Michael G. Ross

mgross@broadinstitute.org (617) 714-8537 Computer Scientist Computational Research and Development Broad Institute

Research

I am a computer scientist working on computational problems in genome analysis. Previously, I worked on applications of statistical machine learning to problems in computer and human vision. I did my doctoral research with Leslie Pack Kaelbling at the MIT Computer Science and Artificial Intelligence Laboratory. After receiving my Ph.D. I was a postdoctoral researcher with Professor Andrew Cohen at the University of Massachusetts Amherst's Psychology Department and with Professor Aude Oliva at MIT's Department of Brain and Cognitive Sciences.

Manuscripts

Publications

• Daniel Aird, Michael G. Ross, Wei-Sheng Chen, Maxwell Danielsson, Tim Fennell, Carsten Russ, David B. Jaffe, Chad Nusbaum, Andreas Gnirke. Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biology, 12(2). Februrary 2011. [link]
• Michael G. Ross, Aude Oliva. Estimating perception of scene layout properties from global image features. Journal of Vision, 10(1). January 2010. [link][code and data]
• Andrew L. Cohen, Michael G. Ross. Exploring Mass Perception with Markov Chain Monte Carlo. Journal of Experimental Psychology: Human Perception and Performance, 35(6). December 2009. [pdf]
• Michael G. Ross, Andrew L. Cohen. Using graphical models to infer multiple visual classification features. Journal of Vision, 9(3). 2009. [link]
• Michael G. Ross, Leslie Pack Kaelbling. Segmentation According to Natural Examples: Learning Static Segmentation from Motion Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(4). April 2009. [pdf]
• Marwan A. Mattar, Michael G. Ross, Erik G. Learned-Miller. Nonparametric Curve Alignment. IEEE International Conference on Acoustics, Speech, and Signal Processing. 2009. [pdf]
• Kyle R. Cave, Andrew Cohen, Caren Rotello, Anthony McCaffrey, Michael G. Ross, Min Zeng, Xingshan Li, Matthew Zivot, Kris Chang. Using Eye Movements to Understand Complex Visual Comparisons, K. Rayner, D. Shen, X. Bai, and G. Yan (eds), Tianjin People’s Press/Psychology Press. 2008.
• Michael G. Ross, Andrew L. Cohen. GRIFT: A graphical model for inferring visual classification features from human data. Advances in Neural Information Processing Systems (NIPS) 20. 2008. [pdf]
• Michael Wick, Michael G. Ross, Erik Learned-Miller. Context-Sensitive Error Correction: Using Topic Models to Improve OCR. Ninth International Conference on Document Analysis and Recognition (ICDAR). September 2007. [pdf]
• Andrew L. Cohen, Richard M. Shiffrin, Jason M. Gold, David A. Ross, Michael G. Ross. Inducing features from visual noise. Journal of Vision, 7(8). 2007. [link]
• Michael G. Ross. Learning Static Object Segmentation from Motion Segmentation. Massachusetts Institute of Technology Ph.D. Thesis. August 2005. [pdf]
• Michael G. Ross, Leslie Pack Kaelbling. Learning Static Object Segmentation from Motion Segmentation. Twentieth National Conference on Artificial Intelligence. July 2005. [pdf]
• Michael G. Ross, Leslie Pack Kaelbling. A Systematic Approach to Learning Object Segmentation from Motion. NIPS 2003 Workshop on Open Challenges in Cognitive Vision. December 2003. [pdf]
• Michael G. Ross, Leslie Pack Kaelbling. Learning object segmentation from video data. MIT Artificial Intelligence Lab Memo, AIM-2003-022 (MIT-CSAIL-TR-2003-018). September 2003. [link]
• Michael G. Ross. Exploiting Texture-Motion Duality in Optical Flow and Image Segmentation. Massachusetts Institute of Technology Master's Thesis. August 2000. [pdf]
• Michael G. Ross. Multiscouting: Guiding distributed manipulation with multiple mobile sensors. Dartmouth College Technical Report PCS-TR98-332. June 1998. [pdf]

Code

• Master's Thesis Code Requires quicksort function to be implemented - see README files for details