Renzhi Cao

Assistant Professor of Computer Science

Renzhi Cao
Phone:
Email:
Office:
Morken Center for Learning & Technology - Room 248
M W F:
3:00 pm - 5:30 pm
Mon - Fri:
By Appointment
Website:
  • Professional
  • Personal

Education

  • Ph.D., Computer Science, University of Missouri-Columbia, 2016
  • M.S., Computer Science, University of Science and Technology of China, 2011
  • B.S., Computer Science, Anhui Normal University, 2008

Areas of Emphasis or Expertise

  • Machine learning
  • Data science
  • Bioinformatics

Selected Presentations

  • 23rd International Conference on Intelligent Systems for Molecular Biology (ISMB), Large-Scale Model Quality Assessment for Improving Protein Tertiary Structure Prediction (2015)
  • The 5th International Conference on Wireless Communications Networking and Mobile Computing, WiCOM, Apply Modified Method of Nonlinear Optimization to Improve Localization Accuracy in WSN, Beijing (2009)

Selected Articles

  • D. Bhattacharya, R. Cao, J. Cheng. "UniCon3D: de novo protein structure prediction using united-residue conformational search via stepwise, probabilistic sampling." Bioinformatics 2016:
  • D. Bhattacharya, J. Nowotny, R. Cao, J. Cheng. "3Drefine: An Interactive Web Server for Efficient Protein Structure Refinement." Nucleic Acids Research Vol. 10.1093, 2016:
  • R. Cao, J. Cheng. "Protein single-model quality assessment by feature-based probability density functions." Scientific Reports 2016:
  • J. Nowotny, A. Wells, O. Oluwadare, L. Xu, R. Cao, T. Trieu, C. He, J. Cheng. "GMOL: an interactive tool for 3D genome structure visualization." Scientific Reports 2016:
  • J. Nowotny, S. Ahmed, L. Xu, O. Oluwadare, H. Chen, N. Hensley, T. Trieu, R. Cao, J. Cheng. "an chromosomes from chromosomal contact data." BMC bioinformatics Vol. 16(1), 2015: 338.

Biography

My research interest is mainly focused on developing and applying machine learning and data mining techniques to solve biomedical problems, such as human genome data analysis and protein structure predictions. In addition, I am interested in promoting early engagement of undergraduate students (especially for women and underrepresented students) in machine learning, bioinformatics, and the data science field by interdisciplinary studies, and inspiring students to pursue advanced STEM education/research careers.