I am interested in the careful application of well-crafted and principled machine learning algorithms for data exploration.
Currently, I am the Director of Machine Intelligence at Turnitin, leading a world class team of data scientists, machine learning researchers and engineers in building machine learning systems that enable educators to teach every student how to write with power, voice and conviction. Writing effectively is the strongest predictor of future academic and career success, and I am incredibly excited to be able to contribute to the educating and empowering the next generation of leaders, activists, artists, scientists and world changers.
Prior to joining Turnitin, I was the Principal Data Scientist at Chegg, Inc. working on large scale Natural Language Understanding and Knowledge Engines and designed its Modular AI Ecosystem. Previously, I was a research data scientist and applied statistician at Lawrence Livermore National Laboratory, where I led a team that researched semantic text embeddings for robust knowledge representations. My other research interests are in formalizing the theoretical underpinnings of unsupervised autoencoders as well as developing novel supervised deep learning architectures.
My PhD. was advised by Professor Lawrence Carin at Duke University in Durham, NC and focused on developing cross-modal spatial-temporal nonparametric Bayesian models. Following my PhD., I was a visiting research professor at the United States Naval Academy in Annapolis, MD where I explored the application of nonparametric Bayesian models for studying the propagation of coherent light sources in turbulence.
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