L
Lu Lu
Researcher at Massachusetts Institute of Technology
Publications - 56
Citations - 5802
Lu Lu is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Artificial neural network & Deep learning. The author has an hindex of 19, co-authored 49 publications receiving 1640 citations. Previous affiliations of Lu Lu include University of Pennsylvania & Brown University.
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How the spleen reshapes and retains young and old red blood cells: A computational investigation.
TL;DR: In this article, the passage of RBCs through interendothelial slits (IES) in the spleen at different stages of their lifespan is simulated and the role of spleen in facilitating the maturation of reticulocytes and in clearing the senescent cells is investigated.
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Public good exploitation in natural bacterioplankton communities
TL;DR: In this paper, the authors developed a genomic approach to identify bacteria that can exploit public goods produced during the degradation of polysaccharides, and found that public good exploiters are active in natural chitin degrading microbial communities.
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Quantitative prediction of erythrocyte sickling for the development of advanced sickle cell therapies.
TL;DR: A kinetic model based on the classical nucleation theory was developed to examine the effectiveness of potential anti-sickling drug candidates and quantify the efficacy of sickling inhibitors and obtain results consistent with recent screening assays.
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Synergistic Integration of Laboratory and Numerical Approaches in Studies of the Biomechanics of Diseased Red Blood Cells
TL;DR: In this paper, a review of laboratory and computational studies of disordered red blood cells is presented, emphasizing how experimental techniques and computational modeling can be synergically integrated to improve the understanding of the pathophysiology of hematological disorders.
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Approximation rates of DeepONets for learning operators arising from advection-diffusion equations
TL;DR: In this article , the authors consider the problem of learning solution operators from both linear and nonlinear advection-diffusion equations with or without reaction and find that the approximation rates depend on the architecture of branch networks as well as the smoothness of inputs and outputs of solution operators.