Dartmouth Events

Bio: Elizabeth Bondi is a PhD candidate in Computer Science at Harvard University advised by Prof. Milind Tambe. Her research interests include multi-agent systems, remote sensing, computer vision, and deep learning, especially applied to conservation and public health. Among her awards are Best Paper Runner up at AAAI 2021, Best Application Demo Award at AAMAS 2019, Best Paper Award at SPIE DCS 2016, and an Honorable Mention for the NSF Graduate Research Fellowship Program in 2017.

Abstract: AI is now being applied widely in society, including to support decision-making in important, resource-constrained efforts in conservation and public health. Such real-world use cases introduce new challenges, like noisy, limited data and human-in-the-loop decision-making. I show that ignoring these challenges can lead to suboptimal results in AI for social impact systems. For example, previous research has modeled illegal wildlife poaching using a defender-adversary security game with signaling to better allocate scarce conservation resources. However, this work has not considered detection uncertainty arising from noisy, limited data. In contrast, my work addresses uncertainty beginning in the data analysis stage, through to the higher-level reasoning stage of defender-adversary security games with signaling. I introduce novel techniques, such as additional randomized signaling in the security game, to handle uncertainty appropriately, thereby reducing losses to the defender. I show similar reasoning is important in public health, where we would like to predict disease prevalence with few ground truth samples in order to better inform policy, such as optimizing resource allocation. In addition to modeling such real-world efforts holistically, we must also work with all stakeholders in this research, including by making our field more inclusive through efforts like my nonprofit, Try AI.