A Pareto Dominance Principle for Data-Driven Optimization

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Despite the widespread use of self-tracking technologies for promoting personal wellbeing, there is limited research on the monitoring of intimate data, particularly urine. To shed light on the design possibilities within this unexplored domain we designed ...

Image information about the state of a building after an earthquake, which can be collected without endangering the post-earthquake reconnaissance activities, can be used to reduce uncertainties in response predictions for future seismic events. This paper ...

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The technological advancements of the past decades have allowed transforming an increasing part of our daily actions and decisions into storable data, leading to a radical change in the scale and scope of available data in relation to virtually any object ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

The 70 kDa heat shock protein (Hsp70) chaperones control protein homeostasis in all ATP-containing cellular compartments. J-domain proteins (JDPs) coevolved with Hsp70s to trigger ATP hydrolysis and catalytically upload various substrate polypeptides in ne ...

This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

In this paper, we present a new parameterization and optimization procedure for minimizing the weight of ribbed plates. The primary goal is to reduce embodied CO2 in concrete floors as part of the effort to diminish the carbon footprint of the construction ...

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A Pareto Dominance Principle for Data-Driven Optimization

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Designing for Intimate Wellbeing: Aidee, a Qualitative Approach To Urine Home Monitoring

Reducing uncertainties in response predictions of earthquake-damaged masonry buildings using data from image-based inspection

Seismic Testing of Adjacent Interacting Masonry Structures – Analysis of a Blind Prediction

Discrete choice modeling in the era of big data

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Autorepression of yeast Hsp70 cochaperones by intramolecular interactions involving their J-domains

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Mesh-based topology, shape and sizing optimization of ribbed plates

Biophysically accurate and machine learning-based surrogate models to optimize neuroprosthesis design and operation