معرفی
Scot Adams is a Professor in the School of Mathematics at the University of Minnesota, with research spanning pure mathematics and machine learning applications. His primary academic affiliation is with the Mathematics Department at the University of Minnesota, where he maintains an active research program while managing phased retirement paperwork.
His research interests include dynamical systems, foliations, ergodic theory, hyperbolic groups, trees, Riemannian geometry, and more recently neural networks and thermodynamics. In his diary entries through August 2025, he documents ongoing work developing neural network slides, focusing on visual chain rule explanations, diagram of functions, and coordinate vector spaces.
His recent publications reflect a transition toward machine learning applications while maintaining his foundational mathematics work. The 15 most recent articles show increasing focus on neural network theory (2025), with earlier works covering financial mathematics, climate policy presentations, and pure mathematics topics like Banach-Tarski paradox and central limit theorem.
As an advisor, he works with graduate student Aarya Garimella on neural network research, meeting regularly to refine slides and explore mathematical foundations of neural networks. His teaching activities include courses in linear algebra and financial mathematics.
His diary reveals active engagement with climate policy through Citizens' Climate Lobby (CCL), including Capitol Hill lobbying efforts and local climate forum organization. He maintains detailed records of academic and civic activities while managing household responsibilities.
