Jie Zhou, Ph.D.

Department of Computer Science
Northern Illinois University

Mailing address:
Department of Computer Science
Northern Illinois University
DeKalb, IL 60115

Email: jzhou [at] niu.edu

Office: PM 360

My research interests are in pattern recognition and computational intelligence, including the development of pattern recognition algorithms and tools for problems in the fields of biology and medicine.

My current projects are on automatic classification, annotation and quantification of multi-dimensional biological images,  such as high-content or high-throughput microscopic images obtained by fluorescent protein labeling. My team is working on automatic analysis of multi-dimensional fruit fly Drosophila m. gene expression images,  including annotation during embryogenesis,   cell counting of larvae central nervous system, and inference of regulatory relationship, based on image analysis, regression and classification using machine learning and pattern recognition approaches.  

I am an associate editor of Pattern Recognition Journal since 2006. I am a senior member of IEEE since 2009. I am also a reviewer of many journals and international conferences in the field. I have given invited talks at institutions and international conferences. I am a recipient of Burroughs Wellcome Fund (BWF) Collaborative Research Travel Award and a multiple time recipient of NIU summer research grant. Our research is supported by National Institutes of Health/National Institute of Mental Health.

Here is a list of my selected publications, and the link to BIOCAT, a BIOimage Classification and Annotation Tool.

Group News for the Image Learning And Analytics Lab. See more at the ILAAL Lab Website:

  • 2nd Intl. Conference on Pattern Recognition and Artificial Intelligence
  • Bioimage Informatics Conference 2017
  • Congrats to Varshini for successfully presenting her research at ACM BCB 2017
  • Congrats to Randall Suvanto, Alex Ekstrom and other co-authors for the paper on automatic neuron counting being accepted by Brain Informatics 2016 (BIH 2016).
  • Congrats to Jon Sanders' successful defense! (Jun 2016)
  • Road trip! ILAAL lab travels to Kalamazoo, MI, for a joint research meeting/retreat with collaborator Ye lab. (Apr 2016)
  • Congrats to all co-authors on our paper being accepted by IEEE Intl. Conference on Data Science and Advanced Analytics (IEEE DSAA'2015), with the acceptance rate for long presentation paper being 9%! (August 2015)
  • Paper on "Learning-guided Automatic Three Dimensional Synapse Quantification for Drosophila Neurons" is accepted by BMC Bioinformatics! (May 2015)
  • Paper on "Automatic Dendrite Length Quantification for High Throughput Screening of Mature Neurons" is accepted by Neuroinformatics. Congrats to Tim, Venkat and all co-authors! (Spring 2015)
  • Welcome Joseph Steinke to the team!
  • Venkat Pasupuleti travels to Long Beach, CA to present his research results! (Sept 2014)
  • Congrats to Tim Smafield on his successful defense! (June 2014)
  • Collaborative paper on Drosophila topography is accepted by Current Biology. Our team is responsible for fine-scale 3D quantification. (Spring 2014)
  • Welcome Shawn Horvatic to the team! (Spring 2014)
  • Road trip! Team travels to Univ of Michigan to observe imaging experiments.(Dec 2013)
  • Dr. Jie Zhou attends ACM BCB 2013 presenting "Performance Model Selection for Learning-based Biological Image Analysis on a Cluster".
  • Venkat Pasupuleti joins the team (supported by NIH).
  • The paper "BIOCAT: a Pattern Recognition Platform for Customizable Biological Image Classification and Annotation" is accepted by BMC Bioinformatics.
  • Dr. Jie Zhou is awarded the NIH/NIMH R15 MH099569 as the principal investigator for Automatic 3D Quantification of Synapse Distribution in Complex Dendritic Arbor.
  • Timothy Smafield joins the team (supported by Great Journeys Assistantship).
  • Eric Swatkowski joins the team (supported by LA&S URAP).
  • Jonathan Sanders joins the team (supported by LA&S URAP and Office of Student Engagement of Experiential Learning).