Moe Kayali


Hello there. 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 work includes graph compression methods, which enable analysis of extreme-scale graphs; automatic machine learning, which allows non-expert users to select performant machine learning models; and causal inference, which aids analysts in rejecting spurious statistical results.

In my free time I enjoy reading, hiking and cycling.

Recent Updates

2022‑08‑15 I presented at the AutoML workshop at KDD 2022.
2022‑06‑20 I’m interning at the Gray Systems Lab at Microsoft this summer, working on cardinality estimation.
2021-06-17 Awarded a Herbold Fellowship for the year 2021-2022.
2021-06-14 Working under Chi Wang at the Data Systems Group within Microsoft Research this summer.
2020‑08‑29 View a demonstration of causal inference on relational data with CaRL, which I presented at VLDB 2020.
2020-07-10 Read my letter in The Seattle Times regarding the administration’s (since retracted) plan to expel international students.
2020‑06‑15 Received the Outstanding Senior Award from the Allen School of Computer Science.
2020-04-15 Excited to be joining the database group at the University of Washington as a PhD student in September 2020!
2020‑03‑13 Our first paper, “Causal Relational Learning,” will be presented at SIGMOD 2020.
2019‑12‑25 Selected as a Mary Gates Research Scholar.
2019‑12‑17 Honorable mention in the CRA’s national Outstanding Undergraduate Researcher Award.



ORCID iD iconORCiD , Google Scholar, DBLP, Semantic Scholar.

photograph of the author

My Erdős number is 3.