About
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.
Recent Updates
| 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. |
Contact
- via email: kayali @ cs dot washington dot edu.
- Twitter: @moe_kayali
- Old-fashioned mail to:
3800 E Stevens Way NE
Box 352355
Seattle, WA 98195
Profiles
Google Scholar, DBLP, Semantic Scholar, ORCiD .
My Erdős number is 3.