Barnas Monteith

Barnas Monteith co-founded and runs THInc-AI, a STEM research organization, focused on the use of machine learning in EdTech.

As a student, Barnas conducted research at Harvard University's OEB focused on the intersection of predictive data science and life science. He later started several successful technology companies focusing on software, predictive healthcare, and the manufacture of synthetic diamond materials for the semiconductor, lighting and energy industries.

Barnas currently conducts academic research on the use of LLM's, and diffusion models in the generation of Edtech materials, predominantly focused on leading edge STEM fields such as quantum, DLT and metaverse. He serves as a key senior personnel in multiple NSF projects, and is the organizational Principle Investigator for a current NSF project (Logic-DS) focused on the intersection of mathematics logic, programming (Python, R) and data science, with a focus on AI training datasets.

Barnas has served in various government policy recommendation capacities, including on the Massachusetts Department of Education Math & Science Advisory Council and the inaugural Governor’s STEM Advisory Council, as Chair of its Public Awareness subcommittee. He was the youngest and longest serving leader of the MIT Massachusetts Science & Engineering Fair. He has authored a number of books, patents, peer-reviewed / scientific articles in a variety of areas of Artificial Intelligence, life science, energy/semiconductors and materials science and speaks regularly at AI and STEM education events and conferences throughout the world. He is presently collaborating with multiple universities on a project to develop a new method for machine learning automation of semantic alignment for qualitative research. His current work includes studying language and cultural advantages within LLMs, as well as developing AI context wrappers for improved personalization and leveling in learning content distribution, and related digital finance models.

Barnas' current research is focused on the intersection of AI and future technologies such as quantum information sciences and metaverse content delivery, combined with digital finance tools such as blockchain.

He is presently leading a research project to investigate the use of a token-for token model in the generation and mass distribution of personalized educational content, based on standards-aligned frameworks. This project will enhance the current mode of educational content delivery through the improved AI-enhanced digitization of adaptive learning materials at scale, decreasing costs and improving the transparency of both public and private fund expenditures in the Edtech sector. Tokenization of this customized content equates to a finer resolution of control over expenditures and greater accountability for governments as well as private OST providers; greater efficiencies in Edtech using this method will also lead to improvements in speed of delivery, with higher quality, more dynamic content and this translates to measurable improvements in student performance.