This page describes WiDS Blacksburg at Virginia Tech in 2021. Follow this link for information about WiDS Blacksburg in 2020.
When: April 2, 2020, 5-6:45 pm
Update 3/11/20: Following Virginia Tech's transition to online courses for the remainder of spring semester, we will be transitioning the event to an online format. An updated schedule will be posted by Mar. 20.
Webinar Registration link:
https://forms.gle/oB8LrAuS4sqZwLLc9
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! We would also love to have Virginia Tech alumni call in. We expect that 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):
https://vtnews.vt.edu/articles/2019/02/Science-womenindata_blacksburg_2019conference.html
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 (online format):
5:00-5:10 Introduction video and welcome, Zoom registration link
5:10-5:45 Keynote talk (updated), "To Do Your Best, Be Yourself" by Meera Parat of Microsoft, same Zoom link as intro and welcome
5:50-6:40 Tutorial talks
- Introductory track tutorial, "Back to the Moon with the Help of Data Science," by Dr. Anne Driscoll of Virginia Tech, Zoom registration link
- Advanced track tutorial, "Differential Privacy: What is it?" led by Dr. Claire McKay Bowen of Urban Institute, Zoom registration link
Speakers:
Meera Parat (Data Scientist - Microsoft), 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), Introductory Tutorial Leader
At a future date, we look forward to hosting a talk by Ishita Ganotra (Product Manager - Facebook) on mixed reality
More info about the talks:
Keynote Talk "To Do Your Best, Be Yourself": This talk will be focusing on how it's everyone's hobbies, interests, backgrounds, and basically what makes everyone who they really are (ie. someone's interest in design, or big-picture thinking vs. technical wizardry) that allows people to bring their best selves to work and do their best work; and that in the end, this is what helps the most in (not just life) but career. I was going to tie in my experiences at work the last few years, and how with data (and data science) being such a new discipline in the industry right now, every company defines the various data roles in their own way, bringing us a unique opportunity to bring our own self to a role on a team.
Introductory Track Tutorial "Back to the Moon with the Help of Data Science": One of the major pillars of experimental design is sequential learning. The experimental design should not be viewed as a “one-shot” effort, but rather as a series of experiments where each stage builds upon information learned from the previous study. It is within this realm of sequential learning that experimentation soundly supports the application of the scientific method.
This presentation illustrates the value of sequential experimentation and also the connection between the scientific method and experimentation through a discussion of a multi-stage project supported by NASA’s Engineering Safety Center (NESC) where the objective was to assess the safety of composite overwrapped pressure vessels (COPVs). The analytical team was tasked with devising a test plan to model stress rupture failure risk in carbon fiber strands that encase the COPVs with the goal of understanding the reliability of the strands at use conditions for the expected mission life. This presentation highlights the recommended experimental design for the strand tests and then discusses the benefits that resulted from the suggested sequential testing protocol.
Advanced Track Tutorial "Differential Privacy: What is it?": With recent misuses of data access such as the Facebook - Cambridge Analytica Scandal, society raises valid data privacy concerns when private companies and other entities gather their information. Statistical disclosure control (SDC) or limitation are methods that aim to release high-quality data products while preserving the confidentiality of sensitive data. These techniques have existed within the statistics field since the mid-twentieth century, but, over the past two decades, the data landscape has dramatically changed. Data adversaries (or intruders) can more easily reconstruct datasets and identify individuals from supposedly anonymized data with the advances in modern information infrastructure and computation. While traditional methods of SDC and secure data centers are still used extensively, varying opinions about procedures have been developed across academia, government, and industry and in different countries. A definition known as Differential Privacy (DP) has garnered much attention, and many researchers and data maintainers are moving to develop and implement differentially private methods. In this talk, I will introduce and survey what DP is and the current challenges in applying differentially private methods to real world data.
Event Sponsors:
FAQs
What is the cost?
This is a free event open to all members of the Virginia Tech community.
Do I need to register?
You may register at any time prior to the event using the links provided at: https://forms.gle/oB8LrAuS4sqZwLLc9
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. The keynote talk 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.
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. 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.
Organizing Committee
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