| Machine learning: applications of artificial intelligence to imaging and diagnosis JA Nichols, HW Herbert Chan, MAB Baker Biophysical reviews 11, 111-118, 2019 | 414 | 2019 |
| Quasi-Monte Carlo finite element methods for elliptic PDEs with lognormal random coefficients IG Graham, FY Kuo, JA Nichols, R Scheichl, C Schwab, IH Sloan Numerische Mathematik 131, 329-368, 2015 | 181 | 2015 |
| Fractional order compartment models CN Angstmann, AM Erickson, BI Henry, AV McGann, JM Murray, ... SIAM Journal on Applied Mathematics 77 (2), 430-446, 2017 | 62 | 2017 |
| Fast CBC construction of randomly shifted lattice rules achieving O (n− 1+ δ) convergence for unbounded integrands over Rs in weighted spaces with POD weights JA Nichols, FY Kuo Journal of Complexity 30 (4), 444-468, 2014 | 58* | 2014 |
| Greedy algorithms for optimal measurements selection in state estimation using reduced models P Binev, A Cohen, O Mula, J Nichols SIAM/ASA Journal on Uncertainty Quantification 6 (3), 1101-1126, 2018 | 50 | 2018 |
| Reduced basis greedy selection using random training sets A Cohen, W Dahmen, R DeVore, J Nichols ESAIM: Mathematical Modelling and Numerical Analysis 54 (5), 1509-1524, 2020 | 44 | 2020 |
| Optimal reduced model algorithms for data-based state estimation A Cohen, W Dahmen, R DeVore, J Fadili, O Mula, J Nichols SIAM Journal on Numerical Analysis 58 (6), 3355-3381, 2020 | 38 | 2020 |
| From stochastic processes to numerical methods: A new scheme for solving reaction subdiffusion fractional partial differential equations CN Angstmann, IC Donnelly, BI Henry, BA Jacobs, TAM Langlands, ... Journal of Computational Physics 307, 508-534, 2016 | 35 | 2016 |
| Nonlinear reduced models for state and parameter estimation A Cohen, W Dahmen, O Mula, J Nichols SIAM/ASA Journal on Uncertainty Quantification 10 (1), 227-267, 2022 | 25 | 2022 |
| A general framework for fractional order compartment models CN Angstmann, AM Erickson, BI Henry, AV McGann, JM Murray, ... SIAM Review 63 (2), 375-392, 2021 | 25 | 2021 |
| A discrete time random walk model for anomalous diffusion CN Angstmann, IC Donnelly, BI Henry, JA Nichols Journal of Computational Physics 293, 53-69, 2015 | 23 | 2015 |
| A quantitative comparison of anti-HIV gene therapy delivered to hematopoietic stem cells versus CD4+ T cells B Savkovic, J Nichols, D Birkett, T Applegate, S Ledger, G Symonds, ... PLoS computational biology 10 (6), e1003681, 2014 | 17 | 2014 |
| Topological deep learning: a review of an emerging paradigm A Zia, A Khamis, J Nichols, UB Tayab, Z Hayder, V Rolland, E Stone, ... Artificial Intelligence Review 57 (4), 77, 2024 | 15 | 2024 |
| HW, & Baker, MAB (2019) JA Nichols, H Chan Machine learning: Applications of artificial intelligence to imaging and …, 0 | 11 | |
| Subdiffusive discrete time random walks via Monte Carlo and subordination JA Nichols, BI Henry, CN Angstmann Journal of Computational Physics 372, 373-384, 2018 | 9 | 2018 |
| Leveraging ancestral sequence reconstruction for protein representation learning DM Matthews, MA Spence, AC Mater, J Nichols, SB Pulsford, M Sandhu, ... bioRxiv, 2023.12. 20.572683, 2023 | 2 | 2023 |
| Coarse reduced model selection for nonlinear state estimation J Nichols ANZIAM Journal 62, C192-C207, 2020 | 1 | 2020 |
| Quasi-Monte Carlo methods with applications to partial differential equations with random coefficients J Nichols UNSW Sydney, 2014 | 1 | 2014 |
| Greedy measurement selection for state estimation J Nichols, A Cohen, P Binev, O Mula ScienceOpen Posters, 2018 | | 2018 |
| Application of QMC methods to PDEs with random coefficients: a survey of analysis and implementation F Kuo, J Dick, T Le Gia, J Nichols, I Sloan, I Graham, R Scheichl, ... | | 2016 |
Frances Y. KuoUNSW AustraliaVerified email at unsw.edu.au
