The introduction of a new technology provides individuals and organizations with a large, unowned, and limitless space for communication and organization. How do individuals use or misuse this space in their decision making? Using online discussion platforms, we will analyze what types of discussions thrive - those with depth of discussion or topical complexity or those with cohesive contours? We’ll ask, are there high status actors who are particularly good at recognizing topic gaps which need new conversations? Using social psychological theories with a large-scale archival dataset, we’ll learn more about the impact of new technologies on group decision-making processes.

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The Quadracci Sustainable Engineering Lab (qSEL) has several research efforts related to the low-carbon energy transition, including pathways to decarbonize building space heating. Recent work has produced large model data sets that have supported recent journal articles. Several maps have been produced using QGIS and data has been made public, but user functionality is limited. While we continue to build on these efforts, we also want to make our results and data available more widely for other researchers and policymakers. The large data sets (10 years of hourly data for more than 72,000 census tracts and six scenarios) and different spatial aggregations (e.g. states and electricity planning/operating regions) present challenges. In this project, the DSI Scholar would first work with qSEL researchers to develop an interactive web interface to display maps of relevant analyses and allow users to produce time series data from the underlying models. Additional research would include further analysis at a regional level – likely New York State – to refine the current model based on additional intraregional and energy source data. The project has the possibility of extending through Summer 2020, subject to fundraising efforts and the success of the Spring 2020 project.

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The amount of video content that is being distributed over the Internet is increasing. Video providers rely on HTTP adaptive streaming approaches to deliver video clips to users. Complementary to the video provider, the service provider must determine the priority of each network stream. As part of the project, students will explore wireless network assisted strategies for http adaptive streaming by use of TOS/DSCP. This includes using machine-learning tools to analyze network video traffic and the design of reinforcement learning algorithms to improve users' video Quality of Experience.

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A major challenge to implementing precision medicine arises from patients who share a clinical diagnosis but have different biological causes of disease. Disease subtypes that arise from obscure etiological heterogeneity create inefficiencies in healthcare and attenuate power in clinical trials and research studies. The ability to stratify patients into biologically homogenous subgroups improves the potential for translational research by allowing us to design more powerful studies.

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The research project examines how Inclusionary Zoning (IZ) impacts the housing markets of neighborhoods where it is implemented. The study focuses on the impacts of IZ in New York City and Paris, two cities at the forefront of adopting IZ in their respective countries.

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We are constantly exposed to inputs from the outside world, but we do not perceive everything we are exposed to. Some inputs are rather weak: we might perceive them at one point in time, but not at another. The state of our brains right before we receive such sensory inputs influences whether or not we perceive them. Brain oscillations are proposed to play a key role in setting these brain states; however, how exactly these brain rhythms influence our perception remains a topic of active research.

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Tax evasion is one of the main sources of informal economic activity and has drastic effects on different macroeconomic variables. However, due to various reasons, it is difficult to directly measure the extent of tax evasion. This project aims to develop a novel way of measuring aggregate tax evasion in national economies using Twitter feeds. To this end, using carefully selected keywords in different national languages, we will collect country and regional level data from Twitter feeds in different frequencies for a large cross section of economies and then construct a measure of tax evasion using the collected data. In addition to fully describing the collected dataset, the project will also examine the evolution of the constructed series.

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