Our goal is to use deep learning networks to understand which neurons in the brain encode fine motor movements in mice. We collected large datasets entailing calcium imaging data of active neurons and high-resolution videos when mice perform motor tasks. We want to use recent advances in deep learning to (1) estimate the poses of mouse body parts at a high spatiotemporal resolution (2) extract behaviorally-relevant information and (3) align them with neural activity data. Behavioral video analysis is made possible by transfer learning, the ability to take a network that was trained on a task with a large supervised dataset and utilize it on a small supervised dataset. This has been used e.g. in a human pose–estimation algorithm called DeeperCut. Recently, such algorithms were tailored for use in the laboratory in a Python-based toolbox known as DeepLabCut, providing a tool for high-throughput behavioral video analysis.

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This position is associated with a new research project co-funded by the Earth Institute Frontiers seed funding and the UN Development Programme in Guinea. Its goal is to develop remote sensing technologies to track environmental impact from bauxite mining in rural communities and to help establish protocols for their use. We seek a research assistant who can (1) develop image classification to be applied to satellite imagery to determine extent and abundance of bauxite dust within specific communities in the Boké region (2) develop methods to make classified images available for download to smartphones in the field and (3) adapt existing application technologies to allow users in the field to upload field photographs and locations of potential impacts they identify. The resulting map would represent a composite of satellite imaged areas of impact verified in the field and documentation of other areas of impact not visible by satellite but already identified in the field.

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Research on: (i) COSMOS cloud connected vehicles, (ii) Monitoring of traffic intersections, using bird’s eye cameras, supported by ultra-low latency computational/communications hubs; (iii) Simultaneous video-based tracking of cars and pedestrians, and prediction of movement based on long-term observations of the intersection; (iv) Real-time computational processing, using deep learning, utilizing GPUs, in support of COSMOS applications; (v) Sub-10ms latency communication between all vehicles and the edge cloud computational/communication hub, to be used in support of autonomous vehicle navigation. The research is performed using the pilot node of project COSMOS infrastructure.

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The objective of this project is to construct linkages across disparate public health data systems using machine learning tools and assess them for bias and equitable representation of subpopulations defined by demographic and socioeconomic factors.

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42% of New York City greenhouse gas emissions result from on-site fossil fuel combustion in residential and commercial buildings; space heating is, by far, the majority contributor. Both New York State and NYC have policies to dramatically reduce emissions that will require a transformation in the way buildings are heated, including major efforts in existing buildings. This transition is inextricably linked to existing energy equity issues that we believe significantly overlap across NYC (and elsewhere). These include unreliable heating in the winter, susceptibility to extreme heat (an increasing occurrence with climate change) and struggles to afford energy needs. Various known data sources for NYC are available, though they are disparate and have not been analyzed holistically. Further, we believe there are potential engineering and policy solutions to these challenges. In this project, the DSI scholar will access (and search for where not yet known to qSEL researchers) relevant data sets, analyze those data sets to identify communities exposed to all or a subset of these issues, and assist qSEL researchers in developing models to evaluate possible solutions. 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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Contestation over language use is an unavoidable feature of American politics. Yet, despite the rise of language policing on both sides of the aisle, we know surprisingly little about how ordinary citizens respond to norms governing language use from both in-group and out-group members. Following Munger (2017), I would like to leverage social media platforms such as Reddit and Twitter to evaluate whether injunctions to use particular words (e.g., undocumented immigrant, Latinx) are effective. I plan to use an experimental approach, where conditional on mentions of “illegal alien” or “Hispanic/Latino,” users are randomly assigned to receive a “language correction.” Outcome measures would include subsequent use of corrected terms, valence of user responses, and upvoting/liking/RTing behavior.

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Decoding behavioral signifiers for choice and memory can have far reaching implications for understanding actions and identifying disease. We use a four arm maze where we are able to observe choices and infer memory in mice, but have access to very few pre-determined behavioral signifiers. Several recent publications implemented computer vision to extract a variety of previously unreachable aspects of behavioral analysis, including animal pose estimation (Mathis et al., 2018) and distinguishable internal states (Calhoun et al., 2019). These descriptions allowed for the identification and characterization of dynamics, which then revealed an unprecedented richness to the behaviors that determine decision making. Applying such computational approaches to examine behavior in our maze in the context of behaviors that have been validated to measure choice and memory can reveal dimensions of behavior that predict or even determine these psychological constructs. DSI scholars would use pose estimation analysis to evaluate behavioral signifiers for choice and memory and relate it to our real time concurrent measures of neural activity and transmitter release. The students would also have opportunity to examine the effect of disease models known to impair performance on our maze task on any identified signifier.

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