| 8:30am-10:00am | Session: Poverty and Infrastructure |
| | Improving Power Generation Efficiency using Deep Neural Networks
Stefan Hosein and Patrick Hosein
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| | Machine Learning Across Cultures: Modeling the Adoption of Financial Services for the Poor
Muhammad Raza Khan and Joshua E. Blumenstock
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| | Predicting Ambulance Demand: Challenges and Methods
Zhengyi Zhou
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| | Predicting Student Dropout in Higher Education
Lovenoor Aulck, Nishant Velagapudi, Joshua Blumenstock, and Jevin West
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| | Understanding Innovation to Drive Sustainable Development
Prasanna Sattigeri, Aurélie Lozano, Aleksandra Mojsilović, Kush R. Varshney, and Mahmoud Naghshineh
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10:30am-12:00pm
| Session: Engagement for Social Good
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| | Keynote: Machine Learning for Public Policy and Social Good: Case Studies, Challenges, and Opportunities
Rayid Ghani
Can machine learning help reduce police violence and misconduct? Can it help prevent children from getting lead poisoning? Can it help cities better target limited resources to improve lives of citizens? We're all aware of the machine learning hype right now but turning this hype into any social impact takes effort. In this talk, I'll discuss lessons learned while working on dozens of projects over the past few years with non-profits and governments on high-impact social challenges. These lessons span from challenges these organizations face when trying to use machine learning, to understanding how to effectively train and build cross-disciplinary teams to do practical machine learning, as well as what machine learning and social science research challenges need to be tackled, and what tools and techniques need to be developed in order to have a social and policy impact with machine learning.
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| | Designing Intelligent Automation based Solutions for Complex Social Problems
Sanjay Podder, Janardan Misra, Senthil Kumaresan, Neville Dubash, and Indrani Bhattacharya
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| | Harnessing the Power of the Crowd to Increase Capacity for Data Science in the Social Sector
Peter Bull, Isaac Slavitt, and Greg Lipstein
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| | Rapid-Fire Introduction to Data Science for Social Good Organizations and Opportunities
Confirmed Panelists: JeanCarlo Bonilla, Peter Bull, Rayid Ghani, Gideon Mann, Saška Mojsilović, and Tushar Rao |
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| 1:30pm-3:00pm | Session: Media and Methodology
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| | Twitter as a Source of Global Mobility Patterns for Social Good
Mark Dredze, Manuel Garcia-Herranz, Alex Rutherford, and Gideon Mann
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| | Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News
Elena Erdmann, Karin Boczek, Lars Koppers, Gerret von Nordheim, Christian Pölitz, Alejandro Molina, Katharina Morik, Henrik Müller, Jörg Rahnenführer, and Kristian Kersting
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| | Quantifying and Reducing Stereotypes in Word Embeddings
Tolga Bolukbasi, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai
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| | Cyberbullying Identification Using Participant-Vocabulary Consistency
Elaheh Raisi and Bert Huang
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| | Fast Robustness Quantification with Variational Bayes
Ryan Giordano, Tamara Broderick, Rachael Meager, Jonathan Huggins, and Michael Jordan
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| 3:30pm-4:30pm | Session: Disease
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| | Formal Concept Analysis of Rodent Carriers of Zoonotic Disease
Roman Ilin and Barbara A. Han
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| | Machine Learning for Antimicrobial Resistance
John W. Santerre, James J. Davis, Fangfang Xia, and Rick Stevens
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| | Predicting Novel Tick Vectors of Zoonotic Diseases
Barbara A. Han and Laura Yang
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