Di Wang first became interested in deep learning through a seminar at the Center for Advanced Imaging Innovation and Research of NYU Langone Health.
“The seminar is about deep learning application in medical imaging field. Everybody was talking about it so I wanted to take a look,” he explained.
Wang is currently a student in the joint UT Health San Antonio/UTSA Biomedical Engineering Ph.D. program. He works with Dr. Peter Fox where he is using neuroimaging data to predict brain age.
“Brain age is the predicted age by machine learning using neuroimaging data. It has the potential to become a new aging biomarker,” he explained. “We are still trying to figure out how to improve the accuracy.”
He enjoys working on his project because deep learning is still an active field and there’s a lot of potential for his work to transform lives.
“Currently, artificial intelligence (AI) still can’t be broadly applied clinically, however in the future, I believe AI in healthcare will play a critical role,” he said.
Wang was attracted to our joint UTSA/UT Health San Antonio Biomedical Engineering Ph.D. program because of the ability to work on a project that could combine his passion for deep learning and his passion for improving medical technology.
“The guy doing deep learning isn’t always applying it to medical imaging, so I feel like the bridge because biomedical engineering students equip with both medical and engineering knowledge,” he said.
He completed his bachelor’s degree in biomedical engineering at Jinan University and he first experimented the medical imaging research as a master’s student in Biomedical Engineering at New York University. Wang’s project involved MR imaging processing and diffusion MRI.
“Machine learning algorithms can do it,” he said. “I used to draw ROIs on MR imaging manually with my hand and I would sit on the computer until my hand couldn’t move so I thought to myself why couldn’t I let the computer do it? ”
After graduation, he is interested in working at Google, Simmons, GE or a company that focuses on both deep learning or machine learning and medical imaging.
“It’s a cool area. It’s growing,” he said. “We are witnessing history.”