Kiavash Kianfar

Associate Professor

Image of Kiavash Kianfar

Office: 4014 ETB
Phone: 979.458.2362
Fax: 979.458.4299

MODAL Lab Website
Google Scholar Profile

Research Interests

Dr. Kianfar is the director of the  Mathematical Optimization and Data Analysis Lab (MODAL)  located in 3017 ETB. The research in this lab is on theory, computation, and application of mathematical optimization, network problems, and big data analysis. Several applications ranging from bioinformatics to transportation are studied. Developing faster algorithms for optimization problems and novel applications of optimization/network modeling/statistics in big data analysis are the major research thrusts in this lab.

  •   Theory and Computation of Mixed Integer Programming and Combinatorial Optimization
    •  New classes of complex integer rounding cuts for mixed integer programs
    •  Computations of cutting planes in solving lot-sizing, facility location, and network-design problems
  • Bioinformatics Modeling and Algorithms
    •  Cutting-edge big-data/optimization/network problems in microarray and next-generation sequencing data analysis
  •   Mathematical Optimization in Emerging Areas
    •  Sensor network reliability analysis by measuring redundancy in large-scale systems
    •  Maximum-demand rectangular location problem: A coverage problem in service networks
    •  Optimal deployment of emission reduction technologies (ERTs)
    •  Optimization in health care systems by reducing medication errors

Awards & Honors

  • For a complete list, please refer to the MODAL lab webpage


  • Ph.D., Industrial and Systems Engineering, Aug 2007 Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA
  • M.S., Industrial Engineering, Sep 2000 Industrial Engineering, Sharif University of Technology, Tehran, Iran
  • B.S., Industrial Engineering, Jul 1998 Industrial Engineering, Sharif University of Technology, Tehran, Iran

Selected Publications

For the complete list of publications, click on the button below.