Computer Science and Engineering
SUNY at Buffalo
Jason J. Corso
Publication Tag Cloud
active clustering activity recognition artificial intelligence augmented reality belief propagation bioinformatics biomarkers biometrics braintumor computational finance computer forensics computer graphics computer vision computer-aided diagnosis cosegmentation data mining deformable dictionary transfer document imaging domain adaptation dynamic linear models endoscopy evaluation event recognition facade detection face detection face recognition feature extraction fusion gesture recognition gpu grammar graph cuts graph-based graphical models haptics hierarchical higher-order human pose estimation human-computer interaction image denoising image processing image retrieval image understanding localization lung imaging machine learning mapping max-margin medical imaging metric learning mobile robotics mosaicking motion estimation mrf multimedia natural language navigation neuroimaging object detection ontology probabilistic ontology protein structure prediction random forest reconstruction segmentation semantic segmentation slam spectral clustering spine imaging stereo streaming supervoxel surgical robotics tomographic reconstruction tracking video summarization video understanding volume rendering voxel maps
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:
Code and Data Downloads
Random Forest Distance -- tree-structured metric learning that implicitly adapts the metric over the sample space based on our KDD 2012 paper. Action Bank full code and processed data sets [direct link to code] LIBSVX: A Supervoxel Library and Benchmark for Early Video Processing. Implements a suite of supervoxel video segmentation methods as well as a quantitative set of 2D and 3D metrics for good supervoxels. Graph-Shifts Code (Java) and example data. Video label propagation code and benchmark data set. UB/College Park stereo building facade dataset. [more information].
Selected Publications [complete list here]
CSE 555: Introduction to Pattern Recognition -- Spring 2009--2013
CSE 672: Bayesian Vision -- Spring 2008, Fall 2010, Fall 2012
CSE 642: Techniques in AI: Vision for HCI -- Fall 2009
CSE 702: Seminar in Image Semantics -- Fall 2009
CSE 702: Seminar in Pattern Theory -- Fall 2008
CSE 702: Seminar: Topics in Medical Image Segmentation -- Fall 2007