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 and improve the reliability of data management and data science. 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.
|2022‑08‑15||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 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 Outstanding Undergraduate Researcher Award.|
- Reach me via email at “first name” @ kayali.io.
- Twitter: @moe_kayali
- Old-fashioned mail to:
3800 E Stevens Way NE
Seattle, WA 98195
ORCiD , Google Scholar, DBLP, Semantic Scholar.
My Erdős number is 3.