Patricia Solís, Gautam Dasarathy, Pavan Turaga, Alexandria Drake, Kevin Jatin Vora, Akarshan Sjasa, Ankith Raaman, Sarbeswar Praharaj, Lattus, Robert (2023)
Examining the COVID Crisis from a Geographical Perspective. Routledge
Abstract: The uneven outcomes of the COVID-19 pandemic in the United States can be characterized by its patchwork patterns. Given a weak national coordinated response, state-level decisions offer an important frame for analysis. This article explores how such analysis invokes fundamental geographic challenges related to the modified areal unit problem, and results in scientific predictive models that behave differently in different states. We examined morbidity with respect to state-level policy decisions, by comparing the fit and significance of different types of predictive modeling using data from the first wave of 2020. Our research reflects upon public health literature, mathematical modeling, and geographic approaches in the wake of the underlying complex pattern of drivers, decisions, and their impact on public health outcomes state by statetime line. Contemplating these findings, we discuss the need to improve integration of fundamental geographic concepts to creatively develop modeling and interpretations across disciplines that offer value for both informing and holding accountable decision makers of the jurisdictions in which we live.
Characterizing the Performance of Machine Learning Algorithms: A Study and Novel Techniques
Lattus, Robert (2021)
Barrett, the Honors College Thesis
Abstract: Classification in machine learning is quite crucial to solve many problems that the world is presented with today. Therefore, it is key to understand one's problem and develop an efficient model to achieve a solution. One technique to achieve greater model selection and thus further ease in problem solving is estimation of the Bayes Error Rate. This paper provides the development and analysis of two methods used to estimate the Bayes Error Rate on a given set of data to evaluate performance. The first method takes a "global" approach, looking at the data as a whole, and the second is more "local"--partitioning the data at the outset and then building up to estimation of the three Bayes Error Rate when the dataset is at high dimension, while the other method provides accurate estimation at large sample size. This second conclusion, in particular, can have signification ramifications of "big data" problems, as one would be able to clarify the distribution with an accurate estimation of the Bayes Error Rate by using this method.
Multi-Agent Data Collection with Distributed Stochastic Coordination for Wireless Data Delivery (2025)
Coordination of Distributed Agents Through Stochastic Policies in a Cooperative Jamming Scenario (2025)