George Papandreou – Home Page

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Contact info

Toyota Technological Institute at Chicago
6045 S. Kenwood Ave.
Chicago, Illinois 60637

Phone: +1-310-310-4614
Email: (replace x with gpapan)

About Me

Since December 2014 I have been working at Google as Research Scientist. I will continue to update this web page.

I am a Research Assistant Professor at the Toyota Technological Institute at Chicago. My research interests are in computer vision, machine learning, and multimodal perception. My current focus is on deep learning. I approach these problems with methods from Bayesian statistics, signal processing, and applied mathematics.

From 2009 to 2013 I was a Postdoctoral Research Scholar at UCLA, working with Prof. Alan Yuille. I hold a Diploma (2003) and a PhD (2009) in Electrical and Computer Engineering from NTUA, Greece, where I was a CVSP group member, advised by Prof. Petros Maragos.

[CV…] [Bio…]

Recent Research Highlight: Deep Epitomic Convolutional Networks

Deep Epitomic Convolution 

I have been exploring the powerful epitomic data structure for transformation-aware image analysis and recognition. Building on image epitomes, I have developed a new BoW-type model using a dictionary of flat mini-epitomes learned in an unsupervised fashion from raw images. In my most recent work in the context of deep learning, I have proposed the epitomic convolution layer as a powerful replacement of a consecutive pair of convolution and max-pooling layers.
Deep epitomic nets along with explicit scale/position search have been the key ingredients in our TTIC_ECP entry to the Imagenet LSVRC 2014 image classification competition, achieving 10.2% top-5 error rate, a 3% performance improvement over a baseline conventional max-pooled convnet.
[CVPR 2014] [arXiv] [ILSVRC results] [ILSVRC workshop]

Recent Research Highlight: Perturb-and-MAP Random Fields


I have been developing a new Perturb-and-MAP framework for one-shot random sampling in Gaussian or discete-label Markov random fields (MRF). Perturb-and-MAP random fields turn powerful deterministic energy minimization methods into efficient random sampling algorithms. By avoiding costly MCMC, one can generate in a fraction of a second independent random samples from million-node networks. Applications include model parameter estimation and solution uncertainty quantification in computer vision applications.
For an overview, see my review article which appears as book chapter in the recently published MIT Press book on Advanced Structured Prediction.
[Read more…]


  • December 19, 2014  —  Recent work (DeepLab-CRF arXiv paper) setting a new state-of-art (66.4 % IoU, further improved to 67.1 % IoU with the addition of intermediate layer features) in semantic image segmentation on the PASCAL VOC 2012 benchmark. We refine densely computed convolutional neural network response maps with fully-connected conditional random fields. Algorithmic improvements allow us to compute dense segmentation maps in a fraction of a second. Joint work with Jay Chen and collaborators at Ecole Centrale Paris/INRIA, Google Research, and UCLA.

  • November 30, 2014  —  Technical report on arXiv exploring explicit position, scale, and aspect ratio modeling in the context of deep convolutional neural networks (DCNNs). We describe an improved version of our TTIC_ECP entry on the Imagenet 2014 image classification/localization competition. We further show that competitive object detection results (56.4 % mAP on PASCAL VOC 2007) are possible when applying DCNNs in a plain sliding window fashion. We describe some tricks that allow dense sliding window DCNN detection to surprisingly be faster than current two-stage approaches such as RCNN which rely on region proposal + scoring steps. Joint work with I. Kokkinos and P.-A. Savalle.

  • June 27, 2014  —  I have received a second equipment gift from NVIDIA Corporation (two Tesla K40 GPUs) for my research in deep learning. Thanks NVIDIA!

  • June 10, 2014  —  Pre-print on deep learning with mini-epitomes posted on arXiv.

  • March 1, 2014  —  Paper on image modeling and recognition with mini-epitomes to appear at CVPR 2014.

  • February 8, 2014  —  My review paper on Perturb-and-MAP appears as invited chapter in a forthcoming MIT Press book on Advanced Structured Prediction edited by S. Nowozin, P. Gehler, J. Jancsary, and C. Lampert. [pdf]

  • December 9, 2013  —  Iasonas Kokkinos, Alex Bronstein, Michael Bronstein, and myself will be teaching a full-day tutorial on June 23, 2014 at CVPR 2014. The tutorial BASIS-14 (BASes for Images and Surfaces) will cover linear and non-linear image and surface analysis methods, from fundamental concepts to state-of-the-art techniques, from the viewpoint of basis expansions.

  • November 8, 2013  —  I have received an equipment gift from NVIDIA Corporation (two Tesla K20 GPUs) for my ongoing research on deep generative models. This support is gratefully acknowledged.