Mudassir M. Rashid

Education

Ph.D. in Chemical Engineering from McMaster University, Hamilton, Canada, 2016
B.Eng. in Chemical Engineering from McMaster University, Hamilton, Canada, 2011

Research Interests

Mudassir Rashid’s research focuses on the following areas: 1) data-driven modeling and control algorithms for biological and chemical systems; 2) mathematical modelling and control in diabetes technology; and 3) process analytical technology methods for on-line modeling, monitoring, control, and optimization of pharmaceutical manufacturing processes.

Data-driven Modeling and Control Algorithms

• Recursive identification and adaptive modeling of nonlinear and time-varying systems
• Adaptive control of nonlinear systems with variable time delays

Mathematical Modelling and Control in Diabetes Technology

• Modeling and simulation of metabolic and physiologic processes and pathways
• Optimal drug delivery techniques and automated closed-loop insulin dosing control systems

Pharmaceutical Manufacturing Processes

• Modeling and simulation of pharmaceutical manufacturing processes
• Process analytical technology methods for multivariate prediction and monitoring of quality attributes and predictive control

Publications

1. Hajizadeh, I., Rashid, M.M., Samadi, S., Sevil, M., Hobbs, N., Brandt, R. and Cinar, A. (2019). Adaptive personalized multivariable artificial pancreas using plasma insulin estimates. J. Process Contr., 80, 26-40.
2. Hajizadeh, I., Samadi, S., Sevil, M., Rashid, M.M. and Cinar, A. (2019). Performance assessment and modification of an adaptive MPC for automated insulin delivery by a multivariable artificial pancreas. Ind. Eng. Chem. Res., accepted, in press.
3. Hajizadeh, I., Rashid, M.M. and Cinar, A. (2019). Plasma-insulin-cognizant adaptive model predictive control for artificial pancreas systems. J. Process Contr., 77, 97-113.
4. Hobbs, N., Hajizadeh, I., Rashid, M.M., Turksoy, K., Breton, M. and Cinar, A. (2018). Improving glucose prediction accuracy in physically active adolescents with type 1 diabetes. J. Diabetes Sci. Technol., accepted, in press.
5. Feng, J., Hajizadeh, I., Yu, X., Rashid, M.M., Samadi, S., Sevil, M., Hobbs, N., Brandt, R., Lazaro, C., Maloney, Z., Littlejohn, E., Quinn, L. and Cinar, A. (2018). Multi-model sensor fault detection and data reconciliation: A case study with glucose concentration sensors for diabetes. AIChE J., 65, 629-639.
6. Rashid, M.M., Patel, N., Mhaskar, P. and Swartz, C.L.E. (2018). Handling sensor faults in economic model predictive control of batch processes. AIChE J., 65, 617-628.
7. Hajizadeh, I., Rashid, M.M., Turksoy, K., Samadi, S., Feng, J., Sevil, M., Hobbs, N., Lazaro, C., Maloney, Z., Littlejohn, E. and Cinar, A. (2018). Incorporating unannounced meals and exercise in adaptive learning of personalized models for multivariable artificial pancreas systems. J. Diabetes Sci. Technol., 12, 953-966.
8. Yu, X., Rashid, M.M., Feng, J., Hobbs, N., Hajizadeh, I., Samadi, S., Sevil, M., Lazaro, C., Maloney, Z., Quinn, L., Littlejohn, E. and Cinar, A. (2018). Online glucose prediction using computationally efficient sparse kernel filtering algorithms in type 1 diabetes. IEEE Trans. Control Syst. Technol., accepted, in press.
9. Hajizadeh, I., Rashid, M.M., Samadi, S., Feng, J., Sevil, M., Hobbs, N., Lazaro, C., Maloney, Z., Brandt, R., Yu, X., Turksoy, K., Littlejohn, E., Cengiz, E. and Cinar, A. (2018). Adaptive and personalized plasma insulin concentration estimation for artificial pancreas systems. J. Diabetes Sci. Technol., 12, 639-649.
10. Samadi, S., Rashid, M.M., Turksoy, K., Feng, J., Hajizadeh, I., Hobbs, N., Lazaro, C., Sevil, M., Littlejohn, E. and Cinar, A. (2018). Automatic detection and estimation of unannounced meals for multivariable artificial pancreas system. Diabetes Technol. Ther., 20, 235-246.
11. Feng, J., Hajizadeh, I., Yu, X., Rashid, M.M., Turksoy, K., Samadi, S., Sevil, M., Hobbs, N., Brandt, R., Lazaro, C., Maloney, Z., Littlejohn, E., Philipson, L.H. and Cinar, A. (2018). Multi-level supervision and modification of artificial pancreas control system. Comput. Chem. Eng., 112, 57-69.
12. Yu, X., Turksoy, K., Rashid, M.M., Feng, J., Hobbs, N., Hajizadeh, I., Samadi, S., Sevil, M., Lazaro, C., Maloney, Z., Littlejohn, E., Quinn, L. and Cinar, A. (2018). Model-fusion-based online glucose concentration predictions in people with type 1 diabetes. Control Eng. Pract. 71, 129-141.
13. Hajizadeh, I., Rashid, M.M., Turksoy, K., Samadi, S., Feng, J., Frantz, N., Sevil, M., Cengiz, E. and Cinar, A. (2017). Plasma insulin estimation in people with type 1 diabetes mellitus. Ind. Eng. Chem. Res. 56, 9846-9857.
14. Rashid, M.M., Mhaskar, P. and Swartz, C.L.E. (2017). Handling multi-rate and missing data in variable duration economic model predictive control of batch processes. AIChE J. 63, 2705-2718.
15. Rashid, M.M., Mhaskar, P. and Swartz, C.L.E. (2016). Multi-rate modeling and economic model predictive control of the electric arc furnace. J. Process Control. 40, 50-61.
16. Yu, J., Chen, K., Mori, J. and Rashid, M.M. (2013). A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction. Energy. 61, 673-686.
17. Chen, J., Yu, J., Mori, J., Rashid, M.M., Hu, G., Yu, H., Flores-Cerrillo, J. and Megan, L. (2013). A non- Gaussian pattern matching based dynamic process monitoring approach and its application to cryogenic air separation process. Computer Chem. Eng. 58, 40-53.
18. Yu, J., Chen, K. and Rashid, M.M. (2013). A Bayesian model averaging based multi-kernel Gaussian process regression framework for nonlinear state estimation and quality prediction of multiphase batch processes with transient dynamics and uncertainty. Chem. Eng. Sci. 93, 96-109.
19. Yu, J., Chen, J. and Rashid, M.M. (2013). Multiway independent component analysis mixture model and mutual information based fault detection and diagnosis approach of multiphase batch processes. AIChE J. 59(8), 2761-2779.
20. Yu, J. and Rashid, M.M. (2013). A novel dynamic Bayesian network-based networked process monitoring approach for fault detection, propagation identification, and root cause diagnosis. AIChE J. 59(7), 2348- 2365.
21. Rashid, M.M. and Yu, J. (2012). Nonlinear and non-Gaussian dynamic batch process monitoring using a new multiway kernel independent component analysis and multidimensional mutual information based dissimilarity approach. Ind. Eng. Chem. Res. 51(33), 10910-10920.
22. Rashid, M.M. and Yu, J. (2012). A new dissimilarity method integrating multidimensional mutual information and independent component analysis for non-Gaussian dynamic process monitoring. Chemometrics Intell. Lab. Syst. 115, 44-58.
23. Rashid, M.M. and Yu, J. (2012). Hidden Markov model based adaptive independent component analysis for chemical process monitoring. Ind. Eng. Chem. Res. 51, 5506-5514.