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