A cross-library image augmentation module for Deep Learning Training
Published:
A versatile image augmentation framework incorporating 300+ operations from 8 popular libraries 
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Posts by Collection
portfolio
Feature Reduction to Classifiers
Published:
Performance studies of PCA, LDA, and their kernel versions to SVM, ML, KNN, GMM 
Compressive Image Recovery
Published:
Low-cost and high-efficient seismic image recovery and optimal sampling recommendation 
Deep Eraser
Published:
An object-oriented “eraser” for images and videos 
Pick-up Drop-off Design
Published:
Use reinforcement learning to design a route for delivery man 
Zero-human-effort Segmentation
Published:
A fully automatic iterative deep learning framework for cell segmentation on noisy Label 
Pixel Translator
Published:
Convert gray images of border/vein to RGB leaf images using cGAN
Hierarchical Spatial Pattern Analysis on Neuronal Neighborhood
Published:
A robust method to detect & profile injury-caused alterations to brain tissue at the multi-cellular scale 
Large Scale Image Registration
Published:
Accelerated large-scale image alignment by 10× with uniform keypoint control and multiprocessing 
Multiplex Channels Denoising and Deblurring by Wavelet Transform
Published:
Wavelet analysis for recovering useful information from damages with as noise and blurs 
publications
A Simplified Normalization Operation for Perfect Reconstruction from a Modified STFT
Published in IEEE International Conference on Signal Processing (ICSP), 2014
A improved version of short-time-fourier-transform.
Phasetime: Deep Learning Approach to Detect Nuclei in Time Lapse Phase Images
Published in Journal of clinical medicine, 2019
A Mask RCNN approach of nuclei segmentation in time lapse time in nanowells.
Attenuating Random Noise in Seismic Data by a Deep Learning Approach
Published in arXiv preprint, 2019
Attenuate Gaussian noise by residual neural networks.
Swell-noise attenuation: A deep learning approach.
Published in The Leading Edge, 2019
The full manuscript of Swell-noise attenuation by residual neural networks.
Seismic Compressive Sensing by Generative Inpainting Network: Toward An Optimized Acquisition Survey
Published in The Leading Edge, 2019
The full manuscript of compressive image recovery and non-uniform sampling recommendation of my summmer intern project at Anadarko.
Generative Inpainting Network Applications on Seismic Image Compression and Non-Uniform Sampling
Published in Workshop on Neural Information Processing Systems (NIPS): Solving Inverse Problems with Deep Networks, 2019
The preliminary results of compressive image recovery and non-uniform sampling recommendation of my summmer intern project at Anadarko.
Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification
Published in Medical Image Computing and Computer Assisted Intervention(MICCAI), 2020
An active meta-Learning approach of cell classification use a very few tranining data.
Comprehensive Cell Phenotyping Method for Whole-Brain Tissue Mapping Using Highly Multiplexed Immunofluorescence Imaging
Published in Nature Communications, 2021
Our lab’s complete pipleline for whole brain analysis, including my main thesis topic of nuclei cell segmenetation.
ARIA: Adversarially Robust Image Attribution for Content Provenance
Published in CVPR, 2022
Internship mentoring project at Adobe related to Coalition for Content Provenance and Authenticity (C2PA) initative, setting up industrial standard to address misleading information, lead by Adobe
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.