CSSS Fall 2024 Seminar Series (10/2/24 – 12/4/24)
Posted: 10/8/2024 ()
The CSSS Seminar features local and visiting scholars presenting current research at the intersection of statistics and the social sciences. Seminars are held on Wednesdays from 12:30-1:30 pm in room SAV 409 during an academic year. Seminars are available to anyone interested and are being presented in a hybrid format. To attend a seminar virtually, please register here. An email with login information will be sent to you upon registration. Graduate students pursuing a CSSS track may receive credit by enrolling in CS&SS 590. Questions? Contact CSSS (csss@uw.edu).
Upcoming Seminar:
Wednesday, October 16th, 12:30pm-1:30pm
NOTE: This seminar will be offered as a remote ZOOM session only. Find the Zoom link here.
Mayana Pereira, Microsoft AI for Good Research Lab
Opening Microsoft Data for Social Good: privacy-preserving technologies unlocking powerful social insights
In this talk you will learn how privacy preserving data disclosure technologies can unlock powerful social insights. The talk will cover two of Microsoft’s differentially private data releases: the broadband data and the digital literacy data. These data sets, created using Microsoft’s private data, bring powerful insights to the current state digital divide in the United States.
Mayana is a Data Scientist at Microsoft AI for Good Research Lab – a philanthropic team of data scientists and researchers dedicated to using AI, Machine Learning and statistical modeling to tackle some of humanity’s greatest challenges. Microsoft AI for Good Research Lab partners with leading nonprofits, research institutions, NGOs, and governments to accelerate work across the AI for Good program portfolio—Earth, Accessibility, Humanitarian Action, Cultural Heritage, Health—as well as other pressing issues such as affordable housing, broadband access, digital skills, justice reform, legal compliance, etc.
Mayana’s research is currently focused on the intersection of digital safety/cybersecurity/software security and artificial intelligence, as well as the impacts of privacy-preserving techniques in machine learning deployment scenarios. Mayana is an active collaborator of OpenDP, an open-source project for the differential privacy community to develop general-purpose, vetted, usable, and scalable tools for differential privacy.