2019-07-23 –, BoF: Room 353
JuliaDB is an analytical data framework that offers typed dataframes, parallel processing, and limited out-of-core support. This session gives JuliaDB users and contributors the opportunity to discuss how JuliaDB works for them, tackle issues, and discuss the future of JuliaDB.
Possible topics of conversation/things to work on:
- JuliaDB wishlist
- Utilities for feature engineering/other ML tasks
- Fixing bugs
- Creating benchmarks
I am a PhD statistician who enjoys programming (particularly with Julia) for difficult optimization and machine learning problems. My niche is the intersection of statistics and computer science, which allows me to quickly translate whiteboard math into efficient programs. During my PhD years I researched on-line algorithms for statistics (single-pass algorithms for streaming and big data), an underused paradigm where statistics/models can be updated on new batches of data without revisiting past observations (see OnlineStats.jl). I am a research scientist, data scientist, machine learning engineer, and software engineer. I contribute to a variety of open source data science tools, some of which can be found here: https://github.com/joshday.