About
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.
Recent Updates
| 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. |
Contact
- Reach me via email at “first name” @ kayali.io.
- Twitter: @moe_kayali
- Old-fashioned mail to:
3800 E Stevens Way NE
Box 352355
Seattle, WA 98195
Profiles
ORCiD
, Google Scholar, DBLP, Semantic Scholar.
My Erdős number is 3.