Sahar Sharifzadeh
Associate Professor
Department of Electrical and Computer Engineering
Division of Materials Science and Engineering
Affiliated Faculty: Departments of Physics and Chemistry
| Location: | PHO 535 | |
| Email: | ssharifz@bu.edu | |
| Address: | The Photonics Center Boston University 8 St. Mary’s St. Boston, MA 02215 |
Profile
Sahar Sharifzadeh is an Associate Professor within the Electrical & Computer Engineering Department and the Materials Science & Engineering Division, and an affiliated faculty member within the Physics and Chemistry Department, as well as Hariri Institute of Computing, at Boston University. She obtained her PhD from Princeton University in 2009, working under the guidance of Prof. Emily Carter. She then joined the Molecular Foundry, a nanoscience User facility at Lawrence Berkeley National Laboratory, as a postdoctoral fellow, and subsequently project scientist in the group of Dr. Jeffrey Neaton. She joined Boston University in 2014. Prof. Sharifzadeh was awarded the Boston University Hariri Institute Junior Faculty Fellowship in 2016, the Department of Energy Early Career Award in 2017, the National Science Foundation Early Career Award in 2019, and the BU College of Engineering Early Career Award in 2019. In 2018, she was named as one of eleven rising stars of Science by Scientific American.
Education
PhD, Princeton University, 2009
Research
Sahar Sharifzadeh’s research is focused on understanding and predicting the atomic-scale physical phenomena that drive the functional behavior of novel materials. This is particularly important for next generation technologies such as those related to nanoscience and quantum information science, where the phenomena of interest are dominated by the behavior of single atoms and electrons. Her group develops and applies first-principles electronic structure theories, which can predict, with quantitative accuracy, the electronic properties of materials from the basic laws of quantum mechanics. The ultimate goal is to understand complex materials phenomena in simple, physically intuitive ways, thereby learning how to control functionality.