Tag: machineLearning

2022-23, Depression, General Psychology

Psychological Applications of Machine Learning: Quantifying the Risk of Prenatal Depression

Zhifei Zeng ’23 Current research suggests that factors such as socioeconomic deprivation, inadequate prenatal care, unplanned pregnancy, and psychosocial vulnerability such as stress may contribute to prenatal depression. PROMOTE is a newly developed screening tool that identifies psychosocial vulnerability in prenatal populations by assessing social determinants of health, social resources, stress and health behaviors. A research group led by Heidi Preis of Stony Brook University … Continue reading Psychological Applications of Machine Learning: Quantifying the Risk of Prenatal Depression

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Artificial Intelligence

Optimizing Movement-Based Behavior Networks in Artificial Intelligence

Ishmam Khan ’25 Deep learning involves the use of machinery to simulate biological phenomena, especially human behavior. Researchers have developed two systems of programming that proved useful in mimicking movements: convolutional neural networks (CNNs), which are based on virtual imagery and spatial information, and recurrent neural networks (RNNs), which adapt long-short term memory (LSTM) to model long term contextual information of temporal sequences.  When used … Continue reading Optimizing Movement-Based Behavior Networks in Artificial Intelligence

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Uncategorized

Deep(ly) Learning About Deep Learning

Ishmam Khan ’25 Machine learning is the ability of artificial intelligence to build a model based on previously collected data and use it to identify patterns in a way that simulates human behavior. Many applications branch off from machine learning, such as bioinformatics, the intersection of technology and biology. Recently, researchers at Stony Brook University studied a process called deep learning, a subset of machine … Continue reading Deep(ly) Learning About Deep Learning

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Figure 1. Downtown Manhattan, New York City, where high population density makes infectious disease spread easily.
2017-18

Predicting Flu With Mobility Behaviors

Gene Yang ‘19 In the mathematical modeling of infectious disease, determining the mobility of infectious diseases as well as the mobility patterns of individuals in a population is crucial to predicting the spatial and temporal diffusion of such illnesses. Researchers from the University of Trento gathered two types of data, mobility data and self-reported health data, to construct and validate a model that predicts the … Continue reading Predicting Flu With Mobility Behaviors

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