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!
A Fall 2018 internship is available in the Eaton lab to work on the development and application of machine learning approaches to historical evolutionary inference. Research will involve learning to use high performance distributed computing infrastructure, performing population genetic simulations, fitting machine learning models, and writing reproducible shareable code. The ideal candidate will have experience and interest in Python coding and a reasonable understanding of linear algebra.
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.
Networked systems are ubiquitous in modern society. In a dynamic social or biological environment, the interactions among subjects can undergo large and systematic changes. Due to the rapid advancement of technology, a lot of social networks are observed with time information. Some examples include the email communication network between users, comments on Facebook, the retweet activities on Twitter, etc. We aim to propose new statistical models and associated methodologies for various problems including community detection, change point detection and behavior prediction. The proposed methods will be evaluated on a wide range of network datasets in different areas.
