This dissertation focused on deep learning methods for large-scale dish classification and nutrient estimation through ingredient-guided RGB-D imaging supported by vision-text contrastive learning and Swift UI iOS application development.

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Abstract: In this talk, we introduce a general stochastic maximum principle for systems of partially observed optimal control of semi-linear stochastic partial differential equations in a nonconvex control domain. The state evolves in a Hilbert space driven by a cylindrical Wiener process and finitely many Brownian motions, while observations are in a Euclidean space having correlated noise. For the convex control domain and diffusion coefficients in the state being control-independent, numerical algorithms are developed to solve the partially observed optimal control problems using a stochastic gradient descent algorithm combined with finite element approximations and the branching filtering algorithm. Numerical experiments are conducted for demonstration. Speaker's short bio: Hongjiang is currently a postdoc in the Department of Mathematics at Auburn University. He completed his Ph.D. in mathematics at the University of Connecticut under the supervision of Prof. George Yin, and received B.S. in Mathematics and Applied Mathematics from Huazhong University of Science and Technology in 2018. Please visit his website https://hongjiang-qian.github.io/ (https://hongjiang-qian.github.io/) for more information.

TBA Speaker's short bio: Hongjiang is a postdoctoral fellow in the Department of Mathematics and Statistics at Auburn University. He obtained his PhD in mathematics at the University of Connecticut in December 2023, under the supervision of Prof. George Yin. Before that, he received his B.S. in Mathematics and Applied Mathematics at Huazhong University of Science and Technology in 2018. Please visit his website https://hongjiang-qian.github.io/index.html (https://hongjiang-qian.github.io/index.html) for more information.