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SPIN-RSCI: 8537-1776
ORCID: 0000-0001-8188-3391
ResearcherID: X-3960-2018
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D. Vetrov
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Nadezhda Chirkova

  • Nadezhda Chirkova has been at HSE University since 2016.


  • research in the area of deep neural networks



Bachelor, Master in Applied Mathematics and Information Science
Lomonosov Moscow State University

Awards and Accomplishments

Young Faculty Support Program (Group of Young Academic Professionals)
Category "New Researchers" (2018)

Courses (2019/2020)

Courses (2018/2019)

Courses (2017/2018)

Courses (2016/2017)



11-я международная конференция Интеллектуализация обработки информации (Барселона). Presentation: Additive Regularization for Hierarchical Multimodal Topic Modeling

Employment history

Junior machine learning researcher, Antiplagiat JSC, 2016 July→2016 August. Developing a prototype of domain specific search system that incorporates hierarchical topic structure learned from the domain data.

Machine Learning Teacher Assistant, Coursera machine learning specialization, 2016 January→2017 March. Developing practical assignments for the students explaining how machine learning algorithms work.

Timetable for today

Full timetable

The faculty presented the results of their research at the largest international machine learning conference NeurIPS

Researchers of the Faculty of Computer Science presented their papers at the annual conference of Neural Information Processing Systems (NeurIPS), which was held from 2 to 8 December 2018 in Montreal, Canada.

Faculty of Computer Science Staff Attend International Conference on Machine Learning 

On August 6-11 the 34th International Conference on Machine Learning was held in Sydney, Australia. This conference is ranked A* by CORE, and is one of two leading conferences in the field of machine learning. It has been held annually since 2000, and this year, more than 1,000 participants from different countries took part.

'Machine Learning Algorithm Able to Find Data Patterns a Human Could Not'

In December 2016, five new international laboratories opened up at the Higher School of Economics, one of which was the International Laboratory of Deep Learning and Bayesian Methods. This lab focuses on combined neural Bayesian models that bring together two of the most successful paradigms in modern-day machine learning – the neural network paradigm and the Bayesian paradigm.