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Dandy Informational Session
10:00 AM to 12:00 PM
Schermerhorn Building, 3rd Floor, Student Learning Center
Dandy was created with one goal in mind: to modernize the dental lab process. Backed by some of the world’s leading venture capital firms, they're on an ambitious mission to simplify and modernize every function of the dental practice through technology, empowering clinicians and their teams with innovation. Their platform is designed to level up practices by making the entire process effortlessly digital — from start to finish. They are actively recruiting for various roles (more information TBA on specific roles).
Representatives will conduct an in-person Employer informational session and offer a presentation about their company, mission, past/upcoming projects, and future recruitment efforts, followed by a Q&A session. For more information about the company, please visit the company website: meetdandy.com. Registration Information will be posted via email and Handshake.
*EVENT AUDIENCE: CS Graduate Students (Ms/PhD) & Bridge Students.
What would it cost to end extreme poverty?
3:00 PM to 4:00 PM
CSB 453
Roshni Sahoo
We study poverty minimization via direct transfers, framing this as a statistical learning problem while retaining the information constraints faced by real-world programs. Using nationally representative household consumption surveys from 34 countries that together account for 76% of the world’s poor, we estimate that reducing the poverty rate to 1% (from a baseline of 13%) would cost $211B nominal per year. This is 4.0 times the corresponding reduction in the aggregate poverty gap, but only 19% of the cost of universal basic income. Extrapolated globally, the results imply a cost of 0.28% of global GDP to (approximately) end extreme poverty.
Roshni Sahoo is assistant professor in the Decision, Risk, and Operations Division at Columbia Business School. She works on data-driven decision making for social impact. Her research develops statistical methodology to address challenges in public health and economic development, combining tools from machine learning, causal inference, and optimization. Her work has appeared in leading operations research journals (such as Operations Research) and machine learning conferences (such as NeurIPS and ICLR), and has been featured by media outlets including The Economist and Vox. Roshni completed her PhD in Computer Science at Stanford University in 2026. She also received Bachelor of Science degrees in Computer Science and Mathematics at MIT in 2020.
Organized by the AI in the Public Interest group and the DAPLab
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