Shahriar Hooshmand

My research focus is understanding the effect of chemical elements on mechanical properties of structural materials and mass transport. The specific research emphasis is on the modeling of defects such as dislocations, twinning and grain boundaries using state-of-the-art computational methods combined with theoretical considerations. I will be using density functional theory (DFT) to study the effect of solutes on mechanical properties of titanium alloys. I am also interested to combine high-throughput DFT calculations and novel machine learning (ML) algorithms to discover new materials and predict the mechanical properties.
I am currently working at UC Berkeley as a postdoctoral researcher advised by Prof. Mark Asta. I received my B.S degree in Civil Engineering at Sharif University of Technology in Tehran, Iran in 2014, and my Ph.D. degree in Materials Science and Engineering (MSE), advised by Prof. Maryam Ghazisaeidi and graduate minor in Computer Science and Engineering (CSE)-artificial intelligence and machine learning track at The Ohio State University in 2019.