University of Hawaii

Electrical Engineering

Talks

Conference presentations

[21] “Iterative neural networks for inverse problems in medical imaging,”
IEEE Intl. Conf. on Nano/Molecular Med. & Eng. (NANOMED), 12/2020. (Invited talk)
[20] “Momentum-Net for low-dose CT image reconstruction,”
Asilomar Conf. on Signals, Syst., and Comput., 11/2020.
[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), 5/2020. (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), 12/2019. (Invited poster)
[17] “BCD-Net for low-dose CT reconstruction: Acceleration, convergence, and generalization,”
Med. Image Compt. and Computer Assist. Interven. (MICCAI), 10/2019. (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), 11/2018.
[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., 10/2018. (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., 10/2018. (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), 6/2018. (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, 6/2018.
[11] “Low-rank plus sparse tensor models for light-field reconstruction from focal stack data,”
IEEE Image, Video, and Multidim. Signal Process. (IVMSP) Workshop, 6/2018.
[10] “Physics-driven deep training of dictionary-based algorithms for image reconstruction,”
Asilomar Conf. on Signals, Syst., and Comput., 11/2017. (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), 7/2017.
[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), 6/2017.
[7] “DTI reveals persistent effects on white matter in football players with history of sports-related concussion,”
IN Neuroimaging Symp., 11/2016.
[6] “Optimal sparse recovery for multi-sensor measurements,”
IEEE Inf. Theory Workshop (ITW), 8/2016.
[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), 7/2016.
[4] “Robust detection of axonal abnormalities in high school collision-sport athletes: longitudinal single subject analysis,”
Intl. Soc. Mag. Res. Med. (ISMRM), 5/2015. (E-Poster)
[3] “Non-convex compressed sensing CT reconstruction based on tensor discrete Fourier slice theorem”
IEEE Eng. Med. Biol. Conf. (EMBC), 8/2014.
[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), 6/2013.
[1] “Robust detection of progressive white matter abnormalities in mTBI using DW-MRI,”
Intl. Soc. Mag. Res. Med. (ISMRM), 4/2013. (E-Poster)

Seminar presentations

[11] “Iterative AI for breaking imaging limits,”
ECE Colloquium Series (Eleanore Hale Wilson Lecture), the University of Minnesota (ECE), 11/2019.
[10] “Machine learning & AI for imaging and potential application to EM imaging,”
Industry Advisory Board meeting, the University of Minnesota (ECE), 03/2021.
[9] “ML & AI for breaking imaging limits,”
ECE seminar, Michigan State University (ECE), 3/2019.
[8] “ML & AI for breaking imaging limits,”
EE seminar, the University of Hawaiʻi, Mānoa (EE), 3/2019.
[7] “Breaking imaging limits via ML & AI,”
Seminar, Shanghai Jiao Tong University (UM-SJTU JI), 9/2018.
[6] “Breaking imaging limits via ML & AI,”
Special seminar, Ulsan National Institute of Science and Technology (ECE), 9/2018.
[5] “Breaking imaging limits via ML & AI,”
Seminar, Yonsei University (CSE), 8/2018.
[4] “Breaking imaging limits,”
Colloquium, Ohio State University (ECE), 3/2018.
[3] “Breaking imaging limits,”
Seminar, Texas Tech University (ECE), 2/2018.
[2] “Convolutional dictionary learning using a fast block proximal gradient method,”
Communications & Signal Processing seminar, the University of Michigan (EECS), 4/2017.
[1] “Compressed sensing and parallel acquisition,”
Communications & Signal Processing seminar, the University of Michigan (EECS), 1/2016.