Ira Ktena

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Research Scientist @ Twitter || Data Science Consultant, Data Science Consultant at Imperial Consultants (ICON), Visiting Scientist at Harvard Medical School from 2017-2018, Scientific Project Officer at IMAGE ANALYSIS, GROUP from 2015- 2017, Scientific Programmer / Research Assistant at Imperial College London from 2013-2015, Software Developer / Research Assistant at Forecasting & Strategy Unit from 2008-2012

Featured Co-authors

Daniel Rueckert
99 publications
Pushmeet Kohli
87 publications
Oriol Vinyals
77 publications
Ben Glocker
56 publications
Bernhard Kainz
46 publications
Yujia Li
26 publications
Lucas Theis
21 publications
Michael Bronstein
19 publications
Enzo Ferrante
18 publications
Ferenc Huszár
18 publications
Sarah Parisot
18 publications

Solving Mixed Integer Programs Using Neural Networks

Mixed Integer Programming (MIP) solvers rely on an array of sophisticate...
12/23/2020 ∙ by Vinod Nair, et al. ∙ 9 ∙ share

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Deep Bayesian Bandits: Exploring in Online Personalized Recommendations

Recommender systems trained in a continuous learning fashion are plagued...
08/03/2020 ∙ by Dalin Guo, et al. ∙ 5 ∙ share

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Model Size Reduction Using Frequency Based Double Hashing for Recommender Systems

Deep Neural Networks (DNNs) with sparse input features have been widely ...
07/28/2020 ∙ by Caojin Zhang, et al. ∙ 13 ∙ share

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Privacy-Preserving Recommender Systems Challenge on Twitter's Home Timeline

Recommender systems constitute the core engine of most social network pl...
04/28/2020 ∙ by Luca Belli, et al. ∙ 9 ∙ share

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Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction

One of the challenges in display advertising is that the distribution of...
07/15/2019 ∙ by Ira Ktena, et al. ∙ 1 ∙ share

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Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity

Graph convolutional networks (GCNs) allow to apply traditional convoluti...
06/05/2018 ∙ by Salim Arslan, et al. ∙ 10 ∙ share

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Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease

Graphs are widely used as a natural framework that captures interactions...
06/05/2018 ∙ by Sarah Parisot, et al. ∙ 0 ∙ share

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DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

We present DLTK, a toolkit providing baseline implementations for effici...
11/18/2017 ∙ by Nick Pawlowski, et al. ∙ 0 ∙ share

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Exploring Heritability of Functional Brain Networks with Inexact Graph Matching

Data-driven brain parcellations aim to provide a more accurate represent...
03/29/2017 ∙ by Ira Ktena, et al. ∙ 0 ∙ share

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Spectral Graph Convolutions for Population-based Disease Prediction

Exploiting the wealth of imaging and non-imaging information for disease...
03/08/2017 ∙ by Sarah Parisot, et al. ∙ 0 ∙ share

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Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

Evaluating similarity between graphs is of major importance in several c...
03/07/2017 ∙ by Ira Ktena, et al. ∙ 0 ∙ share

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Comparison of Brain Networks with Unknown Correspondences

Graph theory has drawn a lot of attention in the field of Neuroscience d...
11/15/2016 ∙ by Ira Ktena, et al. ∙ 0 ∙ share

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