About
What is AIDMM-NRT program?
This NRT program is led by Michigan State University’s Department of Computational Mathematics, Science and Engineering in collaboration with faculty members from the Department of Mathematics, the Department of Statistics and Probability, the Department of Biochemistry and Molecular Biology, the Department of Physics and Astronomy, and the Institute for Cyber-Enabled Research. It establishes a world-class research and training program across these units to Harness the data revolution to enable predictive multi-scale modeling across STEM. The program will address two of NSF’s 10 Big Ideas namely HDR and AI. The dual research and training thrusts will:
(a) Develop predictive multi-scale models by novel structure preserving machine learning methods in core areas:
(1) complex fluids
(2) polymeric materials
(3) biophysics
(4) Image analysis
(b) Create an innovative, flexible, and dynamic environment for interdisciplinary graduate education that emphasizes scientific writing and communication, critical thinking, and career development;
(c) Develop a cross-department curriculum including core courses in structure preserving ML, HPC, and scientific communication, as well as targeted short courses in cutting-edge topics.
Our trainee cohort will include graduate students from the member departments, but the components of the program will be open to students from other science and engineering disciplines. The program features several innovative aspects. Graduate assistantships funded by this training program will be allocated to MSU PhD students whose research focuses on any aspect of SP-ML, including, but not limited to, the development of: new algorithms for existing SP-ML; theory to provide insight into key challenges such as those described in the research section below in SP-ML; and in the applications we consider here.
Support will be prioritized for those who come from groups underrepresented in STEM. All aspects of the training program will be open to any PhD students at MSU, to the greatest extent possible. A salient feature of this training program will be substantial engagement with partners in industry and in the national laboratories, who will participate in mentoring students in research and professional skills as well as place students into internship
Our Mission
Complex fluids, biological systems, and multi-component materials are examples of challenging multi-scale problems that are of critical importance for society at large, playing an important role in modern life from computer chips to new cancer therapies, to lightweight materials in cars and aircraft. Despite an enormous multi-decade national investment in massively parallel computational resources and scalable numerical methods, these problems remain some of the most difficult and challenging to solve over the length and time scales of interest. To meet this challenge across a range of important applications, the participants in this NRT come together to advance the fundamental understanding of critical problems by integrating high-fidelity computed and experimental data to create data-informed predictive surrogate models based on modern AI inspired techniques coupled with traditional simulation tools.
Program Features
The AIDMM-MSU-NRT has several key features that together will create an innovative, flexible, and dynamic environment for interdisciplinary graduate education. Key features of the program are:
Three core courses on applications of Machine Learning and AI to mathematical modeling across STEM
Targeted short courses in cutting-edge hot topics,
An internship at a partner site,
Short-term visits from partners,
Weekly Seminars focusing on professional development and Networking,
Team building among cohort members,
Inclusion of partners in PhD students’ guidance committees.
Timeline
Trainees admitted to the NRT cohort will have a diverse background, including Computational Mathematics, Science and Engineering (CMSE), Mathematics, Statistics and Probability, Biochemistry and Molecular Biology and Physics and Astronomy. Trainees need to fulfill the PhD requirements of their home departments. As such, students in the first year will only be expected to attend the professional development seminar and the regular AIDMM-MSU-NRT activities. Ambitious students may take Course 1 in their first year as well.
Students will be expected to complete the NRT curriculum by the end of their second year. The sequence will run every year, with Course 1 in the fall and Courses 2/3 in the spring. Students will be required to complete the internship no later than the start of their fifth year, but will be encouraged to complete it between their second and third years. This timeline fits the requirements of the participating departments.
What we will achieve together
1. A globally competitive work force, MSU Spartans, with substantial technical skills and the professional skills to be successful.
2. A diverse workforce from groups under-represented in STEM fields, including women, under-represented minority students, and persons with disabilities.
3. Enhanced infrastructure for research, training, and collaboration between academia, industry, and national laboratories.
4. Transformative interdisciplinary research in a range of areas of national importance.
5. Development of pedagogically innovative online courses on a range of SP-ML topics that enable groups outside of MSU to use these courses as a way to facilitate workforce retraining.
6. Contribution to evidence-based teaching practices in an area of critical societal importance but which has received very little formal study as of yet.
7. Broad dissemination of evidence-based teaching and learning practices.