אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Our modern life is marked by continuous interaction with huge and complex computational environments, a setting which gives rise to numerous theoretical and algorithmic challenges. Algorithms nowadays are often required to optimize objectives that may be theoretically ill-defined, on big data that is complex-structured, while maintaining computational efficiency and provable guarantees such as privacy and robustness. In this talk I will discuss some of my work developing new computational models and fast (e.g., sublinear-time or sublinear-space) algorithms for these modern settings, where data is highly structured or undergoes complex dynamics. I will focus on three representative lines of work: (i) the first systematic investigation of adversarial robustness in streaming algorithms, (ii) a new algorithmic framework for real-world social networks based on core-periphery sparsification, and (iii) active learning and testing the “geometry of data” in low-dimensional settings. Through these examples, I will demonstrate how the symbiosis between modeling and algorithm design often leads naturally to new structural insights and multidisciplinary connections. Bio: Omri Ben-Eliezer is an instructor (postdoc) in applied mathematics at MIT. He received his PhD in computer science from Tel Aviv University, under the supervision of Prof. Noga Alon, and held postdoctoral positions at Weizmann Institute and Harvard University. His research focuses on algorithm design in complex environments, with specific interests including sublinear-time and streaming algorithms, large networks, robustness and privacy, and knowledge representation. For his work, Omri received several awards, including best paper awards at PODS 2020 and at CVPR 2020 Workshop on Text and Documents, the 2021 SIGMOD Research Highlight Award, and the first Blavatnik Prize for outstanding Israeli doctoral students in computer science.