Profile
Maaz AHMAD
PhD Student
Maaz Ahmad's PhD research has focused on advancing the area of data-driven surrogate modeling and optimsation to aid industrial digitalisation. He has worked on developing a meta-learning-based surrogate selection framework to automate the process of selecting the best surrogate model for any given data set. The studies have also included a more fundamental analysis of different modeling techniques to compare and group similar performing surrogates into distinct families. He has also developed a surrogate-based optimisation framework based on a clustering algorithm, that does not require any derivative information. Hence, this derivative-free optimisation algorithm could potentially be used for optimising black-box systems with unknown form of the objectives. While his focus has been on relatively simpler problems involving bound-constraints and continuous decision variables, Maaz aims to extend the algorithm for more complex optimisation problems involving linear/non-linear constraints, and integer variables.
As part of his future works, Maaz wishes to explore improved modeling methods, possibly incorporating information of the system physics.
e0383058@u.nus.edu
Past Members
Past Members
Research Interest
Key Publications
Ahmad, M., Karimi, I.A., 2021. Revised learning based evolutionary assistive paradigm for surrogate selection (LEAPS2v2). Computers & Chemical Engineering 152, 107385
Ahmad, M., Karimi, I.A., 2022. Families of similar surrogate forms based on predictive accuracy and model complexity. Computers & Chemical Engineering 163, 107845.
Conferences Attended & Corresponding Papers:
2020 Virtual AIChE Annual Meeting, November 16 – November 20, 2020. Oral Presentation: “Upgraded LEAPS2 for Surrogate Recommendation”
European Symposium on Computer Aided Process Engineering 2021, Istanbul, Turkey, June 6 – June 9, 2021
Conference Paper: Ahmad, M., Karimi, I.A., 2021. Upgraded Meta-Learning based Surrogate Selection Paradigm (LEAPS2v2), in: Türkay, M., Gani, R. (Eds.), Computer Aided Chemical Engineering. Elsevier, pp. 1099–1104.
2021 Virtual AIChE Annual Meeting, November 7 – November 19, 2021. Oral Presentation: “Families of Data-driven Surrogate Forms based on Accuracy and Complexity”
PSE 2021+, Kyoto, Japan, June 19 – June 23, 2022.
Conference Paper: M. Ahmad and I. A. Karimi, “Surrogate Classification based on Accuracy and Complexity,” in Computer Aided Chemical Engineering, Elsevier, 2022, pp. 1735–1740. doi: 10.1016/B978-0-323-85159-6.50289-X.
2022 AIChE Annual Meeting, November 13 – November 18, 2022. Oral Presentation: “Surrogate-based Optimization for Box-Constrained Black-Box Systems via k-means Clustering”