Injury, such as falls, motor vehicle crashes, and drug overdose, is a major source of morbidity and mortality. The interaction between injury and disease is complex and mutually causative. For instance, patients with Alzheimer’s Disease or Parkinson’s Disease are known to be at heightened risk of hip fracture from falls and in turn injurious falls among these patients can drastically alter the trajectory of the disease. So far, research on injury-disease interaction has been scant and fragmented. The proposed project is aimed at uncovering the gestalt of the relations between different injuries and different diseases through a data science approach.

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The objective is to use new large cloud-resolving simulations to try and better represent cloud processes in coarse-resolution climate models (~100km in horizontal resolution). Those simulations are global (spanning the entire globe) at 2km resolution and 30-minute output. The data will be hosted on google cloud platform (Pangeo) (the data size is about 50TB). We will in particular evaluate the impact of using Constitutional Neural Network (in time and space) and the capacity for out of sample prediction.

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The ocean has absorbed the equivalent of 41% of industrial-age fossil carbon emissions. In the future, this rate of this ocean carbon sink will determine how much of mankind’s emissions remain in the atmosphere and drive climate change. To quantify the ocean carbon sink, surface ocean pCO2 must be known, but cannot be measured from satellite; instead it requires direct sampling across the vast and dangerous oceans. Thus, there will never be enough observations to directly estimate the carbon sink as it evolves. Data science can fill this gap by offering robust approaches to extrapolate from sparse observations to full coverage fields given auxiliary data that can be measured remotely.

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A common challenge for students in heavy proof-based courses is to come up with a long sequence of logical arguments from the problem statement to the final solution. In doing so, they can often skip steps leading to logical leaps or downright incorrect solutions. Ideally the instructor should identify these mis-steps and help students master such proof-based course material. Here we want to take a data-driven approach to address this challenge.

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Despite the promise of predictive analytics in healthcare, the lack of continuous internal sensing devices has impeded its realization. With the exception of CGMs, no current commercially available wearable devices yield information intimate to the body. To overcome this deficiency, our research group has developed a minimally invasive wearable device capable of continuous monitoring of glucose and electrolytes in the superficial layer of the skin in an extremely minimally invasive manner.

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The development of computational data science techniques in natural language processing (NLP) and machine learning (ML) algorithms to analyze large and complex textual information opens new avenues to study intricate processes, such as government regulation of financial markets, at a scale unimaginable even a few years ago. This project develops scalable NLP and ML algorithms (classification, clustering and ranking methods) that automatically classify laws into various codes/labels, rank feature sets based on use case, and induce best structured representation of sentences for various types of computational analysis.

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Predicting preterm birth in nulliparous women is challenging and our efforts to develop predictors for that condition from environmental variables produce insufficient classifier accuracy. Recent studies highlight the involvement of common genetic variants in length of pregnancy. This project involves the development of a risk score for preterm birth based on both genetic and environmental attributes.

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