Skip to main content

Research Opportunities

The Data Science (DS) Team supports the institute directive by empowering researchers and students to integrate data science tools and techniques into research across all disciplines. Here, you can find opportunities to get involved with currently active projects supported by the DS Team.

 

Available Research Opportunities:

Mchaourab Lab: Towards Enabling AI for Spectroscopy and Protein Folding

Mchaourab Lab: Towards Enabling AI for Spectroscopy and Protein Folding

Interested in biomedicine, protein folding, protein dynamics, biological function, or spectroscopy? Want to learn more about the intersection of these topics with artificial intelligence (AI), deep learning (DL), computation, and data science? This may be the opportunity for you! The Mchaourab Lab investigates mechanisms of protein folding, and currently welcomes…

Read more

Revolutionizing Learning Engagement through Technology: Talk to Einstein

Revolutionizing Learning Engagement through Technology: Talk to Einstein

Looking to work with training transformers for revolutionizing learning engagement in the humanities? Read on to learn more about a novel application by Dr. Ole Molvig – assistant professor of History and founder of the Emergent Technology Lab at the Wond’ry! About Talk To Einstein is an experiment in algorithmically…

Read more

Classification of Drug-Related Adverse Events

Classification of Drug-Related Adverse Events

Our team is interested in developing natural language processing (NLP) systems using transformers to classify whether patients have drug-related adverse events from patients’ clinical notes in Vanderbilt University Medical Center electronic health records (EHRs), which could be potentially associated with a specific drug of interest. The Initial approach to this…

Read more

Evaluation of Transfer Learning Performance of Transformer-Based models in Clinical Notes

Evaluation of Transfer Learning Performance of Transformer-Based models in Clinical Notes

Clinical notes and other free-text documents provide a breadth of clinical information that is not often available within structured data. Transformer-based natural language processing (NLP) models, such as BERT, have demonstrated great promise in using transfer learning to  improve clinical text processing. However, these models are commonly trained on generic corpora, which do not necessarily reflect many…

Read more

Analyzing British Periodicals to Understand Legal Discourse

Analyzing British Periodicals to Understand Legal Discourse

As part of a larger exploration of the British Culture of Litigation (from a literary perspective), we are working on developing text-mining techniques with the corpus of Proquest British Periodicals, which contains (of its total 3.4 mil) roughly a million articles in the relevant timeframe of 1770-1850 produced in several…

Read more