About Me
Hello! I’m Huili Huang. I am a third-year PhD student of Computational Science and Engineering(CSE), I’m advised by Dr. David Frost and Dr. Max Mahdi Roozbahani. My research taps into utilizing deep learning for the assessment of natural disaster damage with limited information. My academic trajectory began at the University of Electronic Science and Technology of China (UESTC) where I acquired my B.S. in Software Engineering in 2019. I furthered my knowledge at Georgia Tech, earning an M.S. in Computer Science in 2021. You can view my resume here.
Publications
Research Experiences
Under the mentorship of Dr. Frost and Dr. Roozbahani, I’m part of the “Disaster Group” dedicated to analyzing natural catastrophes such as earthquakes, hurricanes, and wildfires. We employ a range of tools for data acquisition, encompassing satellites, UAVs, and imagery from social media platforms. My primary research areas include:
- Geospatial damage assessment based on self-supervised learning
- Disaster damage analysis based on multi-source data
Under the mentorship of Dr. Roozbahani, centered around the exploration and development of data augmentation algorithms harnessing the power of deep learning techniques. A primary emphasis was placed on self-supervised learning methods. A simple and efficient unsupervised representation learning method named ScaleNet based on multi-scale images is proposed to enhance the performance of ConvNets when limited information is available.
The development of a fire-detection system, extracting critical flame features using dynamic detection. Implemented the system in C++, first achieving 93% accuracy with SVM, then enhancing it to 99% accuracy by integrating YOLOv3.