| Towards Self-Driving Processes: A Deep Reinforcement Learning Approach to Control S Spielberg, A Tulsyan, N Lawrence, P Loewen, B Gopaluni AIChE Journal 65 (6), 1-20, 2019 | 125 | 2019 |
| State-of-charge estimation in lithium-ion batteries: A particle filter approach A Tulsyan, Y Tsai, B Gopaluni, R Braatz Journal of Power Sources 331 (11), 208–223, 2016 | 122 | 2016 |
| Multiple model approach to nonlinear system identification with an uncertain scheduling variable using EM algorithm L Chen, A Tulsyan, B Huang, F Liu Journal of Process Control 23 (10), 1480-1496, 2013 | 69 | 2013 |
| A Deep Learning Architecture for Predictive Control SSP Kumar, A Tulsyan, B Gopaluni, P Loewen IFAC-PapersOnLine 51 (18), 512-517, 2018 | 67 | 2018 |
| On simultaneous on-line state and parameter estimation in non-linear state-space models A Tulsyan, B Huang, RB Gopaluni, JF Forbes Journal of Process Control 23 (4), 516-526, 2013 | 60 | 2013 |
| Particle filtering without tears: a primer for beginners A Tulsyan, RB Gopaluni, SR Khare Computers & Chemical Engineering 95 (12), 130-145, 2016 | 56 | 2016 |
| Advances in industrial biopharmaceutical batch process monitoring: Machine‐learning methods for small data problems A Tulsyan, C Garvin, C Undey Biotechnology and Bioengineering 115 (8), 1-10, 2018 | 53 | 2018 |
| A machine learning approach to calibrate generic Raman models for real-time monitoring of cell culture processes A Tulsyan, G Schorner, H Khodabandehlou, ... Biotechnology and Bioengineering 116 (10), 2575-2586, 2019 | 47 | 2019 |
| Industrial batch process monitoring with limited data A Tulsyan, C Garvin, C Undey Journal of Process Control 77 (5), 114-133, 2019 | 44 | 2019 |
| Automatic real‐time calibration, assessment, and maintenance of generic Raman models for online monitoring of cell culture processes A Tulsyan, T Wang, G Schorner, H Khodabandehlou, M Coufal, C Undey Biotechnology and Bioengineering 117 (2), 406-416, 2020 | 43 | 2020 |
| A particle filter approach to approximate posterior Cramér-Rao lower bound: The case of hidden states A Tulsyan, B Huang, RB Gopaluni, JF Forbes IEEE Transactions on Aerospace and Electronic Systems 49 (4), 2478-2495, 2013 | 34 | 2013 |
| Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Survey RB Gopaluni, A Tulsyan, B Chachuat, B Huang, JM Lee, F Amjad, ... IFAC World Congress, 2020 | 29 | 2020 |
| Deep reinforcement learning for process control: A primer for beginners S Spielberg, A Tulsyan, NP Lawrence, PD Loewen, RB Gopaluni arXiv preprint arXiv:2004.05490, 2020 | 29 | 2020 |
| Performance assessment, diagnosis, and optimal selection of non-linear state filters A Tulsyan, B Huang, RB Gopaluni, JF Forbes Journal of Process Control 24 (2), 460-478, 2014 | 29 | 2014 |
| Design and Assessment of Delay Timer Alarm Systems for Nonlinear Chemical Processes A Tulsyan, F Alrowaie, RB Gopaluni AIChE Journal 64 (1), 77-90, 2018 | 28 | 2018 |
| Univariate model-based deadband alarm design for nonlinear processes A Tulsyan, B Gopaluni Industrial & Engineering Chemistry Research 58 (26), 11295-11302, 2019 | 23 | 2019 |
| Spectroscopic models for real-time monitoring of cell culture processes using spatiotemporal just-in-time Gaussian processes A Tulsyan, H Khodabandehlou, T Wang, G Schorner, ... AIChE Journal 67 (5), 2021 | 16 | 2021 |
| Estimation and identification in batch processes with particle filters Z Zhao, A Tulsyan, B Huang, F Liu Journal of Process Control 81 (9), 1-14, 2019 | 16 | 2019 |
| Reachability-based fault detection method for uncertain chemical flow reactors A Tulsyan, PI Barton IFAC-PapersOnLine 49 (7), 1-6, 2016 | 16 | 2016 |
| Product Attribute Forecast: Adaptive Model Selection Using Real-Time Machine Learning ES Bayrak, T Wang, A Tulsyan, M Coufal, C Undey IFAC-PapersOnLine 51 (18), 121-125, 2018 | 15 | 2018 |
Cenk ÜndeyVice President, PTD Data & Digital, Roche/GenentechVerified email at gene.com
