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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…

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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…

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Analyzing Audio Files to Determine Therapy Efficacy

Analyzing Audio Files to Determine Therapy Efficacy

Current Project: Using automatic speech recognition to transcribe audio files from patients with depression to determine efficacy of therapy. Before and after outcomes will be assessed using telephone audio files where participants were called and asked what they were “thinking” at the moments just before the call. These audio files…

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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…

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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…

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