Software Engineering for the Mobile Application Market

One of the goals of the current United States government is to lower healthcare costs. One of the solutions is to alter the behavior of the population to be more physically active and to eat healthier. This project will focus on the latter solution by writing applications for the Android and iOS mobile platforms that will allow a user to monitor their dietary intake to see and correct patterns in their eating behavior

Statistical Machine Translation Features with Multitask Tensor Networks

We present a three-pronged approach to improving Statistical Machine Translation (SMT), building on recent success in the application of neural networks to SMT. First, we propose new features based on neural networks to model various non-local translation phenomena. Second, we augment the architecture of the neural network with tensor layers that capture important higher-order interaction among the network units. Third, we apply multitask learning to estimate the neural network parameters jointly. Each of our proposed methods results in significant improvements that are complementary. The overall improvement is +2.7 and +1.8 BLEU points for Arabic-English and Chinese-English translation over a state-of-the-art system that already includes neural network features.Comment: 11 pages (9 content + 2 references), 2 figures, accepted to ACL 2015 as a long pape

Excess death rates for Republicans and Democrats during the COVID-19 pandemic

Political affiliation has emerged as a potential risk factor for COVID-19, amid evidence that Republican-leaning counties have had higher COVID-19 death rates than Democrat-leaning counties and evidence of a link between political party affiliation and vaccination views. This study constructs an individual-level dataset with political affiliation and excess death rates during the COVID-19 pandemic via a linkage of 2017 voter registration in Ohio and Florida to mortality data from 2018 to 2021. We estimate substantially higher excess death rates for registered Republicans when compared to registered Democrats, with almost all of the difference concentrated in the period after vaccines were widely available in our study states. Overall, the excess death rate for Republicans was 5.4 percentage points (pp), or 76%, higher than the excess death rate for Democrats. Post-vaccines, the excess death rate gap between Republicans and Democrats widened from 1.6 pp (22% of the Democrat excess death rate) to 10.4 pp (153% of the Democrat excess death rate). The gap in excess death rates between Republicans and Democrats is concentrated in counties with low vaccination rates and only materializes after vaccines became widely available

A Data Science Approach to Understanding Residential Water Contamination in Flint

When the residents of Flint learned that lead had contaminated their water system, the local government made water-testing kits available to them free of charge. The city government published the results of these tests, creating a valuable dataset that is key to understanding the causes and extent of the lead contamination event in Flint. This is the nation's largest dataset on lead in a municipal water system. In this paper, we predict the lead contamination for each household's water supply, and we study several related aspects of Flint's water troubles, many of which generalize well beyond this one city. For example, we show that elevated lead risks can be (weakly) predicted from observable home attributes. Then we explore the factors associated with elevated lead. These risk assessments were developed in part via a crowd sourced prediction challenge at the University of Michigan. To inform Flint residents of these assessments, they have been incorporated into a web and mobile application funded by \texttt{Google.org}. We also explore questions of self-selection in the residential testing program, examining which factors are linked to when and how frequently residents voluntarily sample their water.Comment: Applied Data Science track paper at KDD 2017. For associated promotional video, see https://www.youtube.com/watch?v=0g66ImaV8A

Functional and Categorical Analysis of Waveshapes Recorded on Microelectrode Arrays

Dissociated neuronal cell cultures grown on substrate integrated microelectrode arrays (MEAs) generate spontaneous activity that can be recorded for up to several weeks. The signature wave shapes from extracellular recording of neuronal activity display a great variety of shapes with triphasic signals predominating. I characterized extracellular recordings from over 600 neuronal signals. I have preformed a categorical study by dividing wave shapes into two major classes: (type 1) signals in which the large positive peak follows the negative spike, and (type 2) signals in which the large positive peak precedes the negative spike. The former are hypothesized to be active signal propagation that can occur in the axon and possibly in soma or dendrites. The latter are hypothesized to be passive which is generally secluded to soma or dendrites. In order to verify these hypotheses, I pharmacologically targeted ion channels with tetrodotoxin (TTX), tetraethylammonium (TEA), 4-aminopyridine (4-AP), and monensin