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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Hi there! My name is Tina.
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Posts
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experiences
projects
publications
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
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
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
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.
teaching
Decision Analysis Courses (TA)
Undergraduate and graduate courses, Stanford University, Department of Management Science & Engineering, 2016
Coherent approach to decision making, using the metaphor of developing a structured conversation having desirable properties, and producing actional thought that leads to clarity of action. Socratic instruction; computational problem sessions. Emphasis is on creation of distinctions, representation of uncertainty by probability, development of alternatives, specification of preference, and the role of these elements in creating a normative approach to decisions. Information gathering opportunities in terms of a value measure. Relevance and decision diagrams to represent inference and decision. Principles are applied to decisions in business, technology, law, and medicine.
Probability Courses (TA)
Undergraduate and graduate courses, Stanford University, Department of Management Science & Engineering, 2018
Concepts and tools for the analysis of problems under uncertainty, focusing on structuring, model building, and analysis. Examples from legal, social, medical, and physical problems. Topics include axioms of probability, probability trees, random variables, distributions, conditioning, expectation, change of variables, and limit theorems.
Introduction to Operations Management (PI)
Graduate course, Stanford University, Department of Management Science & Engineering, 2020
Operations management focuses on the effective planning, scheduling, and control of manufacturing and service entities. Achieving operations excellence is essential to improving efficiency and effectiveness. This course introduces students to a broad range of key issues in the operations function of a firm. Topics include production planning, optimal timing and sizing of capacity expansion, inventory control, supply chain management, revenue management as well as modern operations tools that involve game theoretic considerations and machine learning.