Recent advances in convolutional sparse representations,
SIAM Conf. on Imaging Science (IS), Jun. 2018. (Invited talk)
[12] “Deep BCD-Net using identical encoding-decoding CNN structures for iterative image recovery,”
IEEE Image, Video, and Multidim. Signal Process. (IVMSP) Workshop, Jun. 2018.
[11] “Low-rank plus sparse tensor models for light-field reconstruction from focal stack data,”
IEEE Image, Video, and Multidim. Signal Process. (IVMSP) Workshop, Jun. 2018.
[10] “Physics-driven deep training of dictionary-based algorithms for image reconstruction,”
Asilomar Conf. on Signals, Syst., and Comput., Nov. 2017. (Invited talk)
[9] “Convergent convolutional dictionary learning using adaptive contrast enhancement (CDL-ACE): Application of CDL to image denoising,”
Intl. Conf. on Sampling Theory and Appl. (SampTA), Jul. 2017.
[8] “Efficient sparse-view X-ray CT reconstruction using l1 regularization with learned sparsifying transform,”
Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med. (Fully 3D), Jun. 2017.
[7] “DTI reveals persistent effects on white matter in football players with history of sports-related concussion,”
IN Neuroimaging Symp., Nov. 2016.
[6] “Optimal sparse recovery for multi-sensor measurements,”
IEEE Inf. Theory Workshop (ITW), Aug. 2016.
[5] “Sparsity and parallel acquisition: Optimal uniform and nonuniform recovery guarantees,”
Workshop on Sparsity and Compressive Sensing in Multimedia (MM-SPARSE),
IEEE Intl. Conf. on Multimedia and Expo (ICME), Jul. 2016.
[4] “Robust detection of axonal abnormalities in high school collision-sport athletes: longitudinal single subject analysis,”
Intl. Soc. Mag. Res. Med. (ISMRM), May 2015. (E-Poster)
[3] “Non-convex compressed sensing CT reconstruction based on tensor discrete Fourier slice theorem”
IEEE Eng. Med. Biol. Conf. (EMBC), Aug. 2014.
[2] “Efficient compressed sensing statistical X-ray/CT reconstruction from fewer measurements,”
Intl. Mtg. on Fully 3D Image Recon. in Rad. and Nuc. Med. (Fully 3D), Jun. 2013.
[1] “Robust detection of progressive white matter abnormalities in mTBI using DW-MRI,”
Intl. Soc. Mag. Res. Med. (ISMRM), Apr. 2013. (E-Poster)

Seminar presentations

[10] “Machine learning & AI for imaging and potential application to EM imaging,”
Industry Advisory Board meeting, NSF Industry Univ. Cooperative Research Center, Nov. 2019.
[9] “ML & AI for breaking imaging limits,”
ECE seminar, Michigan State University (ECE), Mar. 2019.
[8] “ML & AI for breaking imaging limits,”
EE seminar, the University of Hawaiʻi, Mānoa (EE), Mar. 2019.
[7] “Breaking imaging limits via ML & AI,”
Seminar, Shanghai Jiao Tong University (UM-SJTU JI), Sep. 2018.
[6] “Breaking imaging limits via ML & AI,”
Special seminar, Ulsan National Institute of Science and Technology (ECE), Sep. 2018.
[5] “Breaking imaging limits via ML & AI,”
Seminar, Yonsei University (CSE), Aug. 2018.
[4] “Breaking imaging limits,”
Colloquium, Ohio State University (ECE), Mar. 2018.
[3] “Breaking imaging limits,”
Seminar, Texas Tech University (ECE), Feb. 2018.
[2] “Convolutional dictionary learning using a fast block proximal gradient method,”
Communications & Signal Processing seminar, the University of Michigan (EECS), Apr. 2017.
[1] “Compressed sensing and parallel acquisition,”
Communications & Signal Processing seminar, the University of Michigan (EECS), Jan. 2016.