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ORCIDDaniel E. Rivera
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- affiliation: Arizona State University, Control Systems Engineering Laboratory, Tempe, AZ, USA
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2020 – today
- 2024
[j25]Steven De La Torre, Mohamed El Mistiri, Eric B. Hekler, Predrag Klasnja, Benjamin M. Marlin, Misha Pavel, Donna Spruijt-Metz, Daniel E. Rivera:
Modeling engagement with a digital behavior change intervention (HeartSteps II): An exploratory system identification approach. J. Biomed. Informatics 158: 104721 (2024)- 2023
- [c52]Rachael T. Kha
, Daniel E. Rivera, Predrag V. Klasnja, Eric B. Hekler:
Idiographic Dynamic Modeling for Behavioral Interventions with Mixed Data Partitioning and Discrete Simultaneous Perturbation Stochastic Approximation. ACC 2023: 283-288 - [c51]Mohamed El Mistiri, Owais Khan, Daniel E. Rivera, Eric B. Hekler:
System Identification and Hybrid Model Predictive Control in Personalized mHealth Interventions for Physical Activity. ACC 2023: 2240-2245 - 2022
- [j24]Penghong Guo, Daniel E. Rivera, Yuwen Dong, Sunil Deshpande, Jennifer S. Savage, Emily E. Hohman, Abigail M. Pauley, Krista S. Leonard, Danielle Symons Downs:
Optimizing behavioral interventions to regulate gestational weight gain with sequential decision policies using hybrid model predictive control. Comput. Chem. Eng. 160: 107721 (2022) - [c50]Mohamed El Mistiri, Daniel E. Rivera, Predrag V. Klasnja, Junghwan Park, Eric B. Hekler:
Enhanced Social Cognitive Theory Dynamic Modeling and Simulation Towards Improving the Estimation of "Just-In-Time" States. ACC 2022: 468-473 - [c49]Rachael T. Kha, Daniel E. Rivera, Predrag V. Klasnja, Eric B. Hekler:
Model Personalization in Behavioral Interventions using Model-on-Demand Estimation and Discrete Simultaneous Perturbation Stochastic Approximation. ACC 2022: 671-676 - [c48]Mohamed El Mistiri, Daniel E. Rivera, Predrag V. Klasnja, Junghwan Park, Eric B. Hekler:
Model Predictive Control Strategies for Optimized mHealth Interventions for Physical Activity. ACC 2022: 1392-1397 - [c47]Owais Khan, Mohamed El Mistiri, Daniel E. Rivera, Cesar A. Martin, Eric B. Hekler:
A Kalman filter-based Hybrid Model Predictive Control Algorithm for Mixed Logical Dynamical Systems: Application to Optimized Interventions for Physical Activity. CDC 2022: 2586-2593 - [c46]Karine Tung, Steven De La Torre, Mohamed El Mistiri, Rebecca Braga De Braganca, Eric B. Hekler, Misha Pavel, Daniel E. Rivera, Pedja Klasnja, Donna Spruijt-Metz, Benjamin M. Marlin:
BayesLDM: A Domain-specific Modeling Language for Probabilistic Modeling of Longitudinal Data. CHASE 2022: 78-90 - [i1]Karine Tung, Steven De La Torre, Mohamed El Mistiri, Rebecca Braga De Braganca, Eric B. Hekler, Misha Pavel, Daniel E. Rivera, Pedja Klasnja, Donna Spruijt-Metz, Benjamin M. Marlin:
BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data. CoRR abs/2209.05581 (2022) - 2020
- [j23]Guillaume Mercère, Alexander Medvedev, Daniel E. Rivera, Caterina M. Scoglio, Bayu Jayawardhana:
Foreword Identification and Control in Biomedical Applications. IEEE Trans. Control. Syst. Technol. 28(1): 1-2 (2020) - [j22]Penghong Guo, Daniel E. Rivera, Jennifer S. Savage, Emily E. Hohman, Abigail M. Pauley, Krista S. Leonard, Danielle S. Downs:
System Identification Approaches for Energy Intake Estimation: Enhancing Interventions for Managing Gestational Weight Gain. IEEE Trans. Control. Syst. Technol. 28(1): 63-78 (2020) - [j21]Paulo J. Lopes dos Santos, Mohammad T. Freigoun, César A. Martín, Daniel E. Rivera, Eric B. Hekler, Rodrigo Alvite Romano, Teresa Paula Azevedo Perdicoúlis:
System Identification of Just Walk: Using Matchable-Observable Linear Parametrizations. IEEE Trans. Control. Syst. Technol. 28(1): 264-275 (2020) - [j20]César A. Martín, Daniel E. Rivera, Eric B. Hekler, William T. Riley, Matthew P. Buman, Marc A. Adams, Alicia B. Magann:
Development of a Control-Oriented Model of Social Cognitive Theory for Optimized mHealth Behavioral Interventions. IEEE Trans. Control. Syst. Technol. 28(2): 331-346 (2020)
2010 – 2019
- 2018
- [j19]Sayali S. Phatak, Mohammad T. Freigoun, César A. Martín, Daniel E. Rivera, Elizabeth V. Korinek, Marc A. Adams, Matthew P. Buman, Predrag V. Klasnja, Eric B. Hekler:
Modeling individual differences: A case study of the application of system identification for personalizing a physical activity intervention. J. Biomed. Informatics 79: 82-97 (2018) - 2017
- [c45]Mohammad T. Freigoun, Cesar A. Martin, Alicia B. Magann, Daniel E. Rivera, Sayali S. Phatak, Elizabeth V. Korinek, Eric B. Hekler:
System identification of Just Walk: A behavioral mHealth intervention for promoting physical activity. ACC 2017: 116-121 - [c44]P. Lopes dos Santos, R. Romano, T.-P. Azevedo-Perdicoúlis, Daniel E. Rivera, José A. Ramos:
LPV system identification using the matchable observable linear identification approach. CDC 2017: 4626-4631 - [p2]Wendy Nilsen, Emre Ertin, Eric B. Hekler, Santosh Kumar, Insup Lee, Rahul Mangharam, Misha Pavel, James M. Rehg, William T. Riley, Daniel E. Rivera, Donna Spruijt-Metz:
Modeling Opportunities in mHealth Cyber-Physical Systems. Mobile Health - Sensors, Analytic Methods, and Applications 2017: 443-453 - [p1]Daniel E. Rivera, César A. Martín, Kevin P. Timms, Sunil Deshpande, Naresh N. Nandola, Eric B. Hekler:
Control Systems Engineering for Optimizing Behavioral mHealth Interventions. Mobile Health - Sensors, Analytic Methods, and Applications 2017: 455-493 - 2016
- [c43]Penghong Guo, Daniel E. Rivera, Danielle S. Downs, Jennifer S. Savage:
Semi-physical identification and state estimation of energy intake for interventions to manage gestational weight gain. ACC 2016: 1271-1276 - [c42]Cesar A. Martin, Daniel E. Rivera, Eric B. Hekler:
A decision framework for an adaptive behavioral intervention for physical activity using hybrid model predictive control. ACC 2016: 3576-3581 - [c41]Cesar A. Martin, Daniel E. Rivera, Eric B. Hekler:
An enhanced identification test monitoring procedure for MIMO systems relying on uncertainty estimates. CDC 2016: 2091-2096 - [c40]Tylar Murray, Eric B. Hekler, Donna Spruijt-Metz, Daniel E. Rivera, Andrew Raij:
Formalization of Computational Human Behavior Models for Contextual Persuasive Technology. PERSUASIVE 2016: 150-161 - 2015
- [c39]Cesar A. Martin, Sunil Deshpande, Eric B. Hekler, Daniel E. Rivera:
A system identification approach for improving behavioral interventions based on Social Cognitive Theory. ACC 2015: 5878-5883 - [c38]Cesar A. Martin, Daniel E. Rivera, Eric B. Hekler:
An identification test monitoring procedure for MIMO systems based on statistical uncertainty estimation. CDC 2015: 2719-2724 - 2014
- [j18]Jay D. Schwartz, Daniel E. Rivera:
A control-relevant approach to demand modeling for supply chain management. Comput. Chem. Eng. 70: 78-90 (2014) - [j17]Kevin P. Timms, Daniel E. Rivera, Linda M. Collins, Megan E. Piper:
Continuous-time system identification of a smoking cessation intervention. Int. J. Control 87(7): 1423-1437 (2014) - [j16]Diego Regruto, Fabrizio Dabbene, Daniel E. Rivera:
Guest Editorial: Special Issue on Relaxation Methods in Identification and Estimation Problems. IEEE Trans. Autom. Control. 59(11): 2869-2870 (2014) - [j15]Sunil Deshpande, Daniel E. Rivera:
Constrained Optimal Input Signal Design for Data-Centric Estimation Methods. IEEE Trans. Autom. Control. 59(11): 2990-2995 (2014) - [c37]Kevin P. Timms, Daniel E. Rivera, Megan E. Piper, Linda M. Collins:
A Hybrid Model Predictive Control strategy for optimizing a smoking cessation intervention. ACC 2014: 2389-2394 - [c36]Cesar A. Martin, Daniel E. Rivera, William T. Riley, Eric B. Hekler, Matthew P. Buman, Marc A. Adams, Abby C. King:
A dynamical systems model of Social Cognitive Theory. ACC 2014: 2407-2412 - [c35]Yuwen Dong, Sunil Deshpande, Daniel E. Rivera, Danielle S. Downs, Jennifer S. Savage:
Hybrid model predictive control for sequential decision policies in adaptive behavioral interventions. ACC 2014: 4198-4203 - [c34]Sunil Deshpande, Daniel E. Rivera:
Data-centric input signal design for highly interactive dynamical systems. CDC 2014: 1023-1028 - [c33]Sunil Deshpande, Daniel E. Rivera:
Towards data-centric input signal design using sparse polynomial optimization. CDC 2014: 1893-1898 - [c32]Paulo J. Lopes dos Santos, T.-P. Azevedo-Perdicoúlis, José A. Ramos, Sunil Deshpande, Daniel E. Rivera, Jorge Leite Martins de Carvalho:
LPV system identification using a separable least squares support vector machines approach. CDC 2014: 2548-2554 - [c31]William T. Riley, Cesar A. Martin, Daniel E. Rivera:
The importance of behavior theory in control system modeling of physical activity sensor data. EMBC 2014: 6880-6883 - [c30]Kevin P. Timms, Cesar A. Martin, Daniel E. Rivera, Eric B. Hekler, William T. Riley:
Leveraging intensive longitudinal data to better understand health behaviors. EMBC 2014: 6888-6891 - 2013
- [j14]Naresh N. Nandola, Daniel E. Rivera:
An Improved Formulation of Hybrid Model Predictive Control With Application to Production-Inventory Systems. IEEE Trans. Control. Syst. Technol. 21(1): 121-135 (2013) - [c29]Kevin P. Timms, Daniel E. Rivera, Linda M. Collins, Megan E. Piper:
Control systems engineering for understanding and optimizing smoking cessation interventions. ACC 2013: 1964-1969 - [c28]Yuwen Dong, Daniel E. Rivera, Danielle S. Downs, Jennifer S. Savage, Diana M. Thomas, Linda M. Collins:
Hybrid model predictive control for optimizing gestational weight gain behavioral interventions. ACC 2013: 1970-1975 - [c27]P. Lopes dos Santos, Sunil Deshpande, Daniel E. Rivera, T.-P. Azevedo-Perdicoúlis, José A. Ramos, Jarred Younger:
Identification of affine linear parameter varying models for adaptive interventions in fibromyalgia treatment. ACC 2013: 1976-1981 - [c26]Sunil Deshpande, Daniel E. Rivera:
Optimal input signal design for data-centric estimation methods. ACC 2013: 3924-3929 - [c25]Sunil Deshpande, Daniel E. Rivera:
A data-centric system identification approach to input signal design for Hammerstein systems. CDC 2013: 5192-5197 - 2012
- [j13]José Luis Guzmán, Daniel E. Rivera, Sebastián Dormido, Manuel Berenguel:
An interactive software tool for system identification. Adv. Eng. Softw. 45(1): 115-123 (2012) - [j12]Kevin T. Roche, Daniel E. Rivera, Jeffery K. Cochran:
A control engineering framework for managing whole hospital occupancy. Math. Comput. Model. 55(3-4): 1401-1417 (2012) - [c24]Yuwen Dong, Daniel E. Rivera, Diana M. Thomas, Jesús Emeterio Navarro-Barrientos, Danielle S. Downs, Jennifer S. Savage, Linda M. Collins:
A dynamical systems model for improving gestational weight gain behavioral interventions. ACC 2012: 4059-4064 - 2011
- [j11]Ascensión Zafra-Cabeza, Daniel E. Rivera, Linda M. Collins, Miguel A. Ridao, Eduardo F. Camacho:
A Risk-Based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health. IEEE Trans. Control. Syst. Technol. 19(4): 891-901 (2011) - [j10]Richard Steenis, Daniel E. Rivera:
Plant-Friendly Signal Generation for System Identification Using a Modified Simultaneous Perturbation Stochastic Approximation (SPSA) Methodology. IEEE Trans. Control. Syst. Technol. 19(6): 1604-1612 (2011) - [c23]Sunil Deshpande, Naresh N. Nandola, Daniel E. Rivera, Jarred Younger:
A control engineering approach for designing an optimized treatment plan for fibromyalgia. ACC 2011: 4798-4803 - 2010
- [c22]Richard Steenis, Daniel E. Rivera:
Probabilistic uncertainty description for an ETFE estimated plant using a sequence of multi-sinusoidal signals. ACC 2010: 3722-3728 - [c21]Naresh N. Nandola, Daniel E. Rivera:
A novel model predictive control formulation for hybrid systems with application to adaptive behavioral interventions. ACC 2010: 6286-6292 - [c20]Naresh N. Nandola, Daniel E. Rivera:
Model-on-Demand predictive control for nonlinear hybrid systems with application to adaptive behavioral interventions. CDC 2010: 6113-6118 - [c19]Jesús Emeterio Navarro-Barrientos, Daniel E. Rivera, Linda M. Collins:
A Dynamical Systems Model for Understanding Behavioral Interventions for Weight Loss. SBP 2010: 170-179
2000 – 2009
- 2009
- [j9]Wenlin Wang, Daniel E. Rivera, Hans D. Mittelmann:
Inner and outer loop optimization in semiconductor manufacturing supply chain management. Comput. Manag. Sci. 6(4): 411 (2009) - [c18]Jay D. Schwartz, Daniel E. Rivera:
Control-relevant estimation of demand models for closed-loop control of a production-inventory system. CDC 2009: 416-421 - [c17]Richard Steenis, Daniel E. Rivera:
Plant-friendly signal generation for system identification using a modified SPSA methodology. CDC 2009: 470-475 - [c16]Cristina Stoica, Manuel R. Arahal, Daniel E. Rivera, Pedro Rodríguez-Ayerbe, Didier Dumur:
Application of robustified Model Predictive Control to a production-inventory system. CDC 2009: 3993-3998 - 2008
- [j8]Wenlin Wang, Daniel E. Rivera:
Model Predictive Control for Tactical Decision-Making in Semiconductor Manufacturing Supply Chain Management. IEEE Trans. Control. Syst. Technol. 16(5): 841-855 (2008) - [c15]Jay D. Schwartz, Manuel R. Arahal, Daniel E. Rivera:
Control-relevant demand forecasting for management of a production-inventory system. ACC 2008: 4053-4058 - 2007
- [j7]Hans D. Mittelmann, Gautam V. Pendse, Daniel E. Rivera, Hyunjin Lee:
Optimization-based design of plant-friendly multisine signals using geometric discrepancy criteria. Comput. Optim. Appl. 38(1): 173-190 (2007) - [c14]Hyunjin Lee, Daniel E. Rivera, Hans D. Mittelmann, Gautam V. Pendse:
Optimization-based Design of Plant-Friendly Input Signals for Model-on-Demand Estimation and Model Predictive Control. ACC 2007: 1560-1565 - [c13]Jay D. Schwartz, Daniel E. Rivera:
Multi-Objective Control-Relevant Demand Modeling for Production and Inventory Control. CASE 2007: 710-715 - 2006
- [j6]Jay D. Schwartz, Wenlin Wang, Daniel E. Rivera:
Simulation-based optimization of process control policies for inventory management in supply chains. Autom. 42(8): 1311-1320 (2006) - [c12]Hyunjin Lee, Daniel E. Rivera:
An integrated input signal design and control-relevant parameter estimation approach for highly interactive multivariate systems. ACC 2006: 1-6 - [c11]Jay D. Schwartz, Daniel E. Rivera:
Simulation-based optimal tuning of model predictive control policies for supply chain management using simultaneous perturbation stochastic approximation. ACC 2006 - [c10]Ascensión Zafra-Cabeza, Daniel E. Rivera, Linda M. Collins, Miguel A. Ridao, Eduardo F. Camacho:
A Risk-based Model Predictive Control Approach to Adaptive Interventions in Behavioral Health. CDC 2006: 673-678 - [c9]Dongping Huang, Hessam S. Sarjoughian, Gary W. Godding, Daniel E. Rivera, Karl G. Kempf:
Flexible experimentation and analysis for hybrid DEVS and MPC models. WSC 2006: 1863-1870 - 2005
- [c8]Jay D. Schwartz, Daniel E. Rivera, Karl G. Kempf:
Towards control-relevant forecasting in supply chain management. ACC 2005: 202-207 - [c7]Wenlin Wang, Daniel E. Rivera, Karl G. Kempf:
A novel model predictive control algorithm for supply chain management in semiconductor manufacturing. ACC 2005: 208-213vol.1 - [c6]Hyunjin Lee, Daniel E. Rivera:
Control-relevant curvefitting for plant-friendly multivariable system identification. ACC 2005: 1431-1436 - [c5]Daniel E. Rivera, Michael D. Pew:
Evaluating PID Control for Supply Chain Management: A Freshman Design Project. CDC/ECC 2005: 3415-3419 - [c4]Hessam S. Sarjoughian, Dongping Huang, Gary W. Godding, Karl G. Kempf, Wenlin Wang, Daniel E. Rivera, Hans D. Mittelmann:
Hybrid discrete event simulation with model predictive control for semiconductor supply-chain manufacturing. WSC 2005: 256-266 - 2004
- [c3]Wenlin Wang, Daniel E. Rivera, Karl G. Kempf, Kirk D. Smith:
A model predictive control strategy for supply chain management in semiconductor manufacturing under uncertainty. ACC 2004: 4577-4582 - 2003
- [j5]Martin W. Braun, Daniel E. Rivera, M. E. Flores, W. Matthew Carlyle, Karl G. Kempf:
A Model Predictive Control framework for robust management of multi-product, multi-echelon demand networks. Annu. Rev. Control. 27(2): 229-245 (2003) - [j4]Martin W. Braun, Daniel E. Rivera, W. Matthew Carlyle, Karl G. Kempf:
Application of Model Predictive Control to Robust Management of Multiechelon Demand Networks in Semiconductor Manufacturing. Simul. 79(3): 139-156 (2003) - [j3]Felipe D. Vargas-Villamil, Daniel E. Rivera, Karl G. Kempf:
A hierarchical approach to production control of reentrant semiconductor manufacturing lines. IEEE Trans. Control. Syst. Technol. 11(4): 578-587 (2003) - [c2]Wenlin Wang, Daniel E. Rivera, Karl G. Kempf:
Centralized model predictive control strategies for inventory management in semiconductor manufacturing supply chains. ACC 2003: 585-590 - 2001
- [j2]Felipe D. Vargas-Villamil, Daniel E. Rivera:
A model predictive control approach for real-time optimization of reentrant manufacturing lines. Comput. Ind. 45(1): 45-57 (2001) - [c1]Lei Yang, Daniel E. Rivera:
Integrated identification and model predictive control using iterative refinement. ACC 2001: 1190-1195
1990 – 1999
- 1990
- [j1]Daniel E. Rivera, Manfred Morari:
Low-order SISO controller tuning methods for the H2, H∞ and μ objective functions. Autom. 26(2): 361-369 (1990)
Coauthor Index
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