| Tier structure of strongly endotactic reaction networks DF Anderson, D Cappelletti, J Kim, TD Nguyen Stochastic Processes and their Applications 130 (12), 7218-7259, 2020 | 33 | 2020 |
| Some network conditions for positive recurrence of stochastically modeled reaction networks DF Anderson, J Kim SIAM Journal on Applied Mathematics 78 (5), 2692-2713, 2018 | 26 | 2018 |
| Absolutely robust controllers for chemical reaction networks J Kim, G Enciso Journal of the Royal Society Interface 17 (166), 20200031, 2020 | 23 | 2020 |
| Stationary distributions of systems with discreteness-induced transitions E Bibbona, J Kim, C Wiuf Journal of The Royal Society Interface 17 (168), 20200243, 2020 | 22 | 2020 |
| Derivation of stationary distributions of biochemical reaction networks via structure transformation H Hong, J Kim, M Ali Al-Radhawi, ED Sontag, JK Kim Communications biology 4 (1), 620, 2021 | 20 | 2021 |
| Computational translation framework identifies biochemical reaction networks with special topologies and their long-term dynamics H Hong, BS Hernandez, J Kim, JK Kim SIAM Journal on Applied Mathematics 83 (3), 1025-1048, 2023 | 13 | 2023 |
| Stochastically modeled weakly reversible reaction networks with a single linkage class DF Anderson, D Cappelletti, J Kim Journal of Applied Probability 57 (3), 792-810, 2020 | 13 | 2020 |
| Stochastic models of nucleosome dynamics reveal regulatory rules of stimulus-induced epigenome remodeling J Kim, KM Sheu, QJ Cheng, A Hoffmann, G Enciso Cell reports 40 (2), 2022 | 6 | 2022 |
| Mixing times for two classes of stochastically modeled reaction networks DF Anderson, J Kim Mathematical Biosciences and Engineering 20 (3), 4690-4713, 2023 | 5 | 2023 |
| Accuracy of multiscale reduction for stochastic reaction systems G Enciso, J Kim Multiscale Modeling & Simulation 19 (4), 1633-1658, 2021 | 4 | 2021 |
| Embracing noise in chemical reaction networks G Enciso, J Kim Bulletin of mathematical biology 81, 1261-1267, 2019 | 4 | 2019 |
| Identifiability of stochastically modelled reaction networks G Enciso, R Erban, J Kim European Journal of Applied Mathematics 32 (5), 865-887, 2021 | 3 | 2021 |
| A path method for non-exponential ergodicity of Markov chains and its application for chemical reaction systems M Kim, J Kim arXiv preprint arXiv:2402.05343, 2024 | 2 | 2024 |
| Slack reactants: A state-space truncation framework to estimate quantitative behavior of the chemical master equation J Kim, J Dark, G Enciso, S Sindi The Journal of Chemical Physics 153 (5), 2020 | 2 | 2020 |
| Boundary-induced slow mixing for Markov chains and its application to stochastic reaction networks WTL Fan, J Kim, C Yuan arXiv preprint arXiv:2407.12166, 2024 | 1 | 2024 |
| A reaction network model of microscale liquid–liquid phase separation reveals effects of spatial dimension J Kim, SD Lawley, J Kim The Journal of Chemical Physics 161 (20), 2024 | | 2024 |
| Noise-robust chemical reaction networks training artificial neural networks S Kang, J Kim arXiv preprint arXiv:2410.11919, 2024 | | 2024 |
| Supervised Low-Rank Semi-nonnegative Matrix Factorization with Frequency Regularization for Forecasting Spatio-temporal Data K Kim, H Lyu, J Kim, JH Jung Journal of Scientific Computing 100 (2), 29, 2024 | | 2024 |
| A new path method for exponential ergodicity of Markov processes on Z d, with applications to stochastic reaction networks DF Anderson, D Cappelletti, WTL Fan, J Kim | | 2023 |
| Stochastically modeled reaction networks: positive recurrence and mixing times J Kim The University of Wisconsin-Madison, 2018 | | 2018 |