REU Site: ACRES: Advanced Computational Research Experience for Students

The application of large-scale computing power to solve problems is becoming more and more common in science and industry. It is thus vital to equip the next generation with the skills necessary to meet this need. This award will fund a summer Research Experience for Undergraduates program called “Advanced Computational Research Experiences for Students” or “ACRES” at Michigan State University (MSU) that will offer students the opportunity to conduct cutting-edge research in computational and data science under the supervision of faculty who are recognized experts in their disciplines. Over 10-weeks, students will receive in-depth training, mentoring and professional development, while being immersed in an active research environment at MSU. They will gain valuable experience in developing their own computationally-focused research project and gain exposure to a wide variety of research problems from different areas of specialty. This project also aims to broaden the pipeline of computational researchers by encouraging participation of underrepresented populations in STEM research, as well as providing early exposure to high performance computing to students from institutions with limited access to HPC resources. This award provides funding for the ACRES (“Advanced Computational Research Experiences for Students”) REU program at Michigan State University (MSU), which provides a unique training environment for undergraduate students. This program is jointly headquartered by two organizations in MSU. The first is the Institute for Cyber-Enabled Research (ICER), who administer MSU’s high performance computing cluster and regularly provide computational training and workshops for MSU students and faculty. The second is the Department of Computational Mathematics, Science, and Engineering (CMSE), founded in 2015, which brings together applied mathematicians and statisticians who create algorithms, with computationally-minded engineers and scientists who can apply these algorithms in diverse contexts. This type of environment breaks down silos and facilitates the spread of ideas between disciplines. Training in interdisciplinary environments is particularly beneficial to undergraduate researchers. It allows students to draw parallels between research concepts early on, such as the modeling of planetary motion around a star and the simulation of atoms in a plasma. It also provides students with exposure to a wide range of research domains in computational science, which could improve students’ enthusiasm for science and students’ retention in the scientific pipeline. In this environment, students will develop a broad understanding of research computing and gain experience in applying domain-specific knowledge to computational modeling problems. This project will also broaden participation in computational science by emphasizing the recruitment of women and students from underrepresented groups, and aim to motivate students to pursue careers in computational fields by fostering an inclusive environment. In addition, the dissemination of the knowledge gained as a result of the students’ research will contribute to advancing the respective scientific fields throughout STEM. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.