February 26, 2019 | Erin Bluvas, email@example.com
Originally from the Hainan Province in China, Yuan Wang demonstrated statistical prowess early on—earning a bachelor’s degree in mathematics from Cambridge University followed by a master’s in the same subject from the University of Hong Kong. She became interested in applying her mathematical skills to health when she read a paper on the application of topological data analysis to cortical thickness analysis in maltreated children. The article was based on original research conducted by University of Wisconsin associate professor of biostatistics and medical informatics Moo Chung, who would become Wang’s doctoral advisor.
“Data explosion in neuroscience brings exciting opportunities to truly understand human cognitive functions and neurological disorders,” Wang says of the need to apply topological data analysis to this branch of science. “The increase in data variety and volume means that valuable information may be hidden in a way that cannot be decoded easily by traditional data analytic tools.”
During her Ph.D. program, Wang’s mentor challenged her to develop topological data methodology in areas without much previous research. Her resulting dissertation, Topological Data Analysis of Electroencephalographic Signals, is among the first studies to develop statistical inference frameworks for topological information in electroencephalographic signals and network connectivity in epilepsy, aging and meditation studies.
After completing her doctorate in 2018, Wang spent two months learning about multimodal neuroimaging research in epilepsy with Carrie McDonald’s group at the University of California, San Diego. “The modeling challenges associated with the neurological dysfunctions seen in epilepsy fascinated me, and I decided to tackle the challenges using the methodological development skills I've mastered over the years,” Wang says.
She then joined the Arnold School’s department of epidemiology and biostatistics to collaborate with the Aphasia Laboratory, which is directed by communication sciences and disorders professor and SmartState Endowed Chair of Memory and Brain Function Julius Fridriksson. After working with him on his aphasia research, Wang began developing unified modeling frameworks for both epilepsy and aphasia.
“My current research is aimed at advancing highly efficient statistical and classification algorithms to explore geometric and topological patterns hidden in the sheer volume of neuroimaging datasets combining brain signals and structural, diffusion and functional magnetic resonance imaging data,” says Wang. “I work closely with neuroscientists and neurologists to determine robust electrophysiological and neuroimaging markers for clinical diagnosis and prognosis.”
“The addition of Dr. Yuan Wang enriches our department immeasurably,” says professor and chair Anthony Alberg. “Not only is she an extremely talented statistician, but her research focus highlights the translational impact the quantitative sciences can have in the clinical setting. We look forward to the impactful advances Yuan and her research team will make.”
Wang also joined the Arnold School for its educational and service opportunities. In addition to teaching biostatistics courses, she mentors master’s and doctoral students who are learning how to build robust biostatistical markers for brain network disorders.
“My approach to biostatistical education and collaboration centers on relating mathematical abstraction to the problem-solving nature of statistics,” she says. “Relevance helps students understand statistical concepts and empowers them to creatively apply the concepts in data analysis and method development.”