News

The September issue of Montana NSF EPSCoR's quarterly newsletter, with articles on research activities, new faculty hires, outreach programs, and diversity initiatives, is now available.

On Thursday, February 6, staff from the Science Math Resource Center (SMRC) facilitated SMART FIRES activities at MSU’s Family Science Day. Over 200 fifth grade students from local schools came to campus to engage with hands-on activities centered around STEM research at MSU. Two graduate students from the SMART FIRES project Mary Farina and Azin Rashed graciously helped put on the event and brought their research expertise to the broader impacts team. At the SMART FIRES table the students learned about sensors, which are a core research area of the project.

The MSU Science Math Resource Center, with support from Montana NSF EPSCoR, has partnered with the MSU Library to provide four citizen science kits that all MSU Library users, including community borrowers, may use for free.

Academic Technology and Outreach at Montana State University and the Montana University System Water Center will offer a series of four noncredit courses that provide water quality training and continuing education opportunities for water and wastewater system operators. Registration is now open for these fully online, free courses, which are self-paced and can be accessed at any time.

Video produced by Gerard Sapes (UM)

For FY 2022, RII Track-2 FEC proposals are invited on a single topic: “Advancing research towards industries of tomorrow to ensure economic growth for EPSCoR jurisdictions.”

Dr. Jacob Downs is a new associate professor in computer science at the University of Montana. With a background in machine learning and numerical modeling regarding environmental systems he is excited to engage with SMART FIRES researchers and inspire the next generation of computer scientists. How did you get into STEM?

What is your specialty? I am a computational cognitive neuroscientist. I’m really interested in brain network dynamics and larger systems that relate to patients where you can pick out biomarkers that are similar across patients to help diagnoses.

What is your specialty? I work in the space between statistics and machine learning.Statistical models are nice because they’re interpretable, probabilistic, and reliable to train. But they can struggle with large, complex datasets. On the other hand, machine learning models are great for large, complex datasets, but many of them are black boxes which are hard to interpret, and the training process can be unreliable and resource intensive. My research is about developing new methods which blend the best of these worlds.