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Computer Science Department Colloquium

When fast algorithms meet modern society

 

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Monday, March 06, 2023, 10:30am - 11:30am

 

Speaker: Omri Ben-Eliezer

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 blends aspects of algorithm design and data modeling, 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.

Location : Core 301

Event Type: Computer Science Department Colloquium

Abstract: The rapidly growing societal impact of data-driven systems requires modern algorithms to process massive-scale complex data not just efficiently, but also responsibly, e.g., under privacy or robustness guarantees. In this talk I will discuss some of my recent research developing fast (e.g., sublinear-time or sublinear-space) and responsible algorithms for modern data analysis problems. I will focus on three representative lines of work: (i) the first systematic investigation of adversarial robustness in streaming algorithms, (ii) algorithm design in real-world social networks via new notions of core-periphery sparsification, and (iii) differentially private synthetic data generation in high dimensions via beyond-worst-case data modeling. Through these examples, I will demonstrate how the symbiosis between algorithm design and modeling of complex data often leads naturally to new structural insights and multidisciplinary connections

Contact  Jie Gao

Join Zoom Meeting
https://rutgers.zoom.us/j/96152219159?pwd=TnZBazZic011Vzc5VVYvdkpna1VOQT09
Meeting ID: 961 5221 9159
Password: 716980