Viability Principles for Constrained Optimization Using a (1+1)-CMA-ES

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This paper develops a new storage-optimal algorithm that provably solves almost all semidefinite programs (SDPs). This method is particularly effective for weakly constrained SDPs under appropriate regularity conditions. The key idea is to formulate an app ...

In this thesis, we propose new algorithms to solve inverse problems in the context of biomedical images. Due to ill-posedness, solving these problems require some prior knowledge of the statistics of the underlying images. The traditional algorithms, in th ...

In a context of rapid global change, one of the key components for the survival of species is their genetic evolutionary potential for adaptation. Many methods have been developed to identify genetic variants underpinning adaptation to climate, but few too ...

Protein structure is tightly intertwined with function according to the laws of evolution. Understanding how structure determines function has been the aim of structural biology for decades. Here, we have wondered instead whether it is possible to exploit ...

Isogeometric shape optimization has been now studied for over a decade. This contribution aims at compiling the key ingredients within this promising framework, with a particular attention to sensitivity analysis. Based on all the researches related to iso ...

The development of new solid-state electrolytes is a key step in improving the performance and safety of battery technology. Although the use of first-principle methods has proved invaluable in better understanding the process at play in these materials, ...

Darwin is a genomics co-processor that achieved a 15000x acceleration on long read assembly through innovative hardware and algorithm co-design. Darwins algorithms and hardware implementation were specifically designed for DNA analysis pipelines. This pape ...

As demands for computing have been continuously increasing, various solutions have been proposed. Some approaches deal with improving algorithms and software programs, mainly through the tuning of advanced heuristics and learning methods. Some other tackle ...

The interest in multi-drone systems flourished in the last decade and their application is promising in many fields. We believe that in order to make drone swarms flying smoothly and reliably in real-world scenarios we need a first intermediate step which ...

This thesis develops mathematical programming frameworks to operate electric autonomous vehicles in the context of ride-sharing services. The introduced problem is a novel variant of the Dial-a-Ride Problem (DARP), denoted by the electric Autonomous Dial-a ...

Adaptive evolution in new or changing environments can be difficult to predict because the functional connections between genotype, phenotype, and fitness are complex. Here, we make these explicit connections by combining field and laboratory experiments i ...

Large dataflow designs are a result of behavioral specification of modern complex digital systems and/or a result of unfolding and transforming looped and branched programs. Since deep-submicron silicon technology provides large amounts of available resour ...

By combining well-established population genetic theory with high-throughput sequencing data from natural populations, major strides have recently been made in understanding how, why, and when vertebrate populations evolve crypsis. Here, we focus on backgr ...

The central task in many interactive machine learning systems can be formalized as the sequential optimization of a black-box function. Bayesian optimization (BO) is a powerful model-based framework for \emph{adaptive} experimentation, where the primary go ...

This research presents a mathematical formulation for optimizing integration of complex industrial systems from the level of unit operations to processes, entire plants, and finally to considering industrial symbiosis opportunities between plants. The fram ...

Viability Principles for Constrained Optimization Using a (1+1)-CMA-ES

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An Optimal-Storage Approach to Semidefinite Programming Using Approximate Complementarity

From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging

Spatial areas of genotype probability: Predicting the spatial distribution of adaptive genetic variants under future climatic conditions

Protein Structural Information and Evolutionary Landscape by In Vitro Evolution

A New Lighting on Analytical Discrete Sensitivities in the Context of IsoGeometric Shape Optimization

Accelerating materials discovery for solid state electrolytes

IMPACT: Interval-based Multi-pass Proteomic Alignment with Constant Traceback

Roundtable Panel Discussion at DAC 2019: Evolutionary Computing or Heuristic Forever?

The influence of limited visual sensing on the Reynolds ﬂocking algorithm

The electric autonomous dial-a-ride problem

Linking a mutation to survival in wild mice

Pipeline Synthesis and Optimization from Branched Feedback Dataflow Programs

The population genetics of crypsis in vertebrates: recent insights from mice, hares, and lizards

Robust Adaptive Decision Making: Bayesian Optimization and Beyond

A Mixed-Integer Linear Programming Formulation for Optimizing Multi-Scale Material and Energy Integration