Moe Kayali


I’m Moe, a database researcher and PhD student at the University of Washington, Seattle.

I currently work on applying of large language models to data discovery tasks such as table annotation. Previously, I worked graph compression methods, which accelerate graph analysis 10–100⨉ while introducing a minimal approximation error. I have also worked on automated machine learning (autoML), which allows for selecting performant and robust machine learning models. As well as causal inference, which aids analysts in finding true cause-and-effect relationships in complex data.

My work has found real-life applications at places from large enterprises, such as Microsoft Research, to small startups such as Virta Labs.

Photogram of an IBM store front, Tronto, 1963

Recent Updates

Mar-2024 Our “CHORUS: Foundation Models for Unified Data Discovery and Exploration” paper has been accepted to VLDB 2024
Jan-2024 I’ll be on a panel on AI+DB at the NWDS 2024.
Dec-2023 CHORUS will be at the Table Representation Learning workshop at NeurIPS ‘23. Come say hi!
Jun‑2023 Read our preprint on using large language-models (LLMs) for data discovery
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
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. My Erdős–Bacon number is ∞.