1. Hanchao Wang | Decomposition Theorem for Log-Concave Canonical Process
2. Viktoria Kurzhumova | Stopping times
3. Nikita Chuikin | Application of changepoint detection theory to brain encephalogram data
4. Artem Lokotchenko | AI-engine based option pricer
5. Polina Troshina | Introduction to changepoint detection theory
6. Arseniy Loskutnikov | Markov, Chebyshev, Chernov inequalities, their connections to Kullback-Leibner distance and entropy
7. Mathew Benevolenskii | Autocallable Notes: Market Incompleteness, Martingale Measures and Model Risk
8. Timofey Maximov | TBA
9. Denis Belomestny | TBA

TBA

1. Reinforcement Learning (Maxim Medvedev, Egor Slyachkus).
2. Connection between exponential distribution and setting up uniform at [0, +∞) (Polina Troshina).
3. Connection between Binomial and Hypergeometric distributions for large N (Damir Tyrishkin).
4. Exact derivation of confidence interval for population proportion (Zagir).
5. Maximum likelyhood estimation for stochastic processes (Zoe Chesnokova).
6. Markov->Chebyshev->Chernov inequalities, their connections to Kullback-Leibner distance and entropy (Arseniy Loskutnikov)
7. Least-squares Monte-Carlo method (Ekaterina Khvatkina).
8. The concept of sufficient statistic (Ekaterina Kupreeva).
9. Convergence of stochastic processes.
10. Fourier transforms and their applications.
11. Glivenko-Kantelli theorem.
12. Laplace and Esscher transforms.
13. Copula method.
14. Random forests in machine learning and connection of their loss function, complexity with enrtory.
15. Heat equation, transfer equation and their applications.
16. CLT for Binomial distribution. De Moivre–Laplace theorem.
17. Kolmogorov equations via CLT. Lindeberg's condition.
18. Monge-Kantorovich Problem.
19. Stochastic optimal control. Zermelo's navigation problem.
20. Bootstrapping.
21. The concepts of VaR and CVaR (Expected shortfall): finance background and mathematical models.