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.
|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.|
- via email: kayali @ cs dot washington dot edu.
- Twitter: @moe_kayali
- Old-fashioned mail to:
3800 E Stevens Way NE
Seattle, WA 98195
Google Scholar, DBLP, Semantic Scholar, ORCiD .
My Erdős number is 3.