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Zoe Zhang

Fengqing Zoe Zhang, PhD

Assistant Professor
Department of Psychology
Office: Stratton 316
fengqing.zhang@drexel.edu
Phone: 215.553.7172

Additional Sites: Quantitative Psychology and Statistics Lab

Curriculum Vitae:

View/Download CV [PDF]

Research Interests:

  • Multimodal Neuroimaging
  • Data Mining
  • Data Integration
  • Mobile Health
  • Wearable Computing

Impact:

Zhang’s work on quantitative modeling aims to improve our understanding of complex and high dimensional data and ultimately our ability to fully utilize the informational complexity for new levels of scientific discovery.

As the amount of data being generated is exploding, we have entered the era of Big Data. To the extent that data can be analyzed, we may be able to gain a completely new perspective on our world, how people interact, spend their resources, and organize their time. Though promises are held, the increasing amount of data, the different types of data from heterogeneous sources, and required fast speed of data processing pose great challenges to data management and analysis. Zhang and her team are committed to making a difference in the statistical thinking and computational approaches required to handle these challenges.

Bio:

Fengqing (Zoe) Zhang, PhD, is an assistant professor in the Department of Psychology. Prior to joining Drexel University, she obtained her PhD degree in Statistics at Northwestern University. Her research interests lie primarily in the development and application of advanced statistical models to analyze complex and high dimensional data (e.g. neuroimaging data, complex behavioral data). In particular, her lab has been focused on using multimodal neuroimaging (e.g., MRI, DTI, fMRI, PET) to examine neurodegenerative diseases (e.g., Alzheimer’s disease) and psychiatric disorders (e.g., PTSD, eating disorders). The modeling approach she takes includes machine learning, Bayesian inference, and high dimensional data analysis. In addition, she works on the statistical methods development for informing real time individualized sequences of treatments (Just-in-Time Adaptive Interventions) and integrating multimodal data generated from wearables (e.g., fitness trackers, heart rate monitors).