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Amir H. Alavi
Researcher at University of Pittsburgh
Publications - 219
Citations - 15536
Amir H. Alavi is an academic researcher from University of Pittsburgh. The author has contributed to research in topics: Genetic programming & Metaheuristic. The author has an hindex of 49, co-authored 202 publications receiving 11559 citations. Previous affiliations of Amir H. Alavi include Harvard University & Shahid Beheshti University.
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Small and large deformation models of post-buckled beams under lateral constraints:
TL;DR: In this paper, the buckling behavior of bilaterally constrained beams with respect to different geometric parameters and conditions was theoretically and experimentally investigated, and the results showed that the buckled behavior of these beams can be characterized.
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Smartphone-based molecular sensing for advanced characterization of asphalt concrete materials
TL;DR: In this article, a pocket-sized near-infrared (NIR) molecular sensor that is fully integrated with smartphones is used to detect and classify asphalt mixtures fabricated with various binder components such as asphaltenes, resins, and aromatic fractions.
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New design equations for assessment of load carrying capacity of castellated steel beams: a machine learning approach
TL;DR: New design equations were developed to predict the load carrying capacity of CSB using linear genetic programming (LGP), and an integrated search algorithm of genetic programming and simulated annealing, called GSA, to formulated in terms of the geometrical and mechanical properties of the castellated beams.
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A data mining approach to compressive strength of CFRP-confined concrete cylinders
TL;DR: In this paper, the compressive strength of carbon fiber reinforced polymer (CFRP) confined concrete cylinders is formulated using a hybrid method coupling genetic programming (GP) and simulated annealing (SA), called GP/SA, and a robust variant of GP, namely multi expression programming (MEP).
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Supervised Adversarial Alignment of Single-Cell RNA-seq Data
TL;DR: By overcoming batch effects this method was able to correctly separate cell types, improving on several prior methods suggested for this task and analysis of the top features used by the network indicates that by taking the batch impact into account, the reduced representation is much better able to focus on key genes for each cell type.