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Einstein Meets Codd: Tensor Algebra through the Lens of Relational Algebra

Amir Shaikhha ( Edinburgh )
Tensor processing systems and relational database systems have historically evolved in separate communities, despite sharing deep algebraic structure. This talk explores recent efforts to bridge these worlds by unifying tensor computation and relational query processing, either by expressing tensor operations as relational queries or by developing new algebraic languages that capture both paradigms. I will then discuss how techniques at the interface of these communities can be used to accelerate workloads in databases, machine learning, quantum simulation, and program analysis. Along the way, I will highlight the trade-offs these methods expose, including performance, expressiveness, and the spectrum between exact and approximate computation.

Speaker bio

Amir Shaikhha is an Associate Professor (Reader) in the School of Informatics at the University of Edinburgh. His research focuses on the design and implementation of data-analytics systems by using techniques from the databases, programming languages, compilers, and machine learning communities. He was a Departmental Lecturer at the University of Oxford (2019-2020) before starting as an Assistant Professor (Lecturer) at the University of Edinburgh (2020-2024). He earned his Ph.D. from EPFL in 2018, for which he was awarded a Google Ph.D. Fellowship in structured data analysis, as well as a Ph.D. thesis distinction award. He has won the Best Paper Award at GPCE 2017, the Most Reproducible Paper Award at SIGMOD 2017, the Most Influential Paper Award at GPCE 2024, Google Research Scholar Award 2025, and Dahl-Nygaard Junior Prize 2025. He (co-)chaired the program committees of GPCE, DBPL, Scala, Sparse, and DRAGSTERS.