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Delft University of Technology

Academic institutioneurope · nl
Official website
Research library1,126linked papers
Opportunities0open roles
Selected work

Representative Papers

Coalitional model predictive control of an irrigation canal

Apr 01, 2014

Large-scale irrigation canal systems (e.g., the Dez Canal) face significant challenges in multi-gate coordinated regulation, including high communication overhead, stringent input constraints, slow state response, and weak disturbance rejection. To address these issues, this paper proposes a novel framework integrating coalition game theory with distributed model predictive control (MPC). It is the first work to embed a dynamic coalition formation mechanism into a distributed MPC architecture, enabling subsystems to autonomously form and dissolve coalitions based on real-time operating conditions—while respecting hard input constraints and balancing global optimization with local autonomy. Leveraging linear time-varying modeling, receding-horizon optimization, and explicit constraint handling, the method achieves coordinated water-level and flow control in representative canal system simulations: overshoot is reduced by 35%, settling time improves by 28%, and robustness and scalability are significantly enhanced.

101 citations2 influentialRead paper

One-Bit Compressed Sensing Using Generative Models

May 01, 2020IEEE International Conference on Acoustics, Speech, and Signal Processing

This paper addresses sparse signal reconstruction in one-bit compressive sensing by proposing the first reconstruction framework leveraging pre-trained generative models. Methodologically, it models the target signal as a low-dimensional latent variable on a learned generative manifold and directly optimizes this latent variable under one-bit measurement constraints, integrating gradient-based search with theoretically grounded regularization. Its key contribution lies in moving beyond conventional ℓ₁ sparsity priors: it exploits expressive generative priors to capture broader classes of structured signals and establishes, for the first time, a theoretical reconstruction error bound under a RIP-like condition on the measurement operator. Experiments on standard benchmarks demonstrate substantial improvements—3–8 dB higher PSNR—over state-of-the-art methods including ℓ₁ minimization and AQI, confirming the superior representational power and robustness of generative priors in one-bit reconstruction.

6 citationsRead paper

The Design Space of in-IDE Human-AI Experience

Oct 11, 2024arXiv.org

Current AI assistant features in IDEs exhibit a significant misalignment with developers’ authentic needs, necessitating a systematic understanding of heterogeneous user requirements. Method: We conducted semi-structured interviews with 35 practitioners—comprising AI adopters, attriters, and non-users—to empirically construct the first human-AI interaction design space for IDE-integrated AI assistants. Through thematic coding and cross-cohort comparative analysis, we identified fundamental divergences across user groups along five dimensions: reliability, privacy, personalization, proactivity, and ethical concerns. Contribution/Results: We propose a role-driven, five-dimensional design framework—encompassing technical robustness, interaction modality, goal alignment, skill abstraction, and cognitive offloading—alongside 12 actionable design guidelines. This work advances IDE AI tools toward greater reliability, contextual awareness, privacy-by-design, and seamless workflow integration.

4 citationsRead paper

Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection

Jul 26, 2024arXiv.org

Existing finite-width deep neural networks lack analytically tractable Gaussian process (GP) approximations with provable error bounds. Method: We propose the first Gaussian Process Mixture (GPM) approximation framework with certified error bounds: leveraging Wasserstein distance to model output distributions layer-wise, it achieves ε-accurate approximation of arbitrary non-i.i.d. parameterized networks over finite input sets. The method integrates optimal transport theory with hierarchical probabilistic modeling, yielding differentiable error bounds that guide network parameter optimization toward user-specified prior distributions. Results: Experiments demonstrate that GPM enables controllable-accuracy approximation on both regression and classification tasks, while simultaneously supporting principled uncertainty quantification and Bayesian prior design—bridging finite-width neural networks and rigorous GP inference with guaranteed approximation quality.

4 citationsRead paper

Limits of Kernelization and Parametrization for Phylogenetic Diversity with Dependencies

Feb 13, 2026

In the Maximize Phylogenetic Diversity problem, we are given a phylogenetic tree that represents the genetic proximity of species, and we are asked to select a subset of species of maximum phylogenetic diversity to be preserved through conservation efforts, subject to budgetary constraints that allow only k species to be saved. This neglects that it is futile to preserve a predatory species if we do not also preserve at least a subset of the prey it feeds on. Thus, in the Optimizing PD with Dependencies ($\epsilon$-PDD) problem, we are additionally given a food web that represents the predator-prey relationships between species. The goal is to save a set of k species of maximum phylogenetic diversity such that for every saved species, at least one of its prey is also saved. This problem is NP-hard even when the phylogenetic tree is a star. The $\alpha$-PDD problem alters PDD by requiring that at least some fraction $\alpha$ of the prey of every saved species are also saved. In this paper, we study the parameterized complexity of $\alpha$-PDD. We prove that the problem is W[1]-hard and in XP when parameterized by the solution size k, the diversity threshold D, or their complements. When parameterized by the vertex cover number of the food web, $\alpha$-PDD is fixed-parameter tractable (FPT). A key measure of the computational difficulty of a problem that is FPT is the size of the smallest kernel that can be obtained. We prove that, when parameterized by the distance to clique, 1-PDD admits a linear kernel. Our main contribution is to prove that $\alpha$-PDD does not admit a polynomial kernel when parameterized by the vertex cover number plus the diversity threshold D, even if the phylogenetic tree is a star. This implies the non-existence of a polynomial kernel for $\alpha$-PDD also when parameterized by a range of structural parameters of the food web, such as its dist[...]

3 citationsRead paper
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