"Leveraging Data for Generosity: GivingPulse and the GivingTuesday Data Commons"
Friday 27 September, noon (EDT)
On Friday 27 September 2024, noon (EDT), we host Annie Collins, GivingTuesday, on “Leveraging Data for Generosity: GivingPulse and the GivingTuesday Data Commons”.
All welcome: https://utoronto.zoom.us/j/84277066292
Annie is a Data Scientist at GivingTuesday, a US-based nonprofit focused on researching generosity and charitable giving behaviours. Beyond GivingTuesday, Annie has spent several years in data management and research roles within the Canadian nonprofit sector. She holds a Bachelors of Science in applied mathematics and statistics from the University of Toronto, and uses her experience to provide data for the public good and support a more data-driven social sector worldwide.
The American non-profit sector faces significant challenges in fully understanding the complex landscape in which it operates, particularly when it comes to accessing reliable data on individual giving in the U.S. At the same time, there is increasing interest in gaining a more comprehensive view of the generosity ecosystem—one that encompasses not only monetary donations to formal non-profits but also volunteerism, in-kind contributions, advocacy, and peer-to-peer giving. To this end, this presentation will explore the objectives of the GivingTuesday Data Commons, its core features, and potential use cases. Additionally, it will introduce the GivingPulse project, an ongoing weekly survey that tracks a broad spectrum of individual giving behaviours and related attitudes.
ICYMI
Last week’s talk is available here. The key takeaway for me was that combining LLMs with interdisciplinary collaboration can improve the accessibility and usability of large, complex datasets. The presenters established a nice implementation that others could adopt.
Links
The University of Toronto Map & Data Library is offering Fall Workshops. Topics include GIS, Data Visualization, Qualitative Data Analysis, Research Data Management, and more. You can sign up here.
I've been enjoying reading Nuclear Navy, 1946-1962, by Richard G. Hewlett and Francis Duncan. You can read a legitimate free PDF here.
Upcoming
Friday 4 October 2024, noon (EDT)
Sean Taylor, Motif
Sean Taylor is a data scientist, social scientist, statistician, and software developer. He mostly specializes in methods for solving causal inference and business decision problems, and is particularly interested in building tools for practitioners working on real-world problems. He is a co-founder and chief scientist at Motif.
Friday 11 October 2024, noon (EDT)
Rona Fang-Yu Hu, University of Michigan
“Predicting the Unobserved: Integrating ‘Something Else’ Sexual Identity Responses in Health Disparity Studies Using Machine Learning and Resampling Techniques”
Rona Hu is a second-year Master’s student in the Michigan Program in Survey and Data Science. She graduated from National Chengchi University with a B.S. in Psychology. Before coming to the United States for her graduate studies, she was a Research Associate and Chief Operating Officer at Quanthon Corporation in Taiwan. She has presented papers at the conference of the American Association of Public Opinion Research and the Joint Statistical Meetings since 2022.
Friday 18 October 2024, noon (EDT)
Xiaojun Su, Unilever
Xiaojun Su is a Machine Learning Lead, Horizon 3 Labs, Unilever where she leads cross-functional teams of data engineers, software developer, data scientists, postgraduate researchers, and 3rd party vendors to launch in-house models to drive significant ROIs. She holds a M.Sc from the University of Toronto.
Friday 25 October 2024, noon (EDT)
Jay Alammar, Cohere
“Hands-On Large Language Models: Language Understanding and Generation”
Jay Alammar is Director and Engineering Fellow at Cohere (pioneering provider of large language models as an API). In this role, he advises and educates enterprises and the developer community on using language models for practical use cases).
Friday 1 November 2024, noon (EDT)
TBA
Friday 8 November 2024, noon (EST)
Jacob Baldwin, Pro Football Focus (PFF)
Jacob Baldwin is a Senior Data Scientist at PFF. He holds an online M.S. degree in Applied Mathematics from the University of Washington, and graduated from Clarkson University with a B.S. in Physics, a B.S. in Applied Mathematics, and a minor in Computer Science.
Wednesday 13 November 2024, noon (EST)
Sayash Kapoor, Princeton
“CORE-Bench and other AI agent use cases”
Sayash Kapoor is a computer science Ph.D. candidate at Princeton University’s Center for Information Technology Policy. His research focuses on the societal impact of AI. He has previously worked on AI in industry and academia, including Facebook, Columbia University, and EPFL Switzerland. He received a best paper award at ACM FAccT, an impact recognition award at ACM CSCW, and was included in TIME’s inaugural list of the 100 most influential people in AI. His recently released book with Arvind Narayanan, AI Snake Oil, looks critically at what AI can and cannot do.
Friday 22 November 2024, noon (EST)
Yiqin Fu, Stanford University
Born and raised in China, Yiqin Fu spent many of her formative years in the U.S. and the U.K. Yiqin (pronounced ee-ching) is studying towards a Ph.D. in political science at Stanford University, after having worked as a research associate at Yale Law School’s Paul Tsai China Center in New Haven, Connecticut and Beijing, China. She holds a B.A. in Philosophy, Politics, and Economics from the University of Oxford and is broadly interested in innovation, U.S.-China relations, and comparative political and electoral systems.
Friday 29 November 2024, noon (EST)
Caroline Weis, gsk.ai
Caroline Weis is a Senior AI/ML Engineer and team lead at gsk.ai. In 2021, she completed her PhD in Machine Learning for Computational Biology and Healthcare at ETH Zurich. Her research interests lie in the development of personalized healthcare through data analysis and machine learning on medical and biological data.