Artificial Intelligence in Scholarly Research, Part 3: AI in the Academic Publishing Ecosystem

This session was recorded live on June 17, 2020.

Description: Join us for a conversation between Ruth Pickering, Co-Founder and COO at Yewno, and James W. Weis, a researcher in the MIT Media Lab, the founder and lead of the MIT Network Intelligence Program, and a doctoral candidate in the MIT Computational and Systems Biology program, on the topic of artificial intelligence (AI) in the academic publishing ecosystem.

Ruth Pickering is the curator of a new title in the Charleston Briefings series, and James W. Weis is the author of a chapter on the same subject as the webinar.

“Technology underpins all aspects of today’s academic publishing ecosystem. But only recently have AI-based platforms—systems that employ learning and other humanlike or rule-based behavior—begun to affect scholarly publishing. However, the vast amounts of digital content and data output by the publishing ecosystem provide fertile ground for the application of artificial intelligence (AI) and machine learning (ML) methodologies to the production and consumption of research content. On the one hand, data-driven algorithms can provide consumers of academic literature with more efficient ways of identifying and—subsequently—assessing the value and relevance of research content via smarter search, better disambiguation, and improved metrics. On the other hand, academic publishers can use AI techniques to assist in intelligent targeting and curation of research, such as post-publication impact measures, sentiment analysis, and improved methods of plagiarism detection and reproducibility testing.”

This session will be recorded and made available on the conference website at a later time. Registration is required to attend and space is limited.

Speaker Bio: James W. Weis is a researcher in the MIT Media Lab, the founder and lead of the MIT Network Intelligence Program, and a doctoral candidate in the MIT Computational and Systems Biology program.

James’s research focuses on the application of artificial intelligence and network science to maximizing human efficiency—especially with respect to catalyzing innovation and entrepreneurship. James has published peer reviewed research in fields ranging from computational biology and machine learning to technology transfer and hackathon design, and his most recent work includes developing large-scale, AIbased methods to predict, and then allocate resources across, the most impactful areas of scientific research and development.

James is also actively involved in the application of his research. He was the Founding CEO of Nest.Bio Labs, a multi-country incubator that provides shared laboratory infrastructure to biotechnology startups, a Founding Partner at Nest.Bio Ventures, an early-stage biotechnology-focused venture capital firm, Founder of the MIT Alumni Life Science Angels of Boston, Founding President of the MIT Biotech Group (which now has over 2,000 members and is the largest biotechnology-focused organization at MIT), and, previously, a quantitative trader in New York City.

James is a graduate of MIT’s Computer Science and Artificial Intelligence Laboratory, where he received his S.M. for designing machine learning algorithms to engineer next-generation biocatalysts, and of Brown University’s Computer Science Department, where he graduated with highest Latin and Departmental honors in Computational Biology and Computer Science.