Amir is an Assistant Professor (Lecturer) 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. Prior to that, he was a Departmental Lecturer at Oxford. 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 and the Most Reproducible Paper Award at SIGMOD 2017. He (co-)chaired the program committees of DBPL 2021, Scala 2022, DRAGSTERS 2023, and GPCE 2023.

• August 2023. Our paper on automatic differentiation for sparse tensors accepted to appear at CGO 2024.
• July 2023. Our paper on structured tensor algebra accepted to OOPSLA 2023.
• June 2023. The demonstration for OpenDBML accepted to appear at VLDB 2023.
• May 2023. I was selected for a Best Reviewer Award in SIGMOD 2023.
• April 2023. The demonstration for SDQL.py accepted to appear at SIGMOD 2023.
• March 2023. I will serve as the PC Chair of GPCE 2023; consider submitting a paper.
• February 2023. Our work on combining prefetching with vectorization for hash tables accepted to appear at ECOOP 2023.
• January 2023. I will be co-chairing DRAGSTERS’23. Consider submitting a paper.
• December 2022. Two papers accepted in CC 2023: BayesTensor and SDQL.py.
• November 2022. Our work on using machine learning for data structure selection accepted to appear at CGO 2023.
• October 2022. Our work on Deep Loop Fusion accepted to appear at GPCE 2022.
• August 2022. STOREL for tensor processing on flexible storage accepted to SIGMOD 2023.

• A Tensor Algebra Compiler for Sparse Differentiation; A. Shaikhha, M. Huot, S. Hashemian, to appear in CGO’24.
• Compiling Structured Tensor Algebra; M. Ghorbani, M. Huot, S. Hashemian, A. Shaikhha, to appear in OOPSLA’23.
• Demonstration of OpenDBML, a Framework for Democratizing In-Database Machine Learning; M. Ghorbani, A. Shaikhha, VLDB’23 (demo).
• Compiling Discrete Probabilistic Programs for Vectorized Exact Inference; J. Pan, A. Shaikhha, CC’23.
• Building a Compiled Query Engine in Python; H. Shahrokhi, A. Shaikhha, CC’23.
• An Efficient Vectorized Hash Table for Batch Computations; H. Shahrokhi, A. Shaikhha, ECOOP’23.
• Fine-Tuning Data Structures for Query Processing; A. Shaikhha, M. Kelepeshis, M. Ghorbani, CGO’23.
• Efficient Query Processing in Python Using Compilation; H. Shahrokhi, A. Shaikhha, SIGMOD’23 (demo).
• Optimizing Tensor Programs on Flexible Storage; M. Schleich, A. Shaikhha, D. Suciu, SIGMOD’23.
• Functional Collection Programming with Semi-ring Dictionaries; A. Shaikhha, M. Huot, J. Smith, D. Olteanu, OOPSLA’22.
• Hinted Dictionaries: Efficient Functional Ordered Sets and Maps; A. Shaikhha, M. Ghorbani, H. Shahrokhi, ECOOP’22.
• Deep Fusion for Efficient Nested Recursive Computations; A. Shaikhha, GPCE’22.
• An Intermediate Representation for Hybrid Database and Machine Learning Workloads; A. Shaikhha, M. Schleich, D. Olteanu, VLDB’21 (Demo).
• Scalable Querying of Nested Data; J. Smith, M. Benedikt, M. Nikolic, A. Shaikhha, VLDB’21.
• Multi-Stage Programming in the Large with Staged Classes; L. Parreaux, A. Shaikhha, GPCE’20.
• Synthesis of Incremental Linear Algebra Programs; A. Shaikhha, M. El Seidy, D. Espino, S. Mihaila, C. Koch, TODS’20.
• Multi-Layer Optimizations for End-to-End Data Analytics; A. Shaikhha, M. Schleich, A. Ghita, D. Olteanu, CGO’20.
• BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation; Z. Wu, A. Shaikhha, Rong Zhu, Kai Zeng, Yuxing Han, Jingren Zhou, CoRR’20.
• Compiler Generation for Performance-Oriented Embedded DSLs; A. Shaikhha, V. Jovanovic, C. Koch, GPCE’19 (Short Paper).
• Polymorphic Extractors for Semantic and Portable Pattern Matching; A. Shaikhha, GPCE’19 (Short Paper).
• Efficient Differentiable Programming in a Functional Array-Processing Language; A. Shaikhha, A. Fitzgibbon, S. Peyton-Jones, D. Vytiniotis, ICFP’19.
• Finally, a Polymorphic Linear Algebra Language; A. Shaikhha, L. Parreaux, ECOOP’19.
• Building Efficient Query Engines in a High-Level Language; A. Shaikhha, Y. Klonatos, C. Koch, TODS’18.
• Push vs. Pull-Based Loop Fusion in Query Engines; A. Shaikhha, M. Dashti, C. Koch, JFP’18.
• Unifying Analytic and Statically-Typed Quasiquotes; L. Parreaux, A. Voizard, A. Shaikhha, and C. Koch, POPL’18.
• Quoted Staged Rewriting: a Practical Approach to Library-Defined Optimizations; L. Parreaux, A. Shaikhha, and C. Koch, GPCE’17 (Best Paper Award).
• Repairing Transaction Conflicts in Optimistic Multi-Version Concurrency Control; M. Dashti, S. John, A. Shaikhha, C. Koch, SIGMOD’17 (Most Reproducible Paper Award).
• How to Architect a Query Compiler; A. Shaikhha, Y. Klonatos, L. Parreaux, L. Brown, M. Dashti, C. Koch, SIGMOD’16.
• Yin-Yang: Concealing the Deep Embedding of DSLs; V. Jovanovic, A. Shaikhha, S. Stucki, V. Nikolaev, C. Koch, M. Odersky, GPCE’14.
• DBToaster: Higher-order Delta Processing for Dynamic, Frequently Fresh Views; C. Koch, Y. Ahmad, O. Kennedy, M. Nikolic, A. Noetzli, D. Lupei, A. Shaikhha, VLDBJ’14.