Niv Dayan

Niv Dayan is an Assistant Professor in the Department of Computer Science at the University of Toronto. His research focuses on data management, databases, systems, storage engines, key-value stores, NoSQL, data structures, and probabilistic filters. He works across the full pipeline from theoretical analysis to practical implementation, often collaborating with industry startups. His work has been recognized with best-paper awards at SIGMOD and VLDB.

Niv Dayan is a researcher at the University of Toronto with expertise in storage systems, databases, and key-value stores. His work focuses on optimizing data structures for performance and scalability, particularly in the context of LSM-trees and key-value store design. Dayan has published extensively on topics such as adaptive merging, bloom filters, and space-time trade-offs in key-value stores. His research includes the development of novel data structures like Monkey, Dostoevsky, and Rosetta, which aim to improve efficiency and reduce overhead in storage systems. He also explores the design continuums of data structures and their potential for self-designing systems that adapt to data patterns.