[AI Seminar] AI Seminar sponsored by Apple -- Rediet Abebe -- Dec. 11th

[Han Zhao]

Dear faculty and students:

Rediet will be on campus for the whole day tomorrow, please sign up a time
slot on the google sheet (
https://docs.google.com/spreadsheets/d/1oUZGO2VqE1F2uqsWmeu6HgyPRmT6nPkxyuRdOojDVRU/edit#gid=0)
if you'd like to meet and talk with her.

Best,
Han.

Han Zhao <han.zhao at cs.cmu.edu> 于2018年12月9日周日 上午11:18写道：

> Dear faculty and students:
>
> We look forward to seeing you next Tuesday, Dec. 11th, at noon in *Univ.
> Center, Danforth Conference Room *for AI Seminar sponsored by Apple. To
> learn more about the seminar series, please visit the website.
> On Tuesday, Rediet Abebe will give the following talk:
>
> *Title: **Computational Interventions to Improve Access to Opportunity
> for Disadvantaged Populations*
>
> *Abstract: *Poverty and economic hardship are highly complex and dynamic
> phenomena. Due to the multi-dimensional nature of economic welfare,
> assistance programs aimed at improving access to opportunity for
> disadvantaged populations face challenges when relevant information about
> these populations is unavailable or (even when such information is
> available) when they are forced to rely on simplistic measures of welfare
> (e.g., household income or wealth). In this presentation, we explore
> algorithmic and computational challenges that arise in this settings.
>
> In the first part of the talk, we explore one important dimension of
> economic welfare: susceptibility to income shocks in the form of an
> unexpected bill or disruption of one's income flow. We introduce and
> analyze a model of economic welfare that incorporates income, wealth, and
> external shocks and poses the question of how to allocate subsidies in this
> setting. We find that we can give optimal allocation mechanisms under
> natural assumptions on the agent's wealth and shock distributions, as well
> as approximation schemes in the general setting.
>
> In the second part of the talk, we consider settings in which relevant
> information -- such as individuals' information needs -- is not available.
> Focusing on the lack of comprehensive, high-quality data about the health
> information needs of individuals in developing nations, we propose a
> bottom-up approach that uses search data from individuals in all 54 nations
> in Africa. We analyze Bing searches related to HIV/AIDS, malaria, and
> tuberculosis; these searches reveal diverse health information needs that
> vary by demographic groups and geographic regions. We also shed light on
> discrepancies in the quality of content returned by search engines.
>
> We conclude with a discussion on how algorithmic, computational, and
> mechanism design techniques can help inform interventions to improve access
> to opportunity in relevant domains and the Mechanism Design for Social Good
> research initiative.
>
> This talk is based on joint work with Shawndra Hill, Jon Kleinberg, H.
> Andrew Schwartz, Peter M. Small, Jennifer Wortman Vaughan, and S. Matthew
> Weinberger.
>
> *Bio*: Rediet Abebe is a Ph.D. candidate in computer science at Cornell
> University, advised by Professor Jon Kleinberg. Her research focuses on
> algorithms, AI, and applications to social good. She uses computational
> insights to improve access to opportunity, with a focus on under-served and
> marginalized communities. As part of this research mission, she co-founded
> and co-organizes the Mechanism Design for Social Good (MD4SG) initiative,
> an interdisciplinary, multi-institutional research group. She is also a
> co-founder and co-organizer of Black in AI, a transcontinental group aimed
> at increasing the presence and inclusion of Black researchers in the field
> of AI. Her research is deeply influenced by her upbringing in her hometown
> of Addis Ababa, Ethiopia, where she lived until moving to the U.S. in 2009.
> Her work has been generously supported by fellowships and scholarships
> through Facebook, Google, the Cornell Graduate School, and the
> Harvard-Cambridge Fellowship.
> --
>
> *Han ZhaoMachine Learning Department*
>
>
> *School of Computer ScienceCarnegie Mellon UniversityMobile: +1-*
> *412-652-4404*
>


-- 

*Han ZhaoMachine Learning Department*


*School of Computer ScienceCarnegie Mellon UniversityMobile: +1-*
*412-652-4404*
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