Assistant Professor, Computer Science
Dr. Rawassizadeh’s research interest focuses on ubiquitous technologies, including wearables, mobile devices, and robots. He has made notable contributions in designing resource-efficient machine learning algorithms to operate on battery-powered devices. These machine learning algorithms are cloud-independent and small devices, such as smartwatches or fitness trackers, can execute them without any network requirement. Before joining academia, Rawassizadeh has worked in seven different countries and four different states in the US, including time with Siemens (the largest European Engineering Corporation) and the United Nations. He was recognized with the ACM Computing Review Notable Article of 2016, was a finalist for the Vodafone innovation award in 2017, and received an honorable mention award in an IEEE Percom workshop, 2017.