We develop a control-theoretic framework for solving constrained resource allocation problems, formulated as generalized facility location with optimal placement and assignment decisions. Leveraging Control Barrier Functions (CBFs), Control Lyapunov Functions (CLFs), and the Maximum Entropy Principle (MEP), our approach ensures feasibility while improving convergence and constraint handling.
Instructor: Svetlana Lazebnik
Presented and discussed “Deep Sets” by Zaheer et al., a foundational work proposing permutation-invariant neural architectures for set-structured data. The presentation covered theoretical guarantees for invariant and equivariant functions and demonstrated applications in set-based learning.
Presented a poster based on our ACC 2023 paper, “Towards Efficient Modularity in Industrial Drying: A Combinatorial Optimization Viewpoint”. This work addresses the optimal sequencing and operating conditions of multiple drying technologies with distinct mechanisms and constraints, using combinatorial scheduling to improve energy efficiency and modular process design.
Instructor: Nan Jiang
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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