Congratulations to Civil and Environmental Engineering doctoral candidate and ITS student Zhe Fu, who placed second in the UC Berkeley Grad Slam with her project. “Stop-and-go No More: How a Few Smart Cars Can Fix Traffic Jams!”
Grad Slam is a UC-sponsored competition designed to showcase graduate student research for a general audience in three-minute talks. Students compete in preliminary rounds on their UC campus, with prizes awarded at each stage of the selection process. The Berkeley first-place winner will advance to compete against the other UC finalists at the UC-wide competition in May. Fu will advance to the UC-Wide Grad Slam Championship if the winner is unable to attend.
CEE doctoral student and ITS student Jaewon Saw also competed as a semi-finalist in the UC Berkeley Grad Slam with her project, “Listening with Light: Unlocking the World with Distributed Acoustic Sensing.” With only 10 semi-finalist graduate stuents from across UC Berkeley, CEE (and ITS) was the only department to have two students represented!
Watch their presentations here: https://grad.berkeley.edu/professional-development/gradpro/workshops-and...
Zhe Fu is a fifth-year Ph.D. candidate in Transportation Engineering, developing sustainable solutions for mixed-autonomy traffic (where autonomous and human-driven vehicles coexist). Her work blends physics models, control theory, and machine learning across theoretical, algorithmic, and real-world applications to reduce congestion, cut emissions, and improve traffic energy efficiency. Outside academia, she enjoys singing, attending live performances, and playing sports.
Jaewon Saw is a Ph.D. candidate in Systems Engineering, specializing in Distributed Acoustic Sensing, a cutting-edge technology with diverse applications. Growing up, she was fascinated by the engineering behind historic structures and their ability to withstand the test of time. This curiosity led her to pursue structural engineering during her undergraduate and Master’s studies. Now, she applies advanced sensing technologies and data-driven methods to study the built and natural environment, working to enhance the resilience of critical infrastructure.