Workshop on AI & Analytics for Social Good

May 10, 2024

This workshop brings together academics, thought leaders and stakeholders to discuss how analytics can support nonprofit organizations, government entities and social impact organizations in improving their reach and impact through innovative use of data and models. The overarching theme is analytics for doing good.

The conference will be held at the Smith School of Business at the University of Maryland. Registration will close on April 30, 2024.

Confirmed Speakers

Dimitris Bertsimas
Boeing Leaders for Global Operations Professor of Management
Associate Dean for Business Analytics
Professor, Operations Research
MIT Sloan
Justin Boutilier
Assistant Professor
University of Wisconsin – Madison
Miranda Bogen
Founding Director, AI Governance Lab
Center for Democracy & Technology
Catherine Lilian Nakalembe
University of Maryland
Daniel Rock
Assistant Professor of Operations, Information and Decisions
Wharton School of the University of Pennsylvania
Adam Shoemaker
Oversight Board Liaison
Meta
Alp Sungu
Assistant Professor of Operations, Information and Decisions
Wharton School of the University of Pennsylvania
Lily Xu
PhD student
Harvard University

Organizing Committee

Important Dates

  1. Abstract deadline: March 8, 2024
  2. Notification of Acceptance: April 1, 2024
  3. Conference Date: May 10, 2024

Submission Instructions

We invite scholars at universities and other research institutions working on analytics (broadly defined) for good as well as policymakers who frame their policies using business analytics. We are agnostic about the topic of research provided it fits into the workshop theme. We particularly encourage young scholars, junior faculty and PhD students to submit papers.

Submission Requirements: Please submit a 3-page extended abstract (page limit excludes references). In the submission, please include the title of the paper and the names and email addresses of all of the authors, and highlight the research questions/goals, novelty, methodological approach, main results, and impact or potential for impact.

Abstracts should be submitted by email to socialimpactanalytics@umd.edu.

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