Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.
Dr. Tolga Tasdizen

Dr. Tolga Tasdizen - SCI Faculty Member

Department of Electrical and Computer Engineering

WEB 4893
phone (801) 581-3539
fax (801) 585-6513
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My Publications


  • B.S. in Electrical Engineering , Bogazici University , Istanbul, 1995
  • M.S. in Engineering, Brown University, Providence, 1997
  • Ph.D. in Engineering, Brown University, Providence, 2001

Thesis title: "Robust and Repeatable Fitting of Implicit Polynomial Curves to Point Data Sets and to Intensity Images"

Current Responsibilities

Dr. Tasdizen's research group aims to make contributions to solutions of fundamental problems in image analysis and machine learning such as learning in the absence of large labeled datasets and adapting knowledge between domains. He currently leads interdisciplinary research efforts which apply these novel machine learning and image analysis techniques to problems including radiological image interpretation, public health prediction from Google Street View images, and material science.

Dr. Tasdizen is the SCI Institute's second USTAR faculty member. USTAR is an innovative, aggressive and far-reaching effort to bolster Utah's economy with high-paying jobs and keep the state vibrant in the Knowledge Age. The USTAR Support Coalition and the Salt Lake Chamber sought public and private investment to recruit world-class research teams in carefully targeted disciplines. These teams will develop products and services that can be commercialized in new businesses and industries.

Research Interests

  • Image analysis and computer vision
  • Semi-supervised learning
  • Domain adaptation
  • Deep learning for radiology and epidemiology  
  • Deep learning for material science
  • Neural circuit reconstruction