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Introduction to data-driven dynamical systems
Introduction to data-driven dynamical systems
I am pleased to announce the second edition of graduate student workshops titled “Introduction to Data-Driven Dynamical Systems”, which will run during the Academic Year 2026–2027.
This 7-lecture series is designed to introduce you to the theory and applications of modern data-driven methods in dynamical systems, with particular emphasis on sparse identification, federated learning, and the integration of classical modeling with data science. Here is a brief schedule with the topics of each lecture.
Schedule & Topics
Lecture 1: Introduction to the Project & Team
Lecture 2: Background in Statistics and Differential Equations
Lecture 3: Data-Driven Dynamical Systems
Lecture 4: Recap & Hands-on Session on SINDy
Lecture 5: Seminars
Lecture 6: Federated Learning
Lecture 7: Hands-on Session – Federated SINDy
Key Learning Goals
During these lectures, you will
Gain insight into the transition from classical mathematical modeling to modern data science.
Understand the emerging field of science-informed machine learning, which bridges theoretical rigor with data-driven effectiveness.
Learn sparse identification methods for discovering governing equations from empirical data.
Explore federated approaches to dynamical systems modeling, preserving privacy while enabling collaborative science.
Get hands-on experience with Anaconda, Python, and Jupyter notebooks.
Practical Information
The first lecture will be Wednesday 16h September 2026 @ noon in KT 218
Please, bring your own laptop, as we will need it to install some software.
An Introduction to Data-Driven Dynamical Systems for Health
An Introduction to Data-Driven Dynamical Systems for Health
Instructor: A.M. Selvitella
Location: CRM - University of Montreal
Event: Summer School - Advanced methods in modeling for disease dynamics - Part I - Thematic Program on Mathematics for Health
Dates: Monday 3rd August 2026
Abstract
Dynamical systems provide a natural framework for describing evolving biological and clinical processes, from disease progression and treatment response to physiological regulation and population-level health dynamics. In many of these settings, however, first-principles models are incomplete or high-dimensional, while heterogeneous data are increasingly abundant. This workshop introduces data-driven approaches to discovering, approximating, and analyzing dynamical systems directly from data, with a focus on applications in health and medicine.
We will survey core techniques at the interface of dynamical systems, numerical analysis, and machine learning, including (if time permits):
- linear and nonlinear system identification from time-series data (e.g., dynamic mode decomposition, Koopman operator methods, and sparse regression for discovering governing equations);
- reduced-order modeling and dimension reduction for complex physiological and biomedical systems;
- physics-informed neural networks (PINNs) and related methods that integrate data with mechanistic dynamical models.
Illustrative examples will be drawn from applications such as modeling disease dynamics, physiological signals, and mental health progression. Practical issues (data requirements, identifiability, interpretability, and uncertainty) and interesting mathematical challenges will be emphasized.
The workshop is aimed at graduate students, postdoctoral fellows, and researchers with a background in applied mathematics, statistics, or related fields. No prior experience in system identification or machine learning will be assumed; the goal is to provide an accessible introduction and a unifying perspective on data-driven dynamical systems as a powerful tool for modern health applications.
Introduction to data-driven dynamical systems
Introduction to data-driven dynamical systems
I am pleased to announce a new series of graduate student workshops titled “Introduction to Data-Driven Dynamical Systems”, which will run during the Academic Year 2025–2026.
This 7-lecture series is designed to introduce you to the theory and applications of modern data-driven methods in dynamical systems, with particular emphasis on sparse identification, federated learning, and the integration of classical modeling with data science. Here is a brief schedule with the topics of each lecture.
Schedule & Topics
Lecture 1: Introduction to the Project & Team Slides | Wednesday 18th September 2025
Lecture 2: Background in Statistics and Differential Equations Slides | Wednesday 15th October 2025
Lecture 3: Data-Driven Dynamical Systems Slides | Wednesday 19th November 2025
Lecture 4: Recap & Hands-on Session on SINDy | Wednesday 21st January 2026
Lecture 5: Seminars | Wednesday 18th February 2026
Lecture 6: Federated Learning Slides & Homework| Wednesday 18th March 2026
Lecture 7: Hands-on Session – Federated SINDy
Key Learning Goals
During these lectures, you will
Gain insight into the transition from classical mathematical modeling to modern data science.
Understand the emerging field of science-informed machine learning, which bridges theoretical rigor with data-driven effectiveness.
Learn sparse identification methods for discovering governing equations from empirical data.
Explore federated approaches to dynamical systems modeling, preserving privacy while enabling collaborative science.
Get hands-on experience with Anaconda, Python, and Jupyter notebooks.
Practical Information
The first lecture will be Wednesday 17th September 2025 @ noon in KT 218
Please, bring your own laptop, as we will need it to install some software.
Data-Driven Dynamical Systems - Workshops
Data-Driven Dynamical Systems - Workshops
LAB 1 - Tuesday 2nd December 2025 - Alessandro Maria Selvitella
LAB 2 - Thursday 4th December 2025 - Alessandro Maria Selvitella
This Workshop is aimed at graduate students with some background in differential equations and machine learning. The workshop will cover topics such as Runge-Kutta methods, Physics Informed Neural Networks, and System Identification of Nonlinear Dynamics. These tools will be illustrated on a toy problem, a dynamical system modeling the central pattern generator of the lamprey.
[In person only or specific request must be sent to the organizers for online participation]
Data Science for the Biological Sciences Workshops
Data Science for the Biological Sciences Workshops
LAB 1 - Tuesday 2nd December 2025 - Kathleen Lois Foster & Alessandro Maria Selvitella
Background on how to use R, the use of variables to store information, how to call particular elements of a matrix, how to install and use packages.
LAB 2 - Thursday 4th December 2025 - Kathleen Lois Foster & Alessandro Maria Selvitella
Basic statistical methods and visualization. Hypothesis tests, such as t-test, ANOVAs, ANCOVAs, simple linear regression, and multiple linear regression.
Data Science for the Biological Sciences Workshops
Data Science for the Biological Sciences Workshops
LAB 1 - Tuesday 3rd December 2024 - Kathleen Lois Foster & Alessandro Maria Selvitella
Background on how to use R, the use of variables to store information, how to call particular elements of a matrix, how to install and use packages.
LAB 2 - Thursday 5th December 2024 - Kathleen Lois Foster & Alessandro Maria Selvitella
Basic statistical methods and visualization. Hypothesis tests, such as t-test, ANOVAs, ANCOVAs, simple linear regression, and multiple linear regression.
Data-Driven Dynamical Systems - Workshops
Data-Driven Dynamical Systems - Workshops
LAB 1 - Tuesday 3rd December 2024 - Alessandro Maria Selvitella
TBA
LAB 2 - Thursday 5th December 2024 - Alessandro Maria Selvitella
TBA
Data Science for the Biological Sciences Workshops
Data Science for the Biological Sciences Workshops
LAB 1 - Tuesday 28th November 2023 - Kathleen Lois Foster & Alessandro Maria Selvitella
Background on how to use R, the use of variables to store information, how to call particular elements of a matrix, how to install and use packages.
LAB 2 - Thursday 30th November 2023 - Kathleen Lois Foster & Alessandro Maria Selvitella
Basic statistical methods and visualization. Hypothesis tests, such as t-test, ANOVAs, ANCOVAs, simple linear regression, and multiple linear regression.
Data Science for the Biological Sciences Workshops
Data Science for the Biological Sciences Workshops
LAB 1 - Tuesday 29th November 2022 - Kathleen Lois Foster & Alessandro Maria Selvitella
Background on how to use R, the use of variables to store information, how to call particular elements of a matrix, how to install and use packages.
LAB 2 - Thursday 1st December 2022 - Kathleen Lois Foster & Alessandro Maria Selvitella
Basic statistical methods and visualization. Hypothesis tests, such as t-test, ANOVAs, ANCOVAs, simple linear regression, and multiple linear regression.
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