He was awarded an NSF CAREER Award in 2020, a Facebook Secure the Internet Grant in 2018, a UCL BEAMS Future Leaders in Engineering and Physical Sciences Award in 2016, a Google Research Award in 2015, the Outstanding Dissertation Award from the Department of Computer Science at UCSB in 2014, and a Symantec Research Labs Graduate Fellowship in 2012.
In his research, he applies a data-driven approach to better understand malicious activity on the Internet. Through the collection and analysis of large-scale datasets, he and his team develop novel and robust mitigation techniques to make the Internet a safer place. His research involves a mix of quantitative analysis, (some) qualitative analysis, machine learning, crime science, and systems design.
He and his Ph.D. student Shiza Ali will utilize crowd-sourced annotations to build machine learning techniques for identifying online accounts that engage in online hate and to flag the offending content for moderation.
Professor Stringhini’s team plan to develop predictive models to identify content that is most at risk of attracting hateful attacks, and to examine the advantages and disadvantages of various methods of mitigation.