Amir Shaikhha

Amir Shaikhha is an Associate Professor (Reader) in the School of Informatics at the University of Edinburgh, specializing in data-analytics systems. Previously, he served as a Departmental Lecturer at the University of Oxford (2019-2020) and Assistant Professor at Edinburgh (2020-2024), following his 2018 PhD from EPFL where he received a Google Ph.D. Fellowship and thesis distinction award.

His research spans:

• Databases
• Programming Languages
• Compilers
• Machine Learning
• Software Engineering

with emphasis on high-performance system design through techniques like tensor algebra optimization, domain-specific languages, and probabilistic programming.

Recent publications (2023-2025) reveal strong trends in quantum simulation synthesis, sparse tensor processing, and compiler-driven database optimization, evidenced by top-venue acceptances at PLDI, SIGMOD, and OOPSLA.

His scientific recognition includes:

• Dahl-Nygaard Junior Prize (2025)
• Google Research Scholar Award (2025)
• Most Influential Paper Award at GPCE (2024)
• Best Paper Award at GPCE (2017)

He actively mentors students including PhD graduate Hesam Shahrokhi (now at Huawei Research), award-winning MSc candidates like Youning, and CGO competition winners Jingwen and Callum. His service includes PC chair roles for GPCE 2023 and DBPL 2025, alongside program committee work for SIGMOD, VLDB, and ECOOP.

As leader of the Data Analytics Lab (DAL), he develops open-source systems like StructTensor and VecHT for tensor processing, contributing to Edinburgh's Institute for Computing Systems Architecture with focus on bridging theoretical foundations with practical data-engineering solutions.