Maggie Myers
Senior Research Fellow Science of High-Performance Computing Group
Centers and Groups
Research Interests
Bayesian Methods High-Performance Computing Linear Algebra Goal-Oriented ProgrammingBiography
Dr. Maggie Myers is retired from the Department of Computer Science and the Department of Statistics and Data Sciences where she taught undergraduate and graduate courses in discrete mathematics, linear algebra, probability and statistics, as well as Bayesian Statistics. Her research activities range from informal learning opportunities in mathematics education to formal derivation of linear algebra algorithms. With Prof. Robert van de Geijn, she developed several MOOCs, including Linear Algebra: Foundations to Frontiers, LAFF-On Programming for Correctness, LAFF- On Programming for High Performance and Advanced Linear Algebra for Computation, all offered on edX. In addition to running the MOOCs, she currently works part-time with the Science of High Performance Computing Group as they research a Framework for Advanced (Multi)Linear Infrastructure in Engineering and Science (our FAMLIES project.)
Maggie Myers' research interests include goal-oriented approaches for deriving algorithms for linear (and multi-linear) computations, reasoning under uncertainty, and. parent involvement for supporting mathematics achievement.