Jonathan Ventura Ph.D.
Jonathan Ventura is an assistant professor at the University of Colorado, Colorado Springs. His research focus is developing computer vision techniques for mobile augmented reality. He is especially interested in vision-based modeling and camera localization. The goal of his work is to enable sophisticated and widely available mobile augmented reality experiences through advanced sensor technology.
Dr. Ventura is a native of the central coast of California and earned his Ph.D. in Computer Science from the University of California, Santa Barbara in 2012. He received his B.S. and M.S. degrees in Computer Science from UCSB, as well. Before joining the University of Colorado, Colorado Springs, he was a postdoctoral researcher with the Institute for Computer Graphics and Vision at Graz University of Technology in Austria. He has also worked as a research intern at the Adobe Advanced Technologies Lab in San Jose, CA. He has presented his work at several top international conferences and journals, including IEEE CVPR, ECCV, ACM SIGGRAPH, IEEE ISMAR, and IEEE Transactions on Visualization and Computer Graphics. Joint work with his colleagues has twice been awarded the best paper prize from IEEE ISMAR, the premier international augmented reality conference.
Ph.D. positions are available for research projects on geometric computer vision, augmented reality and robotics. If you are interested, please contact me via email and include a summary of your research interests and a CV. Final graduate admission decisions are made by the computer science departmental committee.
Positions are also available for M.S. students. If you have not taken a graduate course taught by me, I prefer that you take a semester of independent study with me before starting your M.S. project or thesis credits. If you are interested, contact me via email and include a statement of research interests.
Undergraduates interested in research experience are encouraged to contact me as well; positions are available in my lab to help with experimental setup and dataset collection.