Upcoming Events

Developing Online Learning Experiments Using Doenet (2023)

Apply to attend


In this five-day workshop, participants will learn how to create and implement online learning experiments using the Distributed Open Education Network (Doenet, doenet.org). Doenet is designed to help faculty critically evaluate how different content choices influence student learning in their classrooms. Doenet enables instructors to quickly test hypotheses regarding the relative effectiveness of alternative approaches by providing tools to assign different variations of an activity and analyze the resulting data.

Following brief introductions and demos of features of the Doenet platform, participants will work in small groups to develop learning experiments that can be used in the college classroom, assisted by the developers of Doenet. The expectation is that participants will leave the workshop with a learning experiment that they can use in their classroom the following year.

The workshop will run from 9 AM on Monday, May 22 through 4 PM on Friday, May 26. All organized activities will occur between 9 AM and 4 PM each day.

The workshop is open to faculty at all levels teaching STEM courses.

To apply, please submit the following documents through the Program Application link at the top of the page:

  1. A personal statement briefly (200 words or less) stating what you hope to contribute to the discussion on learning experiments and what you hope to gain from this workshop. Include courses you teach for which you'd like to develop learning experiments. Priority will be given to those able to run learning experiments in their courses in the following year.
  2. A brief CV or resume. (A list of publications is not necessary.)

This workshop is fully funded by the National Science Foundation. All accepted participants who request funding for travel and/or local expenses will receive support. There is no registration fee.

Participants who perform learning experiments on Doenet during the following academic year will be eligible to receive a small stipend to support their work.

Supported by NSF grant DUE 1915363.

Workshop on Random Structures in Optimizations and Related Applications

Applications due April 30.


  • This summer program aims to promote the studies and research activities on random optimizations in complex systems for Minnesota's local undergraduate students.
  • The workshop will cover a wide range of subjects and tools in probability theory and mathematical physics, especially addressing their applications in machine learning, data science, and imaging processing.
  • During the 10-day program, students are expected to attend two daily lecture sessions and a group problem session. Additional professional development sessions will discuss graduate school and careers in related fields.
  • Upon completion, students will receive a certificate issued by the School of Mathematics at the University of Minnesota.

Who can apply

Undergraduate students from Minnesota's local colleges and universities. 


Introductory Probability, Linear Algebra, and Basic Properties of Differential Equations


Week 1: June 5-9

Time Instructor Topic
9-10:15am Wei-Kuo Chen Statistical Physics and Random Optimizations
10:45am-12pm Arnab Sen Clustering and Community Detection
1:30-3:30pm Ratul Biswas Discussion and Problem Session

Week 2: June 12-16

Time Instructor Topic
9:00-10:15am Rishabh Dudeja Universality in High-Dimensional Optimization and Statistics Detection
10:45am-12:00pm Wonjun Lee Introduction to Computational Optimal Transport
1:30-3:30pm Heejune Kim Discussion and Problem Session


Application materials:

  1. A brief CV
  2. A short recommendation letter from a professor
  3. Personal statement describing scientific interests and course preparations for this workshop

When filling in the Application Form, please only select either "Local expenses (hotel and meals)" or "Not requesting funding."

Apply by April 30

Financial support

The participants will receive either a fixed per diem or a meal plan to cover food. Support is available for students in need of on-campus lodging during the program.


Wei-Kuo Chen (University of Minnesota)

This program is financially supported by the National Science Foundation and Institute for Mathematics and Its Applications. 

Math-to-Industry Boot Camp VIII

Applications due March 17.


The Math-to-Industry Boot Camp is an intense six-week session designed to provide graduate students with training and experience that is valuable for employment outside of academia. The program is targeted at Ph.D. students in pure and applied mathematics. The boot camp consists of courses in the basics of programming, data analysis, and mathematical modeling. Students work in teams on projects and are provided with training in resume and interview preparation as well as teamwork.

There are two group projects during the session: a small-scale project designed to introduce the concept of solving open-ended problems and working in teams, and a "capstone project" that is posed by industrial scientists. Recent industrial sponsors included Cargill, Securian Financial and CH Robinson. 

Weekly seminars by speakers from many industry sectors provide the students with opportunities to learn about a variety of possible future careers.


Applicants must be current graduate students in a Ph.D. program at a U.S. institution during the period of the boot camp.


The program will take place online. Students will receive a $3,000 stipend.


To apply, please supply the following materials:

  • Statement of reason for participation, career goals, and relevant experience
  • Evidence of good standing, and have full-time status
  • Letter of support from advisor, director of graduate studies, or department chair

Apply by March 17

Selection criteria will be based on background and statement of interest, as well as geographic and institutional diversity. Women and minorities are especially encouraged to apply. Selected participants will be contacted in April.