Le calcul modulaire est utilisé dans de nombreuses applications des mathématiques, telles que la cryptographie. La réduction modulaire dans un contexte très général est coûteuse, car elle n

The material flow problems under consideration are inspired by real experiments and allow for a multi-scale model hierarchy description.

Adaptation is a recurring theme in biology, offering vital survival mechanisms in dynamic environments through precise regulation of physiological variables. This talk dives into the intriguing

Community detection is one of most prominent tasks in the analysis of complex networks such as social networks, biological networks, and the world wide web. A community is loosely defined as a group

Graph similarity is the basis for many graph and network analyzing tasks.We will discuss similarity concepts for graphs that are relevant for analysis tasks on graph data sets. These include graph

Integrating Simulation, Machine Learning, and High-performance Computing to Support Public Health Decision Making

Research in Knowledge-Enhanced Machine Learning at the Center for Artificial Intelligence (C4AI)

Information Structures for Privacy and Fairness

This seminar will talk about some of the privacy risks of these systems and will describe some recent attacks. It will also discuss why they sometimes fail to deliver. Finally, we will also show that

Statistical Learning Theory for Modern Machine Learning

Freedom of the press is under threat worldwide, and the quality of information that people have access to is dangerously degraded, under the joint threat of non-democratic governments and fake

Modern programming languages such as Java, Scala, and Rust are examples of concurrent higher-order imperative programming languages.

Recently, many societies seem to shift towards more polarization and volatility in opinions, for example in attitudes about immigration, climate policy, or the best policy response to Covid-19. A

The history of computing tells us that computers can be made of almost anything: silicon, gears and levers, neurons, flowing water, interacting particles or even light. Although lithographically

Over the past decade, and particularly over the past five years, research at the interface of topology and neuroscience has grown remarkably fast. Topology has, for example, been successfully applied

With society’s growing reliance on IT systems, the ability to write high-quality software is ever more critical. While a posteriori verification techniques have their role, there is no substitute for

Computer vision has made impressive gains through the use of deep learning models, trained with large-scale labeled data. However, labels require expertise and curation and are expensive to collect.

Runtime monitoring/verification is a lightweight technique that complements other verification methods in a multi-pronged approach towards ensuring software correctness. The technique poses novel

In 1960 Rudolph Kalman published what is arguably the first paper to develop a systematic, principled approach to the use of data to improve the predictive capability of mathematical models. As our

Data produced by the domain of life sciences in the next decade are expected to be highly challenging. In addition to scalability issues which are shared with other applications domains, data produced

The field of quantum information processing (quantum computation and quantum communication) has grown considerably in recent decades. Numerous proof-of-principle experiments on small-scale quantum

The traditional theory of social choice offers no acceptable solution to the problems of how to elect, to judge, or to rank. The classical model —transforming the “preference lists” of

La collaboration « Planck » de l’Agence Spatiale Européenne a récemment publié une carte plein-ciel du rayonnement fossile cosmologique. Cette image est presque littéralement une « photo

Object class detection is a central area of computer vision. It requires recognizing and localizing all objects of predefined set of classes in an image. Detectors are usually trained under

The networks used in biological applications at different scales (molecular, cellular and populational) are of different types, genetic, neuronal, and social, but they share the same dynamical

Les techniques d’imagerie classiques utilisent des ondes pour sonder un milieu inconnu et sont employées pour des applications médicales (échographie) ou géophysiques (séismologie) par exemple.

We introduce and study the computational power of Oritatami, a theoretical model to explore greedy molecular folding, by which the molecule begins to fold before waiting the end of its

The current spreading of multi-scale and multi-process integrative simulations is challenging for the philosophy of science. What is the epistemic function of such a simulation? What does it

Deep neural networks have achieved impressive experimental results in image classification, but can surprisingly be unstable with respect to adversarial perturbations, that is, minimal changes to

Mathematicians have always been prone to error. As proofs get longer and more complicated, the question of correctness looms ever larger. Andrew Wiles’ proof of Fermat’s last theorem contained a