Past Events

  • 2022 Nov 14

    Conrad Tucker (Carnegie Mellon University)

    11:00am to 12:00pm

    Location: 

    In person and Zoom, register here: https://forms.gle/d4cvu4wA3RLFDjeu5

    Talk Title: Using AI to Advance Mobile Digital Health in Resource-Constrained Environments

    Abstract: The emergence of ubiquitous mobile computing devices and Artificial Intelligence (AI) algorithms is transforming patient care, hereby making it more scalable and accessible. This is of particular importance in resource-constrained environments (RCEs) where access to healthcare technologies or expertise may be limited. The sensing modalities (e.g., camera/gyroscope) of mobile devices are capable of capturing and storing data that can then be...

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  • 2022 Oct 04

    AI and Social Impact in South Asia

    10:00am to 11:30am

    Location: 

    On zoom, register here: https://mittalsouthasiainstitute.harvard.edu/event/ai-and-social-impact-in-south-asia/

    The rise of artificial intelligence technology and the modernization of the South Asia region have occurred in parallel over the past few decades – now, they are growing increasingly interconnected, with wide-ranging impacts and consequences. Join us for this miniseries as we explore how AI technology becomes an integral part of South Asian society and consider the potential opportunities and concerns of its proliferation.

    The first panel in this miniseries will explore the social impact of AI in the region. As AI is used to address social, environmental, and economic...

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  • 2022 May 02

    Miguel Hernan (Harvard T. H. Chan School of Public Health)

    11:00am to 12:00pm

    Location: 

    Zoom conference - Register at https://forms.gle/6761am3KA2QxeaxX7

    “Using healthcare databases to learn what works when no randomized trials exist”
    Making clinical decisions among several courses of action requires knowledge about their causal effects. Randomized trials are the preferred method to quantify those causal effects. When randomized trials are not available, causal effects are often estimated from observational data. Therefore, causal inference from observational data can be...

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  • 2022 Apr 25

    Nisarg Shah (University of Toronto)

    11:00am to 12:00pm

    Location: 

    Zoom conference - Register at https://forms.gle/6761am3KA2QxeaxX7

    Designing Optimal Voting Rules

    A central task in voting is to aggregate the ranked preferences of voters over a set of alternatives (candidates) to select a winning alternative. However, despite centuries of research, the natural question of which voting rule is “best” has remained elusive. A recent approach from computer science offers hope. By proposing a natural quantitative measure of the “efficiency” of a voting rule, called distortion, it allows us to define and seek the most efficient voting rule.

    In a series of joint works, we...

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  • 2022 Apr 20

    The Future of AI for Social Impact

    12:00pm to 1:30pm

    Location: 

    Zoom conference - Register at https://harvard.zoom.us/meeting/register/tJ0sdO6hqzksHdfwuiJGBbH99c9eFv8j1zV0

    The key thrust behind the fast emerging area of AI for Social Impact (AISI) has been to apply AI research to address societal challenges. AI has great potential to provide tremendous societal benefits, having been successfully deployed in areas spanning public health, environmental sustainability, education, public welfare, among many others. In AI, we have just recently begun to define this topic as its own area of research, and we have just started...

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  • 2022 Apr 11

    Joshua Blumenstock (University of California, Berkeley)

    11:00am to 12:00pm

    Location: 

    Zoom conference - Register at https://forms.gle/6761am3KA2QxeaxX7

    Can Machine Learning and Mobile Phone Data Improve the Targeting of Humanitarian Assistance?

    Targeting is a central challenge in the administration of anti-poverty programs: given available data, how does one rapidly identify the individuals and families with the greatest need? Here we show that non-traditional “big” data from satellites and mobile phone networks can improve the targeting of anti-poverty programs. Our analysis compares outcomes – including exclusion errors, total social welfare, and measures of fairness – under different targeting regimes. Relative to...
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