Experience

  1. Graduate Student Researcher

    Optimization and Machine Learning Lab at KAUST

  2. Research Intern

    Optimization and Machine Learning Lab at KAUST
    • Conducted independent theoretical research on Stochastic Coordinate Descent algorithms, which formed the foundation of my bachelor’s thesis.

  3. Junior Researcher in the Research Group of Randomized Algorithms for Distributed Optimization Problems

    Laboratory of Advanced Combinatorics and Network Applications at MIPT
    • Conducted research under a dedicated grant focusing on randomized algorithms for Machine Learning.

Education

  1. PhD in Applied Mathematics and Computational Science

    King Abdullah University of Science and Technology
    Supervisor: Peter Richtárik;
    GPA: 3.89/4.00 (Transcript);
    Selected Courses: Private Data Analysis, Reinforcement Learning.

  2. MS in Applied Mathematics and Computational Science

    King Abdullah University of Science and Technology
    Supervisor: Peter Richtárik;
    GPA: 3.63/4.00 (Transcript);
    Thesis: Non-convex Stochastic Optimization With Biased Gradient Estimators
    Selected Courses: Deep Learning, Numerical Linear Algebra, SGD Methods, Stochastic Processes, Federated Learning, Algorithms, Stochastic Numerical Simulation.

  3. MS in Applied Mathematics and Physics

    Moscow Institute of Physics and Technology
    Supervisor: Alexander Gasnikov;
    GPA: 3.89/4.00;
    Thesis: Distributed Nonconvex Stochastic Optimization With Gradient Compression;
    Selected Courses: Bayesian Methods of Statistical Estimation, Combinatorics.

  4. BS in Applied Mathematics and Physics

    Moscow Institute of Physics and Technology
    Supervisor: Peter Richtárik;
    GPA: 3.49/4.00;
    Thesis: Stochastic Coordinate Descent Method with Arbitrary Sampling.
    Selected Courses: Discrete Mathematics, Calculus, Linear Algebra, Functional Analysis, Probability Theory, Stochastic Processes, Statistics, Optimization, Machine Learning.
Skills & Hobbies
Programming
Python
C/C++
Bash
Scripting & Markup Languages
SQL
LaTeX
Markdown
Frameworks & Libraries
PyTorch
Matplotlib
NumPy
SciPy
Development Environments
VScode
Jupyter
Slurm
Obsidian
Awards
Neural Networks and Deep Learning
Coursera ∙ November 2023
I studied the foundational concept of neural networks and deep learning. By the end, I was familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.
Blockchain Fundamentals
edX ∙ July 2023

Learned:

  • Synthesize your own blockchain solutions
  • Gain an in-depth understanding of the specific mechanics of Bitcoin
  • Understand Bitcoin’s real-life applications and learn how to attack and destroy Bitcoin, Ethereum, smart contracts and Dapps, and alternatives to Bitcoin’s Proof-of-Work consensus algorithm
Object-Oriented Programming in R
datacamp ∙ January 2023
Object-oriented programming (OOP) lets you specify relationships between functions and the objects that they can act on, helping you manage complexity in your code. This is an intermediate level course, providing an introduction to OOP, using the S3 and R6 systems. S3 is a great day-to-day R programming tool that simplifies some of the functions that you write. R6 is especially useful for industry-specific analyses, working with web APIs, and building GUIs.
See certificate
Languages
100%
Russian
80%
English