With the proliferation of Internet-of-Things devices that continuously generate data as input for distributed applications, current frameworks for streaming computing fail to meet the scalability and latency requirements of upcoming applications such as autonomous driving. In this talk, we discuss current challenges and present the design of a scalable and highly adaptive edge stream processing engine that enables fast processing of a large number of concurrent running IoT applications’ queries in dynamic edge environments.

Biography: Dilma Da Silva is a Professor and Holder of the Ford Design Professorship II at the Department of Computer Science and Engineering at Texas A&M University. Her previous roles include Department Head (2014-2019), Associate Dean (2019-2020), Interim Director of the Texas A&M Institute of Data Science, and Interim Director of the Texas A&M Cybersecurity Center. Her primary research interests are distributed systems, operating systems, and computer science education. She currently has projects on cybersecurity, data sciences, and autonomous vehicles. Before joining Texas A&M, she worked at Qualcomm Research (2012-2014), IBM Research (2000-2012), and the University of Sao Paulo (1996-2000). Dilma is an ACM Distinguished Scientist, a member of the board of CRA-W (Computer Research Association’s Committee on the Status of Women in Computing Research) and a co-founder of the Latinas in Computing group. Recent leadership roles include Program Co-chair for IEEE ICDCS’21, ACM Middleware’20, Supercomputing’19, and IPDPS’19. Dilma received her doctoral degree in computer science from Georgia Tech in 1997 and her bachelor’s and master’s degrees from the University of São Paulo, Brazil. She is passionate about enabling the next generation of talent.