Nikita Doikov

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I do research in Computational Mathematics with a focus on Numerical Optimization and Machine Learning.

Now I am a postdoctoral researcher at EPFL, Switzerland, working in the Machine Learning and Optimization Laboratory with Martin Jaggi.

I am excited to explore provably efficient optimization algorithms that exploit the problem structure and combine ideas from various fields. One of my areas of expertise is second-order methods and its global complexity bounds. I believe that bridging the gap between the second-order optimization theory and the best known computational practices is what can lead us to new achievements in the training process of our models.

More broadly, I am interested in pursuing the following areas: I defended my PhD in 2021 at UCLouvain, Belgium, supervised by Yurii Nesterov. My thesis is "New second-order and tensor methods in Convex Optimization".

I received a BSc degree in Computational Mathematics and Cybernetics from Lomonosov Moscow State University in 2015. I obtained a MSc degree from Higher School of Economics in 2017, where I was studying advanced statistical and optimization methods.


Preprints / Various

Refereed Conference Publications

Journal Publications

Recent Talks