Approximation Algorithms for Allocation and Network Design

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In this thesis we present and analyze approximation algorithms for three different clustering problems. The formulations of these problems are motivated by fairness and explainability considerations, two issues that have recently received attention in the ...

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Approximation Algorithms for Allocation and Network Design

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Fairness and Explainability in Clustering Problems

Bayesian reconstruction of memories stored in neural networks from their connectivity

Structural impact of the start-up sequence on Pelton turbines lifetime: Analytical prediction and polynomial optimization

Quality-Constrained Encoding Optimization for Omnidirectional Video Streaming

A Spatial Branch and Bound Algorithm for Continuous Pricing with Advanced Discrete Choice Demand Modeling

Robust Collaborative Learning with Linear Gradient Overhead

Time vs. Truth: Age-Distortion Tradeoffs and Strategies for Distributed Inference

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Fast Parallel Algorithms for Enumeration of Simple, Temporal, and Hop-Constrained Cycles

Adaptive Symplectic Model Order Reduction Of Parametric Particle-Based Vlasov-Poisson Equation

Extractable Witness Encryption for the Homogeneous Linear Equations Problem

Worst-Case Delay Analysis of Time-Sensitive Networks with Network Calculus