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.
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.
Robotic grasp planning based on raw sensory data is difficult due to occlusion and incomplete scene geometry. Often one sensory modality does not provide enough context to enable reliable planning. A single depth sensor image cannot provide information about occluded regions of an object, and tactile information is incredibly sparse spatially. We are building a Deep Learning CNN that combines both 3D vision and tactile information to perform shape completion of an object seen from a single view only, and plan stable grasps on these completed models.
The ubiquity of current smart and IoT devices has the potential to transform healthcare. For example, current devices can measure continuously activity levels, heart rate, blood oxygen levels, and electrocardiogram. Our lab is developing new devices which can measure additional streams of health data which are currently not possible. The summer project will involve visualization of this entire set of data, machine learning, and multiparametric data analysis to extract trends that match health outcomes.
