A common challenge for students in heavy proof-based courses is to come up with a long sequence of logical arguments from the problem statement to the final solution. In doing so, they can often skip steps leading to logical leaps or downright incorrect solutions. Ideally the instructor should identify these mis-steps and help students master such proof-based course material. Here we want to take a data-driven approach to address this challenge.
Despite the promise of predictive analytics in healthcare, the lack of continuous internal sensing devices has impeded its realization. With the exception of CGMs, no current commercially available wearable devices yield information intimate to the body. To overcome this deficiency, our research group has developed a minimally invasive wearable device capable of continuous monitoring of glucose and electrolytes in the superficial layer of the skin in an extremely minimally invasive manner.
The development of computational data science techniques in natural language processing (NLP) and machine learning (ML) algorithms to analyze large and complex textual information opens new avenues to study intricate processes, such as government regulation of financial markets, at a scale unimaginable even a few years ago. This project develops scalable NLP and ML algorithms (classification, clustering and ranking methods) that automatically classify laws into various codes/labels, rank feature sets based on use case, and induce best structured representation of sentences for various types of computational analysis.
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.
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.