I joined DeepMind in April 2019 to work in the machine learning group of Nando de Freitas. Before that, I was a member of EPFL's CVLab since 2014, where I did my PhD supervised by Prof. Pascal Fua and Prof. Raphael Sznitman. In autumn 2017 during my internship at Google Research in Zurich I had a chance to work with Vittorio Ferrari and Jasper Uijlings.
I obtained my M.Sc. degree in Algorithms and Machine Learning from University of Helsinki. During that time, I also worked as a research assistant in the CoSCo group at HIIT. Before, I studied in Russia at the Higher School of Economics in the faculty of Business Informatics and Applied Mathematics.
During my PhD I worked on active learning (AL) for different classification tasks. Given a pool of unlabelled data, the goal of AL is to select which data should be annotated in order to learn the model as quickly as possible. In practice, this means that instead of asking experts to annotate everything, we decide iteratively and adaptively which datapoints should be labelled in priority. Many AL strategies could be proposed, without any of them clearly outperforming others. It led me to meta-learning approach to active learning, where a strategy is learnt from an ensemble of multiple previous AL problems. Most recently, I became interested in meta-learning approaches to different machine learning problems.
April, 2019: I started a new job as a Research Scientist at DeepMind!
January, 2019: We finished work on our new manuscript Discovering General-Purpose Active Learning Strategies with its code.
January, 2019: Our article Geometry in Active Learning for Binary and Multi-class Image Segmentation was published in Computer Vision and Image Understanding journal.
October, 2018: I defended my PhD thesis entitle Learning to Reduce Annotation Load. Youuuhoo! Thanks everyone who supported me during these years!