Welcome to Ross Goroshin's webpage
My name is Ross (also Rostislav) Goroshin, I am a PhD student
in Computer Science at the Courant Institute of Mathematical Sciences.
I study Machine Learning under Yann LeCun. My recent interests include: unsupervised learning, dictionary and representation (feature) learning.
In the past I have worked on computer vision and robotics projects which involved ideas from: 3D computer vision, machine learning, applied variational methods, geodesic active contours, and the level-set method.
 Unsupervised Learning of Spatiotemporally Coherent Metrics
Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun
 Unsupervised Feature Learning from Temporal Data
Ross Goroshin, Joan Bruna, Arthur Szlam, Jonathan Tompson, David Eigen, Yann LeCun, NIPS 2014 Deep Learning Workshop, Montreal, QC
 Efficient Object Localization Using Convolutional Networks
Jonathan Tompson, Ross Goroshin, Arjun Jain, Yann LeCun, Chris Bregler
 Saturating Auto-Encoders
Rostislav Goroshin and Yann LeCun, International Conference on Learning Representations (ICLR 2013), Scottsdale, AZ
 "Approximate solutions to several visibility optimization problems"
Rostislav Goroshin, Quyen Huynh, and Hao-Min Zhou, Communications in Mathematical Sciences Volume 9 Issue 2, June 2011
. Also available from UCLA(UCLA CAM Report 10-07
 "Automated cable detection in sonar imagery" J.C. Isaacs, R. Goroshin, IEEE SMC 2009
, San Antonio, TX
 "Obstacle Detection using a Monocular Camera" Rostislav Goroshin, Georgia Institute of Technology M.S. Thesis 2008. Georgia Tech ETD
Past & Present
2010-Present: New York University (PhD in Computer Science)
2008-Present: Naval Surface Warfare Center-Panama City Division (Computational Science Branch)
2006-2008: Georgia Institute of Technology (M.S. in Electrical Engineering, minor Mathematics)
2002-2006: Concordia University (B.Eng. in Electrical Engineering with Distinction)
2000-2002: Vanier College (Diplome d'etudes collegiales (DEC) en Sciences pures et appliquees)
My contact information can be found here.