Tina Diao

Tina Diao

Ph.D. candidate, Decision and Risk Analysis

Publications

Towards More Consistent Liver Transplant Decisions in the Presence of COVID-19 (Working paper)

Published in Decision Making for Emerging Risks, Informs Decision Analysis Society, 2021

Abstract: We take a principled analytical approach to the surgeon’s decision of whether to perform a particular liver transplant when the patient might die from COVID-19. Our model can help surgeons make such decisions under uncertainty more consistently based on their judgments about the donor organ and their patient’s health and circumstances.

Recommended citation: Diao, T., Liu, W., Shachter, R., & Melcher, M. (2020). "Towards More Consistent Liver Transplant Decisions in the Presence of COVID-19. (Working paper)" Decision Making for Emerging Risks, Informs Decision Analysis Society.

Experimental Study of Big Raster and Vector Database Systems

Published in IEEE 37th International Conference on Data Engineering (ICDE), 2021

Abstract: Spatial data is traditionally represented using two data models, raster and vector. Raster data refers to satellite imagery while vector data includes GPS data, Tweets, and regional boundaries. While there are many real-world applications that need to process both raster and vector data concurrently, state-of-the-art systems are limited to processing one of these two representations while converting the other one which limits their scalability. This paper draws the attention of the research community to the research problems that emerge from the concurrent processing of raster and vector data. It describes three real-world applications and explains their computation and access patterns for raster and vector data. Additionally, it runs an extensive experimental evaluation using state-of-the-art big spatial data systems with raster data of up-to a trillion pixels, and vector data with up-to hundreds of millions of edges. The results show that while most systems can analyze raster and vector concurrently, but they have limited scalability for large-scale data.

Recommended citation: Singla, S., Eldawy, A., Diao, T., Mukhopadhyay, A., & Scudiero, E. (2021). "Experimental Study of Big Raster and Vector Database Systems" 2021 IEEE 37th International Conference on Data Engineering (ICDE) (pp. 2243-2248). https://ieeexplore.ieee.org/abstract/document/9458857

WildfireDB: A Spatio-Temporal Dataset Combining Wildfire Occurrence with Relevant Covariates

Published in NeurIPS 2020 Workshops - AI for Earth Sciences, 2020

Abstract: Modeling fire spread is critical in fire risk management. Creating data-driven models to forecast spread remains challenging due to the lack of comprehensive data sources that relate fires with relevant covariates. We present the first comprehensive dataset that relates historical fire data with relevant covariates extracted from satellite imagery. This open-source dataset contains over 2 million data points. We discuss an algorithmic approach based on large-scale raster and vector analysis that can be used to create similar datasets.

Recommended citation: Diao, T., Singla, S., Mukhopadhyay, A., Eldawy, A., Shachter, R., & Kochenderfer, M. (2020). "WildfireDB: A Spatio-Temporal Dataset Combining Wildfire Occurrence with Relevant Covariates". https://ai4earthscience.github.io/neurips-2020-workshop/papers/ai4earth_neurips_2020_43.pdf

Uncertainty Aware Wildfire Management

Published in AI for Social Good – AAAI Fall Symposium, 2020

Abstract: Recent wildfires in the United States have resulted in loss of life and billions of dollars, destroying countless structures and forests. Fighting wildfires is extremely complex. It is difficult to observe the true state of fires due to smoke and risk associated with ground surveillance. There are limited resources to be deployed over a massive area and the spread of the fire is challenging to predict. This paper proposes a decision-theoretic approach to combat wildfires. We model the resource allocation problem as a partially-observable Markov decision process. We also present a data-driven model that lets us simulate how fires spread as a function of relevant covariates. A major problem in using data-driven models to combat wildfires is the lack of comprehensive data sources that relate fires with relevant covariates. We present an algorithmic approach based on large-scale raster and vector analysis that can be used to create such a dataset. Our data with over 2 million data points is the first open-source dataset that combines existing fire databases with covariates extracted from satellite imagery. Through experiments using real-world wildfire data, we demonstrate that our forecasting model can accurately model the spread of wildfires. Finally, we use simulations to demonstrate that our response strategy can significantly reduce response times compared to baseline methods.

Recommended citation: Diao, T., Singla, S., Mukhopadhyay, A., Eldawy, A., Shachter, R., & Kochenderfer, M. (2020). "Uncertainty Aware Wildfire Management". https://arxiv.org/abs/2010.07915