| Optimal feedback controlled assembly of perfect crystals X Tang, B Rupp, Y Yang, TD Edwards, MA Grover, MA Bevan ACS nano 10 (7), 6791-6798, 2016 | 124 | 2016 |
| Distinct timescales of RNA regulators enable the construction of a genetic pulse generator EF A Westbrrok, X Tang, R Marshall, C Maxwell, J Chappell, D Agrawal, M ... Biotechnology and Bioengineering, 2019 | 67 | 2019 |
| Controlling assembly of colloidal particles into structured objects: Basic strategy and a case study MA Bevan, DM Ford, MA Grover, B Shapiro, D Maroudas, Y Yang, ... Journal of Process Control 27, 64-75, 2015 | 53 | 2015 |
| Mathematical modeling of RNA-based architectures for closed loop control of gene expression DK Agrawal, X Tang, A Westbrook, R Marshall, CS Maxwell, J Lucks, ... ACS synthetic biology 7 (5), 1219-1228, 2018 | 46 | 2018 |
| Optimal design of a colloidal self-assembly process Y Xue, DJ Beltran-Villegas, X Tang, MA Bevan, MA Grover IEEE Transactions on Control Systems Technology 22 (5), 1956-1963, 2014 | 36 | 2014 |
| A comparison of open-loop and closed-loop strategies in colloidal self-assembly X Tang, J Zhang, MA Bevan, MA Grover Journal of Process Control 60, 141-151, 2017 | 28 | 2017 |
| Optimal feedback control of batch self-assembly processes using dynamic programming MA Grover, DJ Griffin, X Tang, Y Kim, RW Rousseau Journal of Process Control 88, 32-42, 2020 | 27 | 2020 |
| The construction and application of Markov state models for colloidal self-assembly process control X Tang, MA Bevan, MA Grover Molecular Systems Design & Engineering 2 (1), 78-88, 2017 | 27 | 2017 |
| Control of microparticle assembly X Tang, MA Grover Annual Review of Control, Robotics, and Autonomous Systems 5 (1), 491-514, 2022 | 21 | 2022 |
| Design and evaluation of synthetic RNA-based incoherent feed-forward loop circuits S Hong, D Jeong, J Ryan, M Foo, X Tang, J Kim Biomolecules 11 (8), 1182, 2021 | 20 | 2021 |
| Colloidal self-assembly with model predictive control X Tang, Y Xue, MA Grover 2013 American Control Conference, 4228-4233, 2013 | 20 | 2013 |
| Convolutional neural network-based colloidal self-assembly state classification A Lizano, X Tang Soft Matter 19 (19), 3450-3457, 2023 | 16 | 2023 |
| Negatively competitive incoherent feedforward loops mitigate winner-take-all resource competition A Stone, J Ryan, X Tang, XJ Tian ACS synthetic biology 11 (12), 3986-3995, 2022 | 15 | 2022 |
| Recent Advances in Reinforcement Learning for Chemical Process Control VS Devarakonda, W Sun, X Tang, Y Tian Processes 13 (6), 1791, 2025 | 10 | 2025 |
| Model-based investigation of the relationship between regulation level and pulse property of I1-FFL gene circuits J Ryan, S Hong, M Foo, J Kim, X Tang ACS synthetic biology 11 (7), 2417-2428, 2022 | 9 | 2022 |
| Externally directing self-assembly with dynamic programming DJ Griffin, X Tang, MA Grover 2016 American Control Conference (ACC), 3086-3091, 2016 | 7 | 2016 |
| MOLA: Enhancing Industrial Process Monitoring Using a Multi-Block Orthogonal Long Short-Term Memory Autoencoder F Ma, C Ji, J Wang, W Sun, X Tang, Z Jiang Processes 12 (12), 2824, 2024 | 6 | 2024 |
| Control of self-assembly with dynamic programming MA Grover, DJ Griffin, X Tang IFAC-PapersOnLine 52 (1), 1-9, 2019 | 5 | 2019 |
| Markov decision process based time-varying optimal control for colloidal self-assembly X Tang, MA Bevan, MA Grover IFAC-PapersOnLine 49 (7), 430-435, 2016 | 4 | 2016 |
| Grain boundary control in colloidal self-assembly with dynamic programming X Tang, Y Yang, MA Bevan, MA Grover 2014 American Control Conference, 1120-1125, 2014 | 4 | 2014 |
