· M.S in Computer Science, June 1994. Northeastern University.
· Diploma in Computer Engineering and Informatics, June 1992. University of Patras, Greece.
· Available Projects for CSE523/524 UPDATED Jan 2005
· Ongoing: CSE656 Seminar in Computer Vision (graduate)
· Fall 2005: CSE 378/525 Introduction to Robotics (undergraduate/graduate)
· Spring 2002-5: CSE527 Introduction to Computer Vision (graduate)
· Fall 2004: CSE 601/ESE559 (with G Gindi and J Liang) Advanced Image Processing (graduate)
· Fall 2004: CSE 592 Introduction to Robotics (graduate)
· Fall 2004: CSE 681 (with T Pavlidis) Topics in Computer Vision (graduate)
· Fall 2003: CSE 615 Advanced Image Analysis (graduate)
· Fall 2001-3: CSE 390 Introduction to Visual Computing (undergraduate)
· Spring 2001: CSE527/627 Introduction to Computer Vision (graduate)
· Fall 2000: ESE 358/CSE 327 Computer Vision (undergraduate)
Computer vision, computer graphics, machine learning, medical imaging, animation and simulation, image based rendering, physics-based modeling.
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. Current
sources of funding include NSF, NIH (NIDA), DoE and DoJ