Difference between revisions of "My first scientific paper"

Revision as of 18:05, 9 September 2026

News

Fall 2026 on Thursdays 10:30 — Functional Data Analysis starts, Telegram Channel

12 February 2027 — My first scientific paper: Telegram Channel

12 February 2027 — My first scientific paper: The course m1p starts at m1p.org/go_zoom

Before 16 February 2027 — 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 2025 — on GitHub

Causal AI Models for Spatio-Temporal Series, 2026 fall

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 spatiotemporal 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 a foundation model solves, 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 Functional Data Analysis page.

My first scientific paper, 2027

This course produces student research papers. It gathers research teams. Each team consists of a student, a consultant, and an expert. The student is a project driver who wants to plunge into scientific research. The graduate student consultant conducts the research and provides help. The expert, a professor, states the problem and enlightens the way to the goal. The projects start in February and end in May, according to the schedule.

Mathematical forecasting, 2026

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

The Art of Scientific Research

The goal is to select and prepare the research topic of your dreams. We must be sure that the problem statement and project planning lead you to successful delivery according to the syllabus. The repository template helps.

Articles

Fundamental theorems of Machine Learning
JMLDA archive

@@ Line 4: / Line 4: @@
 {{Box|Title=News|Content={{News}}<!--''[[News|more]]''-->}}
-== My first scientific paper ==
+==Causal AI Models for Spatio-Temporal Series, 2026 fall==
-This course produces student research papers. It gathers research teams. Each team consists of a student, a consultant, and an expert. The student is a project driver who wants to plunge into scientific research. The graduate student consultant conducts their research and helps. The expert, a professor, states the problem and enlightens the way to the goal. The projects start in February and end in May, according to the [[Course schedule|schedule]].
+'''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 spatiotemporal 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 a foundation model solves, 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 [[Functional Data Analysis]] page.
+== My first scientific paper, 2027 ==
+This course produces student research papers. It gathers research teams. Each team consists of a student, a consultant, and an expert. The student is a project driver who wants to plunge into scientific research. The graduate student consultant conducts the research and provides help. The expert, a professor, states the problem and enlightens the way to the goal. The projects start in February and end in May, according to the [[Course schedule|schedule]].
 *[[Week 0|Week 0: Sign up]]
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 * [http://bit.ly/M1_2019_674 Group 674, spring 2019]
 * [http://bit.ly/M1_2019_694 Group 694, spring 2019]
-==Causal AI Models for Spatial-Time Series==
-'''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 [[Functional Data Analysis]] page.
 ==Mathematical forecasting, 2026==

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