Arseny Moskvichev

Arseny Moskvichev serves as a Research Fellow at the Santa Fe Institute, collaborating with Melanie Mitchell on measuring abstraction and analogy-making capabilities in AI systems. His work bridges Cognitive Science and Machine Learning to model how language and abstraction enable human knowledge sharing, with the goal of developing NLP systems capable of learning through natural dialogue beyond initial training phases.

He holds a B.Sc. in Psychology and M.Sc. in Neuroscience from Saint Petersburg University, completed a two-year Machine Learning and Software Development program at the Computer Science Center, and earned an M.Sc. in Statistics and Ph.D. in Cognitive Science from UC Irvine under Mark Steyvers.

Moskvichev employs behavioral studies, emergent communication simulations, and novel NLP architecture development to investigate language's role in knowledge transfer. His research specifically targets enabling AI systems to update long-term beliefs via conversation, reflecting his vision for "meaningful" human-AI interaction. He actively promotes mathematical skill development through self-study groups and created a Russian-language Neural Networks course on stepic.org.

No scientific awards or current advising activities were documented in the source material.

As a core member of the Santa Fe Institute's research community, Moskvichev contributes to interdisciplinary projects at the intersection of cognitive science, artificial intelligence, and complex systems theory, leveraging SFI's collaborative environment for foundational AI research.