Research Awards Research Awards: A multi-architecture hardware computing cluster for the development and efficient implementation of a variety of robust and scalable numerical algorithms

Research Awards Research Awards: A multi-architecture hardware computing cluster for the development and efficient implementation of a variety of robust and scalable numerical algorithms
A multi-architecture hardware computing cluster for the development and efficient implementation of a variety of robust and scalable numerical algorithms

$ 600,000 awarded to Sigal Gottlieb sponsored by The Air Force Office of Scientific Research

Chancellor Professor and Acting Vice Chancellor for Research Dr. Sigal Gottlieb (Mathematics) recently received a $600,000 grant from the United States Air Force Office of Scientific Research (AFOSR) to be used for the development and implementation of robust and scalable numerical algorithms.

The grant will fund the acquisition of a new multi-architecture hardware computing cluster to be used as a shared campus research instrument for an inter- and multi-disciplinary group of mathematicians, computational scientists, engineers, and their respective research groups. This will enable the development of robust and scalable numerical algorithms for scientific simulation and data science, empowering eight different research projects.

This grant is meaningful to me because it gives the Center for Scientific Computing, as well as the entire UMassD campus, a computational resource that we can run codes on, which we otherwise wouldn't have,” said Gottlieb. I also love that it can serve to bring people together to collaborate on these ideas. Building a community through a shared resource means a lot to me.

The Air Force is looking for algorithms that do a good job getting results. If you want a computer to solve a problem for you, you must tell it how to do so. Ideally you do so in a way that gives you a solution efficiently. This grant will allow us to develop novel and efficient algorithms, mathematical methods for model complexity reduction, data analytics approaches such as machine learning, and so much more.

Filed under: Departments Mathematics