My first scientific paper

News

Fall 2025 — Functional Data Analysis starts soon

Before 13 February 2026 — My first scientific paper: Suggest your project here

On Thursdays at 17:50 — Class m1p.org/go_zoom and discussion channel t.me

See results of 2024 — on GitHub

Functional Data Analysis, 2025

Foundational models for science. Foundation AI models are universal models to solve a wide set of problems. This project proposes to investigate the theoretical properties of foundation models. The domain to model is a spatial-time series. These data are used in various scientific disciplines and serve to generalise scientific knowledge and make forecasts. The essential problems, formulated as user requests that solve a foundation model, are forecasting and generation of time series; analysis and classification of time series; detection of change point, and causal inference. To solve these problems, the foundation AI models are trained on massive datasets. The main goal of this project is to compare various architectures of foundation models to find an optimal architecture that solves the listed problems for a wide range of spatial time series. See the FDA page.

Mathematical forecasting, 2025

This course delivers methods of model selection in machine learning and forecasting. The modeling data are videos, audio, encephalograms, fMRIs, and other measurements in natural science. The models are linear, tensor, deep neural networks, and neural ODEs. The practical examples are brain-computer interfaces, weather forecasting, and various spatial-time series forecasting. The lab works are organized as paper-with-code reports. See the page