CV_Konstantinov_en.tex

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\firstname{Anton}
\familyname{Konstantinov}

\phone{+7~(952)~359-22-55}
\email{antv.konstantinov@gmail.com}
\social[github][github.com/falceeffect]{GitHub: falceeffect}
\social[vk][vk.com/const\_ant]{Vk: Anton Konstantinov}
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\begin{document}
\makecvtitle

\section{Education}
\cventry{Since 2019}{Master's degree in Applied Mathematics and Computer Science}{educational program ``Big Data and Machine Learning''}{}{ITMO University}{Expected to graduate in 2021}
\cventry{2017 -- 2019}{Student}{Computer Science Center}{Data Science track}{}{}
\cventry{2015 -- 2019}{Bachelor's degree in Applied Mathematics and Computer Science, educational program ``Computational stochastics and statistical models''}{Saint Petersburg State University}{School of Mathematics and Mechanics}{}{Diploma with distinction, GPA: 4.85, bachelor's thesis ``Estimating genomic distances in de Bruijn graphs''}

\section{Work experience}
\cventry{Since 2019}{Deep Learning research fellow}{Insilico Medicine}{}{}{Applying deep learning methods to drug discovery problems}
\cventry{Summer 2018}{Intern C\texttt{++} Developer}{Center for Algorithmic Biotechnology}{SPbU}{}{Worked on High Performance Computing SPAdes project --- a cluster version of genome assembler SPAdes based on MPI.}
\section{MOOC}
\cventry{2018}{Обучение на размеченных данных (Supervised Machine Learning) by Moscow Institute of Physics and Technology, Yandex \& ФРОО on Coursera}{}{}{}{\href{https://www.coursera.org/account/accomplishments/certificate/JWLKDHQVKEKW}{Verified certificate.}}
\cventry{2017 -- 2018}{\textit{Data Structures and Algorithms} (specialization) by University of California, San Diego \& National Research University Higher School of Economics on Coursera}{}{}{}{Courses: Algorithmic Toolbox, Data Structures, Algorithms on Graphs, Algorithms on Strings, Advanced Algorithms and Complexity. Verified certificates.}

\section{Skills and professional interests}
\cvitem{Data Science}{Data processing and analysis skills. Firm understanding of classical machine learning models and deep learning models. Hands-on experience in research \& development with such tools as NumPy, SciPy, scikit-learn, imbalanced-learn, PyTorch, PyTorch Geometric. Able to quickly understand and implement ideas presented in scientific papers.}
\cvitem{Mathematics}{Strong mathematical background. Firm knowledge in fundamental fields (abstract algebra, real analysis, probability theory). Skills in applied statistics. Basic understanding of numerical methods.}
\cvitem{Programming}{Strong overall programming skills. Firm understanding of design and analysis of algorithms. Languages of choice are Python and C++, but can quickly adapt to any modern imperative language.}
\cvitem{Python}{Over 5 years of hands-on experience in both pet-projects and research \& development tasks.}
\cvitem{C\texttt{++}}{STL, Qt, basic understanding of MPI technology. Two months of production experience in a big open-source project.}
\cvitem{Infrastructure}{Confident user of GNU/Linux, proficient with terminal tools including fully remote workflow over ssh. Familiar with version control workflow, confident user of Git. Basic understanding of containerization via Docker.}
\cvitem{Languages}{Russian (native), English (fluent).}{}{}{}{}
\cvitem{Miscellaneous}{Confident user of LaTeX.}

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