Difference between revisions of "My first scientific paper"

Latest revision as of 16:11, 8 February 2026

News

12 February 2026 — My first scientific paper: Telegram Channel

12 February 2026 — My first scientific paper: The course m1p starts at m1p.org/go_zoom

Before 16 February 2026 — My first scientific paper: Suggest your project here

On Thursdays at 16:10 — Class m1p.org/go_zoom and discussion channel t.me

See results of 2025 — on GitHub

Fall 2026 — Functional Data Analysis starts in a while

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. 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 schedule.

Links

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

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=Research management course|link=Course_schedule]] &nbsp;
+[[File:Miai logo1.jpeg|class=img-responsive|left|alt=My first scientific paper|link=Course_schedule]] &nbsp;
 {{Box|Title=News|Content={{News}}<!--''[[News|more]]''-->}}
-== m1p Week 1 ==
+== My first scientific paper ==
-* [https://www.youtube.com/live/c3eiTdWVepo Video]
+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]].
-* [[Week 1|Homework]]
-* [https://forms.gle/FEJ28KEjxdj6Zgha7 Fill in the form]
-* [[Week 2|Homework next week]]
-== m1p Course progress==
-This course produces student research papers. It gathers research teams. Each team joins 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 road to the goal. The projects start in February and end in May, according to the [[Course schedule|schedule]].
-* [https://github.com/intsystems/m1p/tree/main-2025 List of problems for 2025 is in progress until March 20th.]
-*[[Course schedule]], Spring 2025
 *[[Week 0|Week 0: Sign up]]
 *[[Week 1|Week 1: Set the toolbox]]
-<!-- ** [https://forms.gle/FEJ28KEjxdj6Zgha7 Questionnaire 1 - '''Imagine your project'''] -->
+*[[Week 2|Week 2: Tell about your project]]
-*[[Week 2|Week 2: Select your project and tell about it]]
-<!--** [https://forms.gle/tmGNe6pbVHtr4cZb6 Questionnaire 2 - '''Check your terminology''']-->
 *[[Week 3|Week 3: State your problem]]
 *[[Week 4|Week 4: Plan the experiment]]
@@ Line 30: / Line 19: @@
 *[[Week 10|Week 10: Select a journal to submit]]
 *[[Week 11|Week 11: Prepare your presentation]]
-*[http://www.youtube.com/watch?v=xW_lXGn1WHs Week 12: Show your results (Youtube)]
+*[https://www.youtube.com/watch?v=uwcbMJamBbM Week 12: Show your results (Youtube)]
+<!-- [http://www.youtube.com/watch?v=xW_lXGn1WHs Week 12: Show your results (Youtube)] -->
-<b>LINKS</b>
-*2024 results [https://github.com/intsystems/m1p/tree/main-2024 GitHub]
-*2024 problems [https://github.com/intsystems/m1p/blob/main-2024/problem_list.md  GitHub]
-*2023 problems [https://github.com/intsystems/m1p/blob/main-2023/problem_list.md  GitHub]
-*2022 results [https://github.com/Intelligent-Systems-Phystech/m1p_2022 GitHub]
-*Telegram: [https://t.me/m1p_org discussion] <b> Ask here! </b>
-*The meeting room: [https://m1p.org/go_zoom m1p.org/go_zoom]
-*More courses from the [http://m1p.org/is Intelligent Systems]
-<!--
-*Check-1 and check-2 sing-in: [https://docs.google.com/spreadsheets/d/1g9ud_qyHJIkzHYWFRac_WmeVZ7Qv11N_OIXAzK5iOs0/edit#gid=298225191 table]
-*Stepik: [https://stepik.org/course/90240/syllabus peer-review and quiz]
-*Machinelearning: [http://www.machinelearning.ru/wiki/index.php?title=%D0%9C%D0%BE%D1%8F_%D0%BF%D0%B5%D1%80%D0%B2%D0%B0%D1%8F_%D0%BD%D0%B0%D1%83%D1%87%D0%BD%D0%B0%D1%8F_%D1%81%D1%82%D0%B0%D1%82%D1%8C%D1%8F_%28%D0%BB%D0%B5%D0%BA%D1%86%D0%B8%D0%B8_%D0%B8_%D0%BF%D1%80%D0%B0%D0%BA%D1%82%D0%B8%D0%BA%D0%B0%2C_%D0%92.%D0%92._%D0%A1%D1%82%D1%80%D0%B8%D0%B6%D0%BE%D0%B2%29/%D0%93%D1%80%D1%83%D0%BF%D0%BF%D1%8B_874%2C_821%2C_813%2C_%D0%B2%D0%B5%D1%81%D0%BD%D0%B0_2021 group table]
--->
-<!--
+===Links===
-<b>CONTENTS</b>
+* 2025 results [https://github.com/intsystems/m1p/tree/main-2025 GitHub]
-[[Fundamental theorem]]|[[Todo list]]|[[Books]]|[[Reviews]]|[[Tools]]|[[Projects]]|[[Proposals]]|[[Templates]]|[[Career]]|[[Notation]]|[[Publication]]
+* 2025 [https://github.com/intsystems/m1p/tree/main-2025 The list of problems for 2025]
--->
+* 2024 results [https://github.com/intsystems/m1p/tree/main-2024 GitHub]
+* 2024 problems [https://github.com/intsystems/m1p/blob/main-2024/problem_list.md  GitHub]
-<b>HISTORY</b>
+* 2023 problems [https://github.com/intsystems/m1p/blob/main-2023/problem_list.md  GitHub]
+* 2022 results [https://github.com/Intelligent-Systems-Phystech/m1p_2022 GitHub]
 * [http://www.machinelearning.ru/wiki/index.php?title=M1 Main page with old homework]
 * [http://bit.ly/m1p_2020  Group 674, 694, spring 2020]
@@ Line 58: / Line 34: @@
 * [http://bit.ly/M1_2019_694 Group 694, spring 2019]
-== The Art of Scientific Research ==
+==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.
-'''See you this [https://m1p.org/go_zoom Saturday at 11:10 m1p.org/go_zoom]'''
-The goal is to select and prepare the research topic of your dream. We must be sure that the problem statement and project planning lead you to successful delivery.
+==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|The course syllabus]]
+== The Art of Scientific Research ==
-** [https://www.youtube.com/watch?v=Vz67fVTQoaE&list=PLk4h7dmY2eYEA8lKRpk5Fy5yLyGqdED9I Youtube playlist]
+<!--'''See you this [https://m1p.org/go_zoom Saturday at 11:10 m1p.org/go_zoom]'''-->
-** [https://github.com/vadim-vic/the-Art-homework Repository template]
+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 [[The Art of Scientific Research|to the syllabus]]. The repository template [https://github.com/vadim-vic/the-Art-homework helps].
 * [[Step 0|Step 0: We start]]
 * [[Step 1|Step 1: Highlight your work]]
@@ Line 74: / Line 50: @@
 * [[Step 5|Step 5: Deliver your message: slides 2 and 3]]
 * [[Step 6|Step 6: Risk management in research planning]]
-* [[Step 7|Step 7: Yield foundation of your research]]
+* [[Step 7|Step 7: Yield the foundation of your research]]
 * [[Step 8|Step 8: Descriptive tools for your problem]]
-* [[Step 9|Step 9: Launch your project with foundation and statement]]
+* [[Step 9|Step 9: Launch your project with reasoning and statement]]
 * [[Step 10|Step 10: Computational experiment and visualizing]]
 * [[Step 11|Step 11: The final talk]]
+==Articles==
+* [[Fundamental theorems]] of Machine Learning
+* [https://m1p.org/jmlda JMLDA archive]<!--|[[Todo list]]|[[Books]]|[[Reviews]]|[[Tools]]|[[Projects]]|[[Proposals]]|[[Templates]]|[[Career]]|[[Notation]]|[[Publication]]-->
+<!--bottom-matter---------------------------------------><!--
-==Mathematical forecasting, 2024==
-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 course page]]
-==Functional Data Analysis, 2024==
-The statistical analysis of spatial time series requires additional methods of data analysis. First,  we suppose time is continuous, put to 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 latent state space. This space aligns the source and target spaces and generates data in source and target manifolds. [[Functional Data Analysis|See the course page]].
-<!--*[http://bit.ly/m1p_file2discuss Upload a file to discussion]
-*All questions to <strong>mlalgorithms [at] gmail [dot] com,</strong>
 <strong>MediaWiki has been installed.</strong>
 Consult the [https://www.mediawiki.org/wiki/Special:MyLanguage/Help:Contents User's Guide] for information on using the wiki software.