Michael Wolf
About Michael Wolf
Michael Wolf is the Manager of the Scalable Algorithms Department at Sandia National Laboratories, with a background in combinatorial algorithms and high-performance computing.
Current Position at Sandia National Laboratories
Since 2018, Michael Wolf has been serving as the Manager of the Scalable Algorithms Department at Sandia National Laboratories. In this role, he oversees the development and implementation of scalable algorithms critical for various research and development projects. His work focuses on the advancement of high-performance computing and large-scale scientific simulations.
Career at Sandia National Laboratories
Michael Wolf has had an extensive career at Sandia National Laboratories, beginning in 2007 as a Graduate Professional Summer Intern. After completing two internships and a postdoctoral position, he served as Technical Staff from 2014 to 2018 in the Albuquerque, New Mexico area. Throughout his tenure, he has contributed to various projects in the Scalable Algorithms Department, culminating in his current managerial role.
Technical Staff at MIT Lincoln Laboratory
From 2011 to 2014, Michael Wolf worked at MIT Lincoln Laboratory as a Technical Staff member. During this period, he was engaged in developing advanced algorithms and contributing to significant research initiatives in scientific computing and large-scale simulations.
Educational Background
Michael Wolf earned a Ph.D. in Computer Science from the University of Illinois Urbana-Champaign, where he studied from 2003 to 2009. His doctoral thesis focused on combinatorial algorithms in the context of matrix-vector multiplication. He also holds a B.S. in Computer Science and Biology from Harvey Mudd College, awarded in 1998.
Research Interests and Expertise
Michael Wolf's research interests lie in the area of combinatorial algorithms, particularly for matrix-vector multiplication. His expertise includes high-performance computing, scientific computing, and large-scale scientific simulations. His work addresses complex problems in combinatorial scientific computing, aiming to enhance the efficiency and scalability of computational methods used in various scientific domains.