Jason J. Corso

Dr. Jason J. Corso is currently an assistant professor of Computer Science and Engineering Department at SUNY at Buffalo. He received his Ph.D. in Computer Science at The Johns Hopkins University in 2005. He received the M.S.E Degree from The Johns Hopkins University in 2002 and the B.S. Degree with honors from Loyola College In Maryland in 2000, both in Computer Science. He spent two years as a post-doctoral research fellow at the University of California, Los Angeles. He is a recipient of the NSF CAREER award, ARO Young Investigator award, on the DARPA CSSG, UB Young Investigator award and a UB Innovator award.

His main research thrust is high-level imaging science. From biomedicine to recreational video, imaging data is ubiquitous. Yet, imaging scientists and intelligence analysts are without an adequate language and set of tools to fully tap the information-rich image and video. He works to provide such a language; specifically, he studies the coupled problems of segmentation and recognition from a Bayesian perspective emphasizing the role of statistical models in efficient visual inference. His long-term goal is a comprehensive and robust methodology of automatically mining, quantifying, and generalizing information in large sets of projective and volumetric images and video. The following four questions drive his current research inquiries:

How to use principled hierarchical structures to model complex real-world phenomena?
How to handle the massive data glut for machine learning yet require little or no user labeling?
How to incorporate prior high-level knowledge (semantics, context, etc.) during both learning and inference?
How to understand and enhance the role of the user in active semi-supervised learning scenarios?

More broadly, his research interests are in the fields of computer and medical vision (segmentation and recognition), computational biomedicine, machine intelligence, statistical learning, perceptual interfaces and smart environments. More information on these topics can be found in the research pages.

Code and Data Downloads

Selected Publications [complete list here]

[1]	C. Xiong, D. Johnson, R. Xu, and J. J. Corso. Random forests for metric learning with implicit pairwise position dependence. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012. [ bib \| .pdf ]
[2]	S. Sadanand and J. J. Corso. Action bank: A high-level representation of activity in video. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2012. [ bib \| code \| project \| .pdf ]
[3]	C. Xu and J. J. Corso. Evaluation of super-voxel methods for early video processing. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2012. [ bib \| code \| project \| .pdf ]
[4]	J. A. Delmerico, P. David, and J. J. Corso. Building facade detection, segmentation, and parameter estimation for mobile robot localization and guidance. In Proceedings of International Conference on Intelligent Robots and Systems, 2011. [ bib \| project \| data \| .pdf ]
[5]	D. R. Schlegel, A. Y. C. Chen, C. Xiong, J. A. Delmerico, and J. J. Corso. AirTouch: Interacting with computer systems at a distance. In Proceedings of IEEE Winter Vision Meetings: Workshop on Applications of Computer Vision (WACV), 2011. [ bib \| .pdf ]
[6]	R. S. Alomari, J. J. Corso, and V. Chaudhary. Labeling of lumbar discs using both pixel- and object-level features with a two-level probabilistic model. IEEE Transactions on Medical Imaging, 30(1):1-10, 2011. [ bib \| .pdf ]
[7]	W. Ceusters, J. J. Corso, Y. Fu, M. Petropoulos, and V. Krovi. Introducing ontological realism for semi-supervised detection and annotation of operationally significant activity in surveillance videos. In Proceedings of the 5th International Conference on Semantic Technologies for Intelligence, Defense and Security (STIDS), 2010. [ bib \| .pdf ]
[8]	J. J. Corso, A. Yuille, and Z. Tu. Graph-Shifts: Natural Image Labeling by Dynamic Hierarchical Computing. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2008. [ bib \| code \| project \| .pdf ]
[9]	J. J. Corso, E. Sharon, S. Dube, S. El-Saden, U. Sinha, and A. Yuille. Efficient Multilevel Brain Tumor Segmentation with Integrated Bayesian Model Classification. IEEE Transactions on Medical Imaging, 27(5):629-640, 2008. [ bib \| .pdf ]