Dimitris Samaras

SUNY Empire Innovation Professor,

Director Computer Vision Lab, 

Computer Science Department

263 New Computer Science

Stony Brook University

Stony Brook, NY 11794-2424

(631) 632-8464


Description: http://www3.cs.stonybrook.edu/~samaras/dim6cr.jpg












a       Ph.D. in Computer Science, January 2001.University of Pennsylvania.

a       M.S in Computer Science, June 1994. Northeastern University.

a       Diploma in Computer Engineering and Informatics, June 1992. University of Patras, Greece._


a       Ongoing:          CSE656 Seminar in Computer Vision (graduate)

a       2005-2016:      CSE 378/525  Introduction to Robotics   (undergraduate/graduate)

a       2002-2015:        CSE527 Introduction to Computer Vision (graduate)

a       Fall 2004:        CSE 601/ESE559  (with G Gindi and J Liang) Advanced Image Processing  (graduate)

a       Fall 2004:                    CSE 592  Introduction to Robotics   (graduate)

a       Fall 2004:                    CSE 681  (with T Pavlidis) Topics in Computer Vision   (graduate)

a       Fall 2003:                    CSE 615  Advanced Image Analysis  (graduate)

a       Fall 2001-3:     CSE 390  Introduction to Visual Computing  (undergraduate)

a       Spring 2001:   CSE527/627 Introduction to Computer Vision (graduate)

a       Fall 2000:                    ESE 358/CSE 327  Computer Vision  (undergraduate)

Research Interests

Computer vision, computer graphics, machine learning, medical imaging, animation and simulation, image based rendering, physics-based modeling.


My research up to now has focused on explaining visual data for Computer Vision, Computer Graphics and Medical Image Analysis, through the appropriate physical and statistical models. A central interest is in modeling the interaction of 3D shape and illumination, (a major source of variability in images) for applications such as shape and motion estimation, object recognition and augmented reality. Further interest in 3D shape deformation is fueled by the availability of image and range data that allows statistical modeling of non-rigid motion. The construction of such statistical models leads to the problem of accurate matching of 3D data. A natural application area for all the above questions is the field of human modeling and especially faces. We have been focusing on topics such as facial appearance under variable illumination and facial expression modeling for biometrics and human computer interaction. My general interest in human modeling has led to exciting collaborations with psychologists who collect visual data about human behavior through multiple modalities such as eye-trackers and fMRI bran imaging. Thus based on recent neuropsychological findings we are exploring the application of machine learning techniques to the analysis of brain images. Sources of funding include NSF, NIH , DoE, DoJ, FRA, New York State and the Adobe Corp.

Selected Projects



DBLP, Google Scholar


More to come...