Mahmoud Abo Khamis

Mahmoud Abo Khamis is joining the University of Oxford as an Associate Professor of Computer Science and a Tutorial Fellow at Wadham from September 2026.

Prior to Oxford, Mahmoud was a Senior Computer Scientist at RelationalAI, 2017 - 2026. He was a Visiting Research Scientist at the Simons Institute for the Theory of Computing, Berkeley, University of California from Aug 2023 - Dec 2023; and a Senior Database Engineer at LogicBlox from 2015 to 2017.

Mahmoud received his Ph.D. and M.S. in Computer Science and Engineering from the State University of New York at Buffalo in 2016 and 2012 respectively, and his B.Eng. in Computer Science from Damascus University in 2010.

Mahmoud has taught Discrete Mathematics at SUNY Buffalo. While working in industry, he mentored several PhD students as interns — working with them to implement their research ideas in real-world systems and to bridge the gap between academic research and industry.

Mahmoud's research interests include database systems and theory, in-database machine learning, query optimization and evaluation, information theory, and beyond worst-case analysis. His broader research vision is for query optimization to guide automated algorithm design — with the ultimate goal of the query optimizer becoming a universal algorithm designer: one that takes in any problem definition and automatically generates an optimal algorithm for it.

Mahmoud’s research has been recognized with two ACM PODS Test-of-Time Awards, in 2025 and 2026; three Best Paper Awards, at ACM SIGMOD 2025 and ACM PODS 2022 and 2016; three ACM SIGMOD Research Highlight Awards; three ACM PODS Distinguished Paper Awards; and the 2016 Best CSE Dissertation Award from SUNY Buffalo. His work has also been invited to the Journal of the ACM, ACM STOC, TheoretiCS, and ACM TODS.

Christoph Mayer, Haozhe Zhang, Mahmoud Abo Khamis, Kyle Deeds, Dan Olteanu, and Dan Suciu. CorrBound: Cardinality Estimation Accounting for Inter- and Intra-relation Correlations. Proc. ACM Manag. Data, 4(1), April 2026

Mahmoud Abo Khamis, Hung Q. Ngo, and Dan Suciu. PANDAExpress: A Simpler and Faster PANDA Algorithm. Proc. ACM Manag. Data, 4(2), May 2026. Distinguished Paper Award. Invited to the Journal of the ACM.

Mahmoud Abo Khamis, Alexandru-Mihai Hurjui, Ahmet Kara, Dan Olteanu, and Dan Suciu. Acyclic Conjunctive Regular Path Queries are no Harder than Corresponding Conjunctive Queries. Proc. ACM Manag. Data, 4(2), May 2026.

Mahmoud Abo Khamis and Hubie Chen. Jaguar: A Primal Algorithm for Conjunctive Query Evaluation in Submodular-Width Time. Proc. ACM Manag. Data, 4(2), May 2026

Mahmoud Abo Khamis, Eden Chmielewski, Andrei Draghici, Ahmet Kara, and Dan Olteanu. Maintaining Queries under Updates Using Heavy-Light Partitioning of the Input Relations. 4(2), May 2026.

Mahmoud Abo Khamis, Jesse Comer, Phokion G. Kolaitis, Sudeepa Roy, and Val Tannen. A unifying algorithm for hierarchical queries. Proc. ACM Manag. Data, 3(5), November 2025.

Haozhe Zhang, Christoph Mayer, Mahmoud Abo Khamis, Dan Olteanu, and Dan Suciu. LpBound: Pessimistic Cardinality Estimation Using Lp-Norms of Degree Sequences. Proc. ACM Manag. Data, 3(3), June 2025. Best Paper Award.

Mahmoud Abo Khamis, Xiao Hu, and Dan Suciu. Fast Matrix Multiplication meets the Submodular Width. Proc. ACM Manag. Data, 3(2), June 2025. Distinguished Paper Award. Invited to TheoretiCS.

Mahmoud Abo Khamis, Ahmet Kara, Dan Olteanu, and Dan Suciu. Output-Sensitive Evaluation of Regular Path Queries. 3(2), June 2025.

Mahmoud Abo Khamis, Hung Q. Ngo, and Dan Suciu. PANDA: Query Evaluation in Submodular Width. TheoretiCS, Volume 4, Apr 2025.

Mahmoud Abo Khamis,Vasileios Nakos, Dan Olteanu, and Dan Suciu. Information Theory Strikes Back: New Development in the Theory of Cardinality Estimation. SIGMOD Rec., 54(1):7–15, April 2025. Invited Article [14].

Mahmoud Abo Khamis, Kyle Deeds, Dan Olteanu, and Dan Suciu. Pessimistic Cardinality Estimation. SIGMOD Rec., 53(4):1–17, January 2025. Invited Article.