Analyze data from one of the following library applications/systems and create visualizations that highlight the most important findings pertaining to the support of self-directed learning: Vialogues (TC Video Discussion Application), PocketKnowledge (TC Online Archive), DocDel (E-Reserve System), Pressible (Blogging Platform), Library Website and Mobile App.
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.
Understand interconnected nature of global multi-national companies via their supply chain, product and services competition, co-investments and co-ownerships as well as other dependencies between operations and revenue streams. We would like to consider the way news on any company specifically propagate down the connection graph and impact other businesses that are related in a way that is not necessarily explicit.
The DSI Center for Data, Media & Society is seeking undergraduate and masters students during the summer to work on projects at the intersection of Computer Science, Data Science, and the humanities. These projects will combine domain expertise in the humanities with computer and data science techniques to tackle important societal and media problems. Projects can vary from documenting human rights violations, providing rural farmers with financial safety-nets, analyzing the sources of social media popularity, and more!
Given calcium imaging data of active neurons, can we detect groups of co-firing neurons, called neuronal ensembles? We have a number of datasets consisting of hundreds of neurons imaged for thousands of time steps, and seek to extend an existing CRF model to consider temporal relationships. The goal is to be able to detect neuronal ensembles that span multiple time steps, and that are not conditioned on external stimuli.
Project components: (i) Monitoring of traffic intersections, using bird’s eye cameras, supported by ultra-low latency computational/communications hubs; (ii) Simultaneous video-based tracking of cars and pedestrians, and prediction of movement based on long-term observations of the intersection; (iii) Real-time computational processing, using deep learning, utilizing GPUs, in support of ii; (iv) Sub-10ms latency communication between all vehicles and the computational/communication hub, to be used in support of autonomous vehicle navigation.
Using machine learning to conduct brain state classification at real-time on EEG/fNIRS/fMRI data.
