• Instructional Assistant Professor
Na Zou

Educational Background

  • Ph.D., Industrial Engineering, Arizona State University, Dec. 2015
  • MSE, Civil, Environmental and Sustainable Engineering, Arizona State University, 2012 

Research Interests

  • Dr. Zou's research focus is statistical machine learning on large scale, dynamic and networked data with its applications in health care, brain science, and process control. Specifically, her interests include integrating Bayesian statistics and sparse learning models for transfer learning, statistical and predictive modeling of dynamic and multi-dimensional data for network evolution and change detection. She is also interested in brain informatics to model brain connectivity for cognitive performance assessment, biomarker identification and disease diagnosis.

Awards & Honors

  • Selected to New Faculty Colloquium, Institute of Industrial and Systems Engineers, 2017
  • Featured in ISE Magazine, Institute of Industrial and Systems Engineers, 2016
  • Irv Kaufman Award for Excellent Graduate Student, Phoenix Section Student Scholarship, IEEE Foundation, 2015
  • Graduate College Block Grant Funding Award, Arizona State University, 2011
  • Graduate Study Incentive Scholarship, Arizona State Universit, 2011

Selected Publications

  • Na Zou, Jing Li. “Modeling and Change Detection of Dynamic Networks by a Network State Space Model.”2017. IISE Transactions. 49(1):45-57 (featured in ISE Magazine)
  • Dmitry Titov, Janine Diehl-Schmid, Kuangyu Shi, Robert Perneczky, Na Zou, Stefan Förster, Timo Grimmer, Jing Li, Alexander Drzezga, Igor Yakushev. “Metabolic Connectivity for Differential Diagnosis of Dementing Disorders.” 2017. Journal of Cerebral Blood Flow & Metabolism. 37(1):252-262 (impact factor 5.41, ranked 18 among 335 journals in Neurology)
  • Na Zou, Mustafa Baydogan, Yun Zhu, Wei Wang, Ji Zhu, Jing Li. “A Transfer Learning Approach for Predictive Modeling of Degenerate Biological Systems.” 2015. Technometrics. 55(3):362-373.
  • Na Zou, Gael Chetelat, Mustafa Baydogan, Jing Li, Florian Fischer, Dmitry Titov, Juergen Dukart, Andreas Fellgiebel, Mathias Schreckenberger, Igor Yakushev. “Metabolic Connectivity as Index of Verbal Working Memory.” 2015. Journal of Cerebral Blood Flow & Metabolism. 35:1122-1126. (impact factor 5.41, ranked 18 among 335 journals in Neurology)