Seed Grant Report: Mathematical Modeling and Algorithms for Speeding up the Process of New Materials Development and Engineering
This CISE Seed Grant funded doctoral student Athar Roshandelpoor in the SE Department to work under the supervision of CISE Faculty Affiliate Pirooz Vakili (ME/SE), Emily Ryan (ME/MSE) and Keith Brown (ME/MSE/Physics) in developing a keen understanding of a range of computational and experimental approaches for new materials development and engineering in order to: (i) Investigate how these problems can be formulated mathematically as learning and optimization problems, and (ii) develop effective algorithms for optimal learning and optimization to speed up the process.
The funding supported the following activities:
Extramural funding received: Awarded NIH grant ($561,312.00 for 3 years), entitled “Optimization and Learning Strategies for Protein Docking”
- RA and advisor focused on item (i) of the CISE project in the context of the NSF project. The NSF project involves a collaborative project of computational and experimental material scientists as well as the RA and advisor for new material development and engineering and it provided a particularly well suited domain for investigating item (i).
- RA and advisor have performed an extensive literature review of classical methods of design of experiments, optimal design, and application of modern machine learning methods to design of new materials. A paper based on the results of this review is planned to be submitted by the end of summer 2019. CISE award will be acknowledged in this paper.
- RA and advisor have begun investigating part (ii) of the CISE proposal.