Presented By: Michigan Institute for Data and AI in Society MIDAS
Michigan Institute of Data Science Annual Symposium
Day 2
Workshops
Sign up to attend one of the four mini-workshops as part of the 2021 U-M Data Science and AI Symposium. Bring your own laptop!
Nov. 16th | 9:00am - 11:00am @ Michigan League
1. Introduction to data visualization on the web with D3.js. Led by Prof. Fred Feng (Industrial and Manufacturing Systems Engineering)
2. Using text as data: Introduction to machine learning for natural language processing. Led by Drs. Jule Krueger (Institute for Social Research) and Meghan Dailey (Advanced Research Computing)
3. Diversity and equity in data science - a community forum. Led by Drs. Lia Corrales (Astronomy), Tayo Fabusuyi (U-M Transportation Research Institute), H. V. Jagadish (MIDAS Director), and Rada Mihalcea (U-M AI Lab Director). Presenters will highlight technical designs to detect and adjust for data and algorithmic biases, and programs that promote diversity in data science and AI research community. Attendees will be encouraged to share their work and discuss ways to collaborate.
4. Developing best practices for reproducible data science. Led by Drs. Jing Liu (MIDAS Managing Director), Johann Gagnon-Bartsch (Statistics), Tom Valley (Internal Medicine) and Sharon Glotzer's Lab. The presenters will offer tutorials on building reproducible workflows, data and code review and sharing. They will also answer questions for those who are interested in entering the MIDAS 2021 Reproducibility Challenge.
Sign up to attend one of the four mini-workshops as part of the 2021 U-M Data Science and AI Symposium. Bring your own laptop!
Nov. 16th | 9:00am - 11:00am @ Michigan League
1. Introduction to data visualization on the web with D3.js. Led by Prof. Fred Feng (Industrial and Manufacturing Systems Engineering)
2. Using text as data: Introduction to machine learning for natural language processing. Led by Drs. Jule Krueger (Institute for Social Research) and Meghan Dailey (Advanced Research Computing)
3. Diversity and equity in data science - a community forum. Led by Drs. Lia Corrales (Astronomy), Tayo Fabusuyi (U-M Transportation Research Institute), H. V. Jagadish (MIDAS Director), and Rada Mihalcea (U-M AI Lab Director). Presenters will highlight technical designs to detect and adjust for data and algorithmic biases, and programs that promote diversity in data science and AI research community. Attendees will be encouraged to share their work and discuss ways to collaborate.
4. Developing best practices for reproducible data science. Led by Drs. Jing Liu (MIDAS Managing Director), Johann Gagnon-Bartsch (Statistics), Tom Valley (Internal Medicine) and Sharon Glotzer's Lab. The presenters will offer tutorials on building reproducible workflows, data and code review and sharing. They will also answer questions for those who are interested in entering the MIDAS 2021 Reproducibility Challenge.
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