Given the recent attention to dams in developing countries as a means to efficiently utilize water resources, mitigating the negative environmental and social impacts they have on riparian states is of utmost importance. This thesis presents a global review of how basin countries, through international water treaties (IWT), ensure that...
The utility of high-throughput, computational screening has become an invaluable asset to the field of materials science. In the hierarchy of computational methods, the most accurate methods are often the most computationally expensive. However, as both the efficiency and fidelity of numerical techniques advance, high-quality screening of large materials datasets...
Listeria monocytogenes is the third most deadly foodborne pathogen in the United States. The young and elderly, as well as pregnant and immunocompromised people are the population most susceptible to serious illness and death from listeriosis infections.
Unlike most foodborne pathogens, L. monocytogenes does not live a solely enteric lifestyle....
Deep learning is now being utilized widely in applications where sensitive data is being used for model training, for example, in health care. In this scenario, any data leakage will cause privacy concerns to whose data records are used to train the model. An attacker can actively cause privacy leakage...
Multi-robot teams offer promising solutions for many long term deployments in remote and dangerous domains, such as extraterrestrial or underseas exploration. However, long term deployments present many problems preventing robot teams from operating effectively. Learning over long time scales is makes it difficult to assign credit to robots' actions, as...
Interactions between proteins are essential to life, driving and regulating a majority of processes within all living cells. Study of protein-protein interactions reveals that some proteins act as hubs within networks of interactions, binding to many partner proteins. These hubs therefore are of particular importance to understanding protein function, interwoven...
Iterative algorithms are simple yet efficient in solving large-scale optimization problems in practice. With a surge in the amount of data in past decades, these methods have become increasingly important in many application areas including matrix/tensor recovery, deep learning, data mining, and reinforcement learning. To optimize or improve iterative algorithms,...