Bashir joined the Scientific Data Management Group as a postdoctoral scholar on the "Deep and Autonomic High-Performance Networks (DAPHNE) project under the supervision of Mariam Kiran of ESnet and John Wu. His current work focuses on developing Machine Learning algorithms to optimally control distributed network resources, improve high-speed big data transfers and control high-speed networks that will eventually minimize network downtime and avoid network traffic congestion for important Exacscale scientific workflows.
Bashir received his Ph.D. in Computer Science from the University of Bradford UK, where he was part of the Network and Performance Engineering(NetPEn) Research group. He has a Bachelor's degree in Electrical and Computer Engineering and a Master's degree in Control Systems Engineering from the University of Sheffield (United Kingdom). He was an intern with Rolls-Royce University Technology Center in Sheffield, where he developed a control and optimization algorithm for the modeling of gas turbine engines.
» Visit Bashir Mohammed's personal web page.