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The MIDAS training program aims to provide researchers with the requisite training to successfully apply data science techniques to environmental science research questions and strategies for integrating data science into their grant applications. Through this training program we hope to develop a research community across U-M that will advance the application of data science to environmental science, broadly defined as encompassing environmental, climate, and earth sciences; as well as ecology.

The full training program will consist of three phases: a half-day workshop, a 3-day bootcamp, and two optional follow-up sessions.

For questions, please contact Beth Uberseder ( and Jordan McKay (

Note: the registration deadline for the bootcamp was Sept. 15.

Data Science Bootcamp

Nov. 4, 11, 18, 2022, 8:30 am – 4:30 pm
Location: Weiser Hall (500 Church Street), Ann Arbor

Syllabus (subject to change):

  • Day 1 morning: Review of linear algebra and probability; Introduction to estimation and inference; Basic regression analysis; Considerations for experimental design; Functional data analysis; Introduction to High-Performance Computing.
  • Day 1 afternoon: Basics of Machine Learning (ML); Supervised ML methods; Unsupervised ML methods; Causal inference.
  • Day 2 morningGeneralized Linear Model; Generalized Estimating Equations; Multilevel regression; Nonparametric regression; Factor analysis; Dimension reduction regression.
  • Day 2 afternoon: Bayesian models; Computational algorithms; Spatial statistics; Time series analysis.
  • Day 3: (with guest speakers) Examples of environmental science research projects with data science methods; Developing research ideas and selecting appropriate data and analytical methods (all participants are welcome to present their research ideas and receive coaching from the instructors).

Who should attend:

Open to all U-M and external environmental scientists interested in the application of data science to their fields, which may include environmental, climate, and earth sciences; as well as ecology.  Faculty, staff researchers and postdocs will be given priority.

Prerequisites: Some college level math and statistics; no coding experience required.

Tuition (We will send payment instructions along with acceptance decisions):

$100 for U-M personnel 
$2,000 for external participants (limited numbers of seats available)

Cancellation Policy:

Before Oct. 20: full refund minus $50 processing fee
Between Oct. 20 and Oct. 27: 50% refund
After Oct. 27: no refund


Yang Chen
Assistant Professor of Statistics

Paramveer Dhillon Assistant Professor, School of Information

Xun Huan

Assistant Professor, Mechanical Engineering

Kerby Shedden 
Professor, Statistics

Director, Consulting for Statistics, Computing and Analytics Research

Two Follow-up Sessions

These sessions will be scheduled for the subsequent two terms after the bootcamp. These sessions will reinforce the knowledge and experiences of the previous bootcamp, while providing additional guidance in applying data science skills to problems in environmental science and developing these results into grant applications. Trainees will have opportunities to:

  • present and discuss their current work with other trainees, providing peer-to-peer support in solving common challenges and sharing best practices; and
  • receive guidance from the instructors in problem solving and further developing their research ideas for applications of data science to environmental science.

Past Events

Half-Day Workshop

March 11, 2022, from 12 to 4:30 PM

The workshop will serve both as an introduction to the program as well as a networking event. Trainees will learn about the overall scope and structure of the training program. The workshop will enable MIDAS to set expectations regarding program goals and trainee responsibilities, while trainees can ensure the program matches their career needs.

View Workshop Recording

Schedule and Details


March 11, 2022, from 12 to 4:30 PM, virtual event.

Workshop objectives:

This workshop will provide an introduction to data science from an environmental science perspective. After participating in this workshop trainees will be able to determine in which areas of environmental science the application of data science, machine learning, artificial intelligence, mechanistic models, or statistics is most appropriate, and where these techniques can be integrated into their own practice.

Who should attend:

This workshop is open to all environmental scientists interested in the application of data science to their respective field, which may include environmental, climate, and earth sciences; as well as ecology. Researchers at the level of faculty, postdoctoral fellows, and graduate students are welcome to attend, with the content being geared towards those who are interested in learning about the bootcamp and who plan to incorporate data science in their research.

What’s covered:

The workshop includes an introduction to key concepts of data science, overview of the bootcamp and other training opportunities on campus, and presentations by U-M faculty and researchers who have successfully incorporated data science into their environmental science research.

Lead Instructors

Yang Chen
Assistant Professor of Statistics

Jonathan Gryak
MIDAS Senior Scientist

View Workshop Recording

Workshop Schedule

March 11

12:00pm – 12:15pm Welcome & brief MIDAS presentation

Jing Liu, MIDAS Managing Director

12:15pm – 12:45pm Goals and general overview of training program; who can best benefit from this training?

Jonathan Gryak, MIDAS Senior Scientist

12:45pm – 1:15pm Data sciences, machine learning, artificial intelligence, mechanistic models, and statistics; similarities and differences

Yang Chen, Assistant Professor of Statistics

1:15pm – 3:30 pm Success Stories – Applications of Data Science to Environmental Science

David Fouhey, Assistant Professor Computer Science and Engineering
Ƶack Spica, Assistant Professor of Earth and Environmental Sciences
Christiane Jablonowski, Professor of Climate and Space Sciences and Engineering
Runzi Wang, Assistant Professor of Environment and Sustainability
Steven Smith, Associate Professor of Ecology and Evolutionary Biology
Stilian Stoev, Professor of Statistics

3:30pm – 4:00pm Description of bootcamp, data science training resources on campus, Q&A

Jonathan Gryak, MIDAS Senior Scientist

4:00pm – 4:30pm Open Virtual Networking

Moderators: Yang Chen, Assistant Professor of Statistics; Jonathan Gryak, MIDAS Senior Scientist