Nadezhda ChirkovaCurriculum Vitae

H (+7) 968 472 56 80B nadiinchi@gmail.compersonal page at hse.ru

Education2016–2018 Master’s Degree in Applied Mathematics and Computer Science,

Lomonosov Moscow State University, Moscow, Russia (with honours)scientific advisor: Dmitry Vetrov.

2012–2016 Bachelor’s Degree in Applied Mathematics and Computer Science,Lomonosov Moscow State University, Moscow, Russia (with honours)scientific advisor: Konstantin Vorontsov.

Work experienceJan, 2018–present

Junior research fellow, National Research University Higher Schoolof Economics, Samsung-HSE Laboratory, Moscow, RussiaSupervisor: Dmitry Vetrov.

Jan–Dec,2017

Research intern and manager, National Research University HigherSchool of Economics, International Laboratory of Deep Learning and BayesianMethods, Moscow, RussiaSupervisor: Dmitry Vetrov.

Jul–Aug,2016

Research Intern, JSC Antiplagiat, Moscow, Russia.

Publications2018 Lobacheva E., Chirkova N., Vetrov D. Bayesian Sparsification of Gated Recurrent

Neural Networks, NIPS 2018 Workshop on Compact Deep Neural Networks withindustrial applications.

2018 Chirkova N., Lobacheva E., Vetrov D. Bayesian Compression for Natural LanguageProcessing, Proceedings of the 2018 Conference on Empirical Methods in NaturalLanguage Processing. Association for Computational Linguistics.

2017 Lobacheva E., Chirkova N., Vetrov D. Bayesian Sparsification of Recurrent NeuralNetworks, ICML 2017 Workshop on Learning to Generate Natural Language.

2016 Chirkova N., Vorontsov K. Additive Regularization for Hierarchical Multimodal TopicModeling, Journal of machine learning and data analysis, Vol. 2. No. 2. P. 187-200.

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Teaching experienceFall 2018 Machine learning (lectures and practical sessions), Higher School of Eco-

nomics, Faculty of Computer Science.Fall 2018 Bayesian methods in data analysis (lectures and practical sessions), Higher

School of Economics, Faculty of Computer Science (co-teacher with AleksandrGrishin).

Spring andfall, 2018

Deep learning (practical sessions), Lomonosov Moscow State University,Faculty of Computational Mathematics and Cybernetics, Moscow Institute ofPhysics and Technology, Department of Control and Applied Mathematics(co-teacher with Oleg Ivanov and Evgeniy Nizhibitsky).

Spring,2016–2018

Machine learning (practical sessions), Higher School of Economics,Intellectual Data Analysis minor.

fall,2016–2017

Machine learning (practical sessions), Higher School of Economics,Faculty of Computer Science.

Research projects2018–present Connectivity of local optimas in deep learning

With Ekaterina Lobacheva, scientific advisor: Dmitry Vetrov.2016–2018 Bayesian sparsification of recurrent neural networks

With Ekaterina Lobacheva, scientific advisor: Dmitry VetrovMaster’s thesis, publication at EMNLP 2018, ICML 2017 workshop paper and NIPS2018 workshop paper .

2014–2016 Hierarchical topic modellingScientific advisor: Konstantin VorontsovBachelor’s thesis, implemented proposed method in open-source python libraryBigARTM, publication in JMLDA.

Awards and achievements2018 Young Faculty Support Program (Group of Young Academic Professionals), Higher

School of Economics.June 2016 Best Bachelor’s thesis award, Faculty of Computational Mathematics and Cybernetics,

Lomonosov Moscow State University.June, 2016 Best research presentation award, Summer School on Optimization, Control and

Information.Fall 2013,

Spring 2016,Fall 2016,Fall 2017

Increased academic scholarship, Lomonosov Moscow State University.

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Scientific interests

{ Machine learning{ Deep learning

{ Natural language processing{ Bayesian deep learning

Skills

Deep learning PyTorch, Keras, TheanoMachinelearning

Numpy/Scipy, Pandas, Scikit-learnVersioncontrol

Git

Markup LaTeX

LanguagesRussian mother tongueEnglish upper intermediate

Other information2017, 2018 Organization manager of Summer School on Deep Learning and Bayesian

Methods, Moscow, Russia (running all the event organization, with EkaterinaLobacheva and Ekaterina Volzhenina, head of the school: Dmitry Vetrov).

2016 Teacher assistant at Coursera Specialization “Machine Learning and DataAnalysis” by MIPT and Yandex (developing assignments and tests).

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