2021 Women in Data Science @ Penn Conference Recap
Overview
The University of Pennsylvania was proud to host the second annual (first virtual) Women in Data Science (WiDS) @ Penn Conference on February 8-12, 2021. Over the course of the week, nearly 500 registrants had access to academic and industry talks, live speaker Q&A sessions, and networking opportunities in our virtual Gather.Town conference space.
This year’s theme – This is What a Data Scientist Looks Like – emphasized the depth, breadth, and diversity of data science, both in subject matter and personnel. A celebrated interdisciplinary event, WiDS @ Penn welcomed academic and industry speakers from across the data science landscape.
Hosted By
These Are What Data Scientists Look Like
WiDS By The Numbers
WiDS Day 1: Keynote
Kate Johnson, WG’94
President
Microsoft US
After welcoming remarks from deans Erika James (The Wharton School) and Vijay Kumar (Penn Engineering), attendees heard an inspiring talk from keynote speaker Kate Johnson, WG’94, President of Microsoft US, who spoke about the importance of technology as a change agent for inclusion. Following her talk, Kate answered questions from attendees, which was moderated by Mary Purk, Executive Director of Wharton Customer Analytics and AI for Business.
Welcoming Remarks
Erika James
Dean
The Wharton School
Vijay Kumar
Dean
Penn Engineering
WiDS Day 2: Speaker Sessions
Data Science in Radiology
and
Imaging Informatics
Tessa Cook
Assistant Professor of Radiology
Perelman School of Medicine
Social Network
Dependence and
the Replication Crisis
Betsy Ogburn
Associate Professor of Biostatistics
Johns Hopkins Bloomberg School of Public Health
WiDS Day 3: Industry Panel
Sponsored by Wharton Women in Business
Christine Cox
Didi Huang
Barkha Saxena
WiDS Day 4: Speaker Sessions
The Influence of Automated Accounts in the Spread of Information on Social Media
Sandra González-Bailón
How Humans Build Models of the World
Danielle Bassett
WiDS Day 5: Student Speaker Sessions
Comcast Customer Analysis
Ashley Clarke
COVID-19 Impact on Counties with Different Social-Economical Characteristics
Katherine Lin
Equalmodel: A Post-Processing Algorithm for Bias Reduction in Big Data Analytics
Brian Handen, Hyewon Lee, Margaret Ji, Tashweena Heeramun and Trishla Pokharna
Predicting Academic Success of Masters Students Using Application Data
Karen Shen
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