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Lorenzo De Stefani is an Assistant Teaching Professor in the Department of Computer Science at Brown University, where he specializes in algorithm design and analysis, particularly within the realm of computational complexity and statistical learning. He received his Ph.D. in Computer Science from Brown University in 2020, under the guidance of Professor Eli Upfal, and holds a Ph.D. in Computer Engineering from the University of Padova, completed in 2016. His research interests encompass design analysis algorithms that include input/output complexity and resilient algorithms for handling Byzantine memory faults. De Stefani has significantly contributed to the fields of statistical learning and adaptive data analysis, developing uniform convergence bounds and efficient procedures controlling accumulated errors. His innovative work on visual data representation has focused on improving the Marginal False Discovery Rate using statistical learning tools. Additionally, his research on dynamic graph streams and algorithm efficiency has been recognized with the KDD Student Paper Award at the 22nd ACM SIGKDD International Conference. He actively teaches courses such as Theory of Computation, Design and Analysis of Algorithms, and Data Science at Brown University.
Department: Department of Economics