This is an introductory-level course to computer graphics. No previous knowledge on the subject is assumed. The objective of the course is to provide a comprehensive introduction to the field of computer graphics, focusing on the underlying theory, and thus providing strong foundations for both designers and users of graphical systems. The course will study the conceptual framework for interactive computer graphics, introduce the use of OpenGL as an application programming interface (API), and cover algorithmic and computer architecture issues.
CS 559:Machine Learning: Fundamentals and Applications
In many fields (e.g., computer vision, speech recognition, data mining, and bioinformatics), machine learning has become a crucial ingredient in translating research into applications. The course is intended to provide an in-depth overview of recent advances in machine learning, with applications in fields such as computer vision, data mining, natural language processing. Fundamental topics that will be covered include supervised (Bayesian) and unsupervised learning, non-parametric methods, graphical models (Bayes Nets and Markov Random Fields) and dimensionality reduction. The course will also cover several of the most important recent developments in learning algorithms, including boosting, Support Vector Machines and kernel methods, and outline the fundamental concepts behind these approaches.
Schaefer School of Engineering & Science
Research & Education
Ph.D. in Electrical Engineering University of Southern California, Los Angeles, CA (2005)
M.S. in Electrical Engineering, University of Southern California, Los Angeles, CA (2000)
Diploma in Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece (1998)
Binocular, multiple-view and video-based 3D reconstruction
3D shape representation and object recognition
Experience & Service
Assistant Professor, Dept. of Computer Science, Stevens Institute of Technology, 2008-present
P. Mordohai and G. Medioni. (2006). "Tensor Voting: A Perceptual Organization Approach to Computer Vision And Machine Learning".
P. Mordohai and G. Medioni. ""Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting", Journal of Machine Learning Research", vol. 11 411-450.
M. Pollefeys, D. Nistér, J.-M. Frahm, A. Akbarzadeh, P. Mordohai, B. Clipp, C. Engels, D. Gallup, S.-J. Kim, P. Merrell, C. Salmi, S. Sinha, B. Talton, L. Wang, Q. Yang, H. Stewénius, R. Yang, G. Welch, H. Towles,. (2008). " "Detailed Real-Time Urban 3D Reconstruction From Video", International Journal of Computer Vision", vol. 78 (2-3), 143-167.
A. Toshev, P. Mordohai and B. Taskar. (2010). "Detecting and Parsing Architecture at City Scale from Range Data", International Conference on Computer Vision and Pattern Recognition".
X. Hu and P. Mordohai. (2010). "Evaluation of Stereo Confidence Indoors and Outdoors", International Conference on Computer Vision and Pattern Recognition".
P. Mordohai. (2009). "The Self-Aware Matching Measure for Stereo", International Conference on Computer Vision".
A. Patterson, P. Mordohai and K. Daniilidis. (2008). "Object Detection from Large-Scale 3D Datasets using Bottom-up and Top-down Descriptors", European Conference on Computer Vision".