Ben Schmidt is a prominent figure in the digital humanities and data science communities, known for his innovative work in text analysis, data visualization, and the application of computational methods to historical and cultural data. With a background as a historian (PhD from Princeton University), he has made significant contributions to understanding large-scale text corpora and has developed widely-used tools. He previously taught at Northeastern University as an Assistant Professor of History and was the Director of Digital Humanities at New York University Libraries. Currently, Ben is the VP of Information at Nomic, where he focuses on building tools to help people understand and interact with massive AI-generated and unstructured datasets, most notably through their product Atlas. He is a strong advocate for open data and accessible computational tools for research.
Ben Schmidt's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Developed Bookworm, an open-source platform for dynamically exploring trends in vast collections of digitized texts, enabling new forms of literary and historical analysis by visualizing word usage over time.
Leads information strategy and contributes to the development of Atlas, Nomic's flagship product for visualizing and interacting with massive unstructured datasets and AI embeddings, making complex data more accessible.
Recognized as an influential voice and practitioner in the field of digital humanities, bridging historical scholarship with computational techniques, and known for insightful visualizations and analyses of cultural trends.
Frequently writes and speaks on topics related to data visualization, text analysis, the history of data, and the societal impact of AI and large datasets, contributing to public and academic discourse.
Princeton University - Year 2005
Harvard University - Year 1999
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Nomic AI is an artificial intelligence company focused on building tools for understanding and interacting with massive unstructured datasets. They aim to make AI more accessible and interpretable, developing open-source models and platforms like Atlas for visualizing and exploring AI embeddings, and GPT4All, an ecosystem for running powerful, open-source large language models locally on everyday hardware. Their work empowers developers and researchers to build and deploy AI applications more effectively.
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