When: April 2, 2020, 4:45-9:00 pm
Location: Robeson and Hahn Hall South
What is WiDS Blacksburg at Virginia Tech?
WiDS stands for Women in Data Science. It is a series of regional events focused primarily on technical aspects and applications of data science, in which all the presenters are women. The goal is to encourage women to pursue careers in data science, and to support them by building a local community of data scientists, helping them develop their skill set and understand more about what it means to apply data science techniques to a variety of challenging problems.
This event is open to all members of the Virginia Tech community interested in data science, regardless of major! In some parts of the program we will break into smaller more focused groups so everyone from freshmen to graduate students and researchers can find topics of interest.
VT News story and video about the first WiDS Blacksburg at Virginia Tech (2019):
And program of the 2019 event: https://eileenrmartin.github.io/docs/WiDS-BBurg-VT-Program-2019.pdf
WiDS Blacksburg @ Virginia Tech is an independent event organized by the local organizing committee with faculty sponsor Eileen Martin to coincide with the annual Global Women in Data Science (WiDS) Conference held at Stanford University and an estimated 200+ locations worldwide. All genders are invited to attend WiDS regional events, which features outstanding women doing outstanding work.
Schedule: (stay tuned)
The event will include a keynote talk, tutorials, a career panel, and breakout discussion groups at dinner.
Ishita Ganotra (Product Manager - Facebook), Keynote Speaker
Dr. Claire McKay Bowen (Lead Data Scientist - Privacy and Security, Urban Institute), Advanced Tutorial Leader
Dr. Anne Driscoll (Faculty in Dept. of Statistics, Virginia Tech), Beginner Tutorial Leader
Sponsorship Inquiries: We'd love to hear from your organization! If you are interested in partnering with WiDS Blacksburg at Virginia Tech, please contact Eileen Martin. Sponsorship packages start at $1000.
What is the cost?
This is a free event open to all members of the Virginia Tech community.
Do I need to register?
No. Registration will happen on-site when you arrive. We will allow in as many participants as we can fit safely in the room.
How much of a data science background do I need to participate?
We have made an effort to ensure that everyone (from those with no background to experts) can get something out of the whole event. Some parts (welcome reception, keynote talk, career panel) will include everyone, while other parts of the event will allow participants to break into smaller groups based on their personal interests and experience. We will have two tutorials at the same time: an introductory track and advanced track. During dinner we will have break-out discussion groups with a wide variety of technical/professional topics.
I’m not a woman. Should I participate?
Yes! Everyone is encouraged to participate. A major goal of the event is encouraging women in data science- a goal that is best achieved through engagement with the entire data science community. The keynote talk and tutorial talks are presented by women, but are focused on data science methods and applications. The professional development parts of the program will include a mix of gender-related and non-gender-related topics, and we urge all aspiring and current data scientists to actively engage in these important discussions.
I have a conflict for some of the event. Is it okay to attend just part of the event?
Yes. We’d love to have you join for any part of the event that you are available.
I’ve never been to this event. How should I dress?
Dress however you feel comfortable. Casual, smart casual or business casual are all common styles for an event like this.
Dr. Eileen Martin, Assistant Professor in Mathematics and CMDA, Faculty Sponsor
Iulia Voina, Statistics and Mathematics Student
Alyza Keo, Computational Modeling and Data Analytics Student
Carrie Choi, Computational Modeling and Data Analytics Student
Akanksha Dash, Computational Modeling and Data Analytics Student
Miranda Manka, Statistics Student
Jane Leetun, Statistics Alumna
Rylee Sweeney, Computational Modeling and Data Analytics Student
Pavani Surapaneni, Computational Modeling and Data Analytics Student
Louisse Bye, Mathematics Student
Allison Woods, Computational Modeling and Data Analytics Student
Yumeng Li, Computational Modeling and Data Analytics Student