About

Biography


I am currently a Research Scientist at the Samsung AI Center in Cambridge and a Research Assistant / PhD student in the Department of Computing at Imperial College London, within the iBug group. I work on automatic facial affect estimation, a field which bridges the gap between Computer Vision and Machine Learning. After contributing to facial landmark detection using Active Appearance Models I have shifted my focus to Machine Learning using tensors.

I created TensorLy, a high-level API for tensor methods and deep tensorized neural networks in Python that aims at making tensor learning simple and accessible.It allows to easily perform tensor decomposition, tensor learning and tensor algebra. Its backend system allows to seamlessly perform computation with NumPy, MXNet, PyTorch, TensorFlow or CuPy, and run methods at scale on CPU or GPU. It is open-source under BSD licensed, making it suitable for both academic and industrial applications.

Prior to my current position, I obtained a Masters in Advanced Computing from Imperial College London. I also hold a French Engineering diploma / MSc in Applied Mathematics, Computing and Finance and obtained a BSc in advanced mathematics in parallel.

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News

Projects

Publications

G. G. Chrysos, J. Kossaifi and S. Zafeiriou, Robust Conditional Generative Adversarial Networks, in CoRR, 2018
J. Kossaifi, L, Tran, Y. Panagakis and M. Pantic, GAGAN: Geometry-Aware Generative Adverserial Networks, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
G. Dhillon, K. Azizzadenesheli, Z.C.Lipton, J. Bernstein, J. Kossaifi, A.Khanna, A.Anandkumar, Stochastic activation pruning for robust adversarial defense, in ICLR, 2018
J. Kossaifi, Y. Panagakis, A. Anandkumar, M. Pantic, TensorLy: Tensor Learning in Python, in arxiv preprint, 2018
J. Kossaifi, G. Tzimiropoulos, S. Todorovic and M. Pantic, AFEW-VA database for valence and arousal estimation in-the-wild, in Image and Vision Computing, 2017
J. Kossaifi, Z. Lipton, A. Khanna, T. Furlanello, A. Anandkumar, Tensor Regression Networks, in CoRR, 2017
J. Kossaifi, A. Khanna, Z. Lipton, T. Furlanello, A. Anandkumar, Tensor Contraction Layers for Parsimonious Deep Nets, in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 2017
J. Kossaifi, G. Tzimiropoulos and M. Pantic, Fast and exact Newton and Bidirectional fitting of Active Appearance Models, in IEEE Transactions on Image Processing (TIP), accepted for publication, 2016
J. Shen, S. Zafeiriou, G. Chrysos, J. Kossaifi, G. Tzimiropoulos and M. Pantic, The First Facial Landmark Tracking in-the-Wild Challenge: Benchmark and Results, in Proceedings of IEEE International Conference on Computer Vision, 300 Videos in the Wild (300-VW): Facial Landmark Tracking in-the-Wild Challenge & Workshop (ICCVW'15), December 2015
J. Kossaifi, G. Tzimiropoulos and M. Pantic, Fast and exact Bi-directional Fitting of Active Appearance Models, in Proceedings of the IEEE Int’l Conf. on Image Processing (ICIP’15), September 2015
A. Abraham, F. Pedregosa, M. Eickenberg, P. Gervais, A. Mueller, J. Kossaifi, A. Gramfort, B. Thirion and G. Varoquaux, Machine Learning for Neuroimaging with Scikit-Learn, in Frontiers in Neuroinformatics, 2014
J. Kossaifi, G. Tzimiropoulos and M. Pantic, Fast Newton Active Appearance Models, in Proceedings of the IEEE Int’l Conf. on Image Processing (ICIP’14), October 2014