Orianna Demasi

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We aim here to leverage supervised learning to enable large-scale analysis of performance logs, in order to accurately classify code runs and understand the importance of different performance metrics. Previous work has demonstrated structured communication patterns in high performance codes. By categorizing these patterns, we can identify what code was(More)
A new trend in medicine is the use of algorithms to analyze big datasets, e.g. using everything your phone measures about you for diagnostics or monitoring. However, these algorithms are commonly compared against weak baselines, which may contribute to excessive optimism. To assess how well an algorithm works, scientists typically ask how well its output(More)
Ensemble methods for supervised machine learning have become popular due to their ability to accurately predict class labels with groups of simple, lightweight “base learners.” While ensembles offer computationally efficient models that have good predictive capability they tend to be large and offer little insight into the patterns or structure in a(More)
Researchers are exploring the ability to infer complex signals, such a mental wellbeing, from easily collected smartphone behavioral data. Rather than focusing on improving overall accuracy of such an approach, we seek to understand when we are and are not capable of predicting an individual's wellbeing. In particular, we consider the ability to predict(More)
Personalizing interventions and treatments is a necessity for optimal medical care. Recent advances in computing, such as personal electronic devices, have made it easier than ever to collect and utilize vast amounts of personal data on individuals. This data could support personalized medicine; however, there are pitfalls that must be avoided. We discuss(More)
The results of 4 years (1981-1984) of monitoring airborne pollen concentration, using a volumetric trap, in the atmosphere of Ascoli Piceno (central Italy) are reported. Pollen production wasn't constant during the studied years, showing in 1982 and 1984 a very high concentration in comparison to 1981 and 1983. This is due to a dry period from April to June(More)
BACKGROUND Automatically tracking mental well-being could facilitate personalization of treatments for mood disorders such as depression and bipolar disorder. Smartphones present a novel and ubiquitous opportunity to track individuals' behavior and may be useful for inferring and automatically monitoring mental well-being. OBJECTIVE The aim of this study(More)
BACKGROUND Cognitive Behavioral Therapy (CBT) for depression is efficacious, but effectiveness is limited when implemented in low-income settings due to engagement difficulties including nonadherence with skill-building homework and early discontinuation of treatment. Automated messaging can be used in clinical settings to increase dosage of depression(More)
Depression is the most common mental disorder and is negatively impactful to individuals and their social networks. Passive sensing of behavior via smartphones may help detect changes in depressive symptoms, which could be useful for tracking and understanding disorders. Here we look at a passive way to detect changes in depressive symptoms from data(More)
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