-------------------------------- --- james ashton nichols ------- -------------------------------- --- mathematics ---------------- --- music, ibis ---------------- --------------------------------
email: james.nichols@anu.edu.au twitter: @james_nichols
--- moi --- ----------- I'm a lecturer at the Australian National University, I teach and research mathematics and applications to biology. I'm interested in approximation, numerical analysis, high dimensional problems, fractional & stochastic partial differential equations, stochastic processes, and statistical learning. Before my doctoral studies I was a quantative analyst at Macquarie Bank, Sydney Australia. I received my Ph.D in 2014, having studied with Ian Sloan and Frances Kuo, at UNSW, Sydney, Australia. I did my postdoc at the Laboratoire Jacques-Louis Lions, Sorbonnes Université, with Albert Cohen. I play some banjo and a bit of piano. I try to code elegantly. I love riding and tinkering with bikes. See my GitHub (although a lot of my code has gone to GitLab lately) Google Scholar ~~~ I am organising the 9th Workshop on High Dimensional Approximation at the Mathematical Sciences Institute, ANU, 20-24 Feb 2023! ~~~ --- teaching --- ---------------- o Semester 1 2023 I am teaching MATH 3062/6116 Fractal Geometry and Chaotic Dynamics. o Semester 2 2023 I am teaching a masters/graduate reading course MATH 8702. The readings cover various topics in optimal transport, including portions of Santambrogio's book Optimal Transport for Applied Mathematicians as well as Peyré and Cuturi's book Computational Optimal Transport. --- selected publications --- ----- (full list here) ------ ----------------------------- o Nonlinear reduced models for state and parameter estimation (axXiv) A Cohen, W Dahmen, O Mula, JA Nichols SIAM/ASA Journal of Uncertainty Quantification, 2022 o A General Framework for Fractional Order Compartment Models CN Angstmann, AM Erickson, BI Henry, AV McGann, JM Murray, JA Nichols SIAM Review, 2021 o Coarse reduced model selection for nonlinear state estimation JA Nichols ANZIAM Journal, 2021 o Reduced basis greedy selection using random training sets A Cohen, W Dahmen, R DeVore, JA Nichols ESAIM: M2AN, 2020 o Optimal reduced model algorithms for data-based state estimation (arXiv) A Cohen, W Dahmen, R DeVore, J Fadili, O Mula, JA Nichols SIAM Numer. Anal., 2020 o Greedy algorithms for optimal measurements selection in state estimation using reduced models (pdf) P Binev, A Cohen, O Mula, JA Nichols SIAM/ASA Journal of Uncertainty Quantification, 2018 o Subdiffusive discrete time random walks via Monte Carlo and subordination (arXiv) JA Nichols, BI Henry, CN Angstmann Journal of Computational Physics, 2018 o Ph.D Thesis --- music and projects --- -------------------------- Occasional host at The Random Sample Podcast The Dubtable Brackets Psychic Synth Svelt Malcolm Turnbot Pattern Machine Book-Bike-Machine