Difference between revisions of "My first scientific paper"

Latest revision as of 18:21, 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, Fall 2026

Foundation AI models are universal models for solving a wide set of problems. This project proposes to investigate the theoretical properties of foundation models. The domain is 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 points; 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 foundation-model architectures to identify an optimal architecture that solves the listed problems across 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 1: / Line 1: @@
-{{#seo: |title=Research management course|titlemode=append|keywords=Research management course|description=This research management course immerses students in research activities that produce scientific papers}}
+{{#seo: |title=AI for applied scientific research|titlemode=append|keywords=Machine Learning, Signal processing, Quantum computing, Causal Inference|description=This research management course immerses students in research activities that produce scientific papers with code}}
 [[File:Miai logo1.jpeg|class=img-responsive|left|alt=My first scientific paper|link=Course_schedule]] &nbsp;
 {{Box|Title=News|Content={{News}}<!--''[[News|more]]''-->}}
-==Functional Data Analysis, 2025==
+==Causal AI Models for Spatio-Temporal Series, Fall 2026==
-'''Foundational models for science'''. The statistical analysis of spatial time series requires additional methods of data analysis. First,  we suppose time is continuous, put the state space changes <math>\frac{d\mathbf{x}}{dt}</math>, and use neural ordinary and stochastic differential equations. Second, we analyze a multivariate and multidimensional time series and use the tensor representation and tensor analysis. Third, since the time series have significant cross-correlation, we model them in the Riemannian space. Fourth, medical time series are periodic, the base model is the pendulum model, <math>\frac{d^2x}{dt^2}=-c\sin{x}</math>. We use physics-informed neural networks to approximate data. Fifth, the practical experiments involve multiple data sources. We use canonical correlation analysis with a latent state space. This space aligns the source and target spaces and generates data in the source and target manifolds. [[Functional Data Analysis|See the FDA page]].
+'''Foundation AI models''' are universal models for solving a wide set of problems. This project proposes to investigate the theoretical properties of foundation models. The domain is 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 points; 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 foundation-model architectures to identify an optimal architecture that solves the listed problems across a wide range of spatial time series. See the [[Functional Data Analysis]] page.
-==Mathematical forecasting, 2025==
+== My first scientific paper, 2027 ==
-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. [[Mathematical forecasting|See the page]]
+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]].
-== My first scientific paper ==
-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 activities. The graduate student consultant conducts the research and helps the student. 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]]
@@ Line 29: / Line 26: @@
 ===Links===
+* 2026 results [https://github.com/intsystems/m1p/blob/main-2026/README.md GitHub]
+* 2026 problems [https://github.com/intsystems/m1p/blob/main-2026/problem_list.md GitHub]
 * 2025 results [https://github.com/intsystems/m1p/tree/main-2025 GitHub]
 * 2025 [https://github.com/intsystems/m1p/tree/main-2025 The list of problems for 2025]
@@ Line 39: / Line 38: @@
 * [http://bit.ly/M1_2019_674 Group 674, spring 2019]
 * [http://bit.ly/M1_2019_694 Group 694, spring 2019]
+==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. [[Mathematical forecasting|See the page]]
 == The Art of Scientific Research ==
@@ Line 56: / Line 58: @@
 * [[Step 11|Step 11: The final talk]]
-==See also==
+==Articles==
-* [[Fundamental theorems]] of ML
+* [[Fundamental theorems]] of Machine Learning
 * [https://m1p.org/jmlda JMLDA archive]<!--|[[Todo list]]|[[Books]]|[[Reviews]]|[[Tools]]|[[Projects]]|[[Proposals]]|[[Templates]]|[[Career]]|[[Notation]]|[[Publication]]-->

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