Last year one of my graduate students developed a novel algorithm for detecting “weird” signals in photometric time series, such as those taken by NASA’s Kepler Mission and now TESS. An undergraduate students will work in my team to run the algorithm on TESS data, which is just starting to be released publicly (https://heasarc.gsfc.nasa.gov/docs/tess/status.html). We hope to detect strange signatures, possibly including analogs to Tabby’s Star, interacting binaries and perhaps even technosignatures.

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This project works with a novel corpus of text-based school data to develop a multi-dimensional measure of the degree to which American colleges and universities offer a liberal arts education. We seek a data scientist for various tasks on a project that uses analysis of multiple text corpora to better understand the liberal arts. This is an ongoing three-year project with opportunities for future collaborations, academic publications, and developing and improving existing data science and machine learning skills.

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In a globalized world we live in today consequences of catastrophic events easily transgress national borders. Whether it’s a natural disaster, a war or an economic crisis it’s likely to spread out and affect all of us. We propose a framework to model global risks that is not bound to any specific model and is a hybrid of human and machine intelligence. The core of this approach is in using Bayesian Nets of causalities constructed by an analyst equipped with text mining and a map of economic, political and business interconnections.

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CTV’s core mission is to facilitate the transfer of inventions from academic labs to the market for the benefit of society. In a typical year, CTV receives ~400 inventions, completes ~100 licenses and options, and helps form ~20 startups. A good video summary of CTV is here: https://vimeo.com/110193999.

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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.

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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.

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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.

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