Moe Kayali


I’m Moe, a third-year PhD student in the database group at the University of Washington, Seattle. I work on discovering new techniques to accelerate data management and make its results more trustworthy.

My focus at the moment is on graph compression methods, which accelerate graph analysis 10–100⨉ while introducing minimal approximation error. Separately, I am exploring the application of large language models to knowledge graphs.

In the past, I have worked automatic machine learning, which allows non-expert users to select performant machine learning models. As well as causal inference, which aids analysts in rejecting spurious statistical results.

Recent Updates

Nov‑2022 Presenting Quasi-stable coloring at VLDB 2023: try the Julia package out.
Aug‑2022 Presented at the AutoML workshop at KDD 2022.
Jun‑2022 Interning at the Gray Systems Lab at Microsoft this summer, working on cardinality estimation.
Jun‑2021 Selected as a Herbold Fellow for the year 2021.
Jun‑2021 Working under Chi Wang at the Data Systems Group within Microsoft Research this summer.
Aug‑2020 View a demonstration of causal inference on relational data with CaRL, which I presented at VLDB 2020.
Jul‑2020 Read my letter in The Seattle Times regarding the administration’s (since retracted) plan to expel international students.
Jun‑2020 Received the Outstanding Senior Award from the Allen School of Computer Science.
Apr‑2020 Excited to be joining the database group at the University of Washington as a PhD student in September 2020!
Mar‑2020 Causal Relational Learning will be presented at SIGMOD 2020.
Dec‑2019 Selected as a Mary Gates Research Scholar
Dec‑2019 Honorable mention in the national CRA Outstanding Undergraduate Researcher Award.



Google Scholar, DBLP, Semantic Scholar, ORCiD .

photograph of the author

My Erdős number is 3.