Planned seminars

Europe/Lisbon —

James Halverson

James Halverson, Northeastern University

In this talk I will review essentials of quantum field theory (QFT) and demonstrate how the function-space distribution of many neural networks (NNs) shares similar properties. This allows, for instance, computation of correlators of neural network outputs in terms of Feynman diagrams and a direct analogy between non-Gaussian corrections in NN distributions and particle interactions. Some cases yield divergences in perturbation theory, requiring the introduction of regularization and renormalization. Potential advantages of this perspective will be discussed, including a duality between function-space and parameter-space descriptions of neural networks.

Europe/Lisbon —

Xavier Bresson

Xavier Bresson, Nanyang Technological University
To be announced

Europe/Lisbon —

Miguel Couceiro

Miguel Couceiro, Université de Lorraine

Algorithmic decisions are now being used on a daily basis, and based on Machine Learning (ML) processes that may be complex and biased. This raises several concerns given the critical impact that biased decisions may have on individuals or on society as a whole. Not only unfair outcomes affect human rights, they also undermine public trust in ML and AI. In this talk, we will address fairness issues of ML models based on decision outcomes, and we will show how the simple idea of feature dropout followed by an ensemble approach can improve model fairness without compromising its accuracy. To illustrate we will present a general workflow that relies on explainers to tackle process fairness, which essentially measures a model's reliance on sensitive or discriminatory features. We will present different applications and empirical settings that show improvements not only with respect to process fairness but also other fairness metrics.

Europe/Lisbon —

Caroline Uhler

Caroline Uhler, MIT and Institute for Data, Systems and Society

Massive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. An exciting opportunity in this regard stems from the growing availability of perturbation / intervention data (drugs, knockouts, overexpression, etc.) in biology. In order to obtain mechanistic insights from such data, a major challenge is the development of a framework that integrates observational and interventional data and allows predicting the effect of yet unseen interventions or transporting the effect of interventions observed in one context to another. I will present a framework for causal structure discovery based on such data and highlight the role of overparameterized autoencoders. We end by demonstrating how these ideas can be applied for drug repurposing in the current SARS-CoV-2 crisis.

Europe/Lisbon — Online

Thomas Strohmer

Thomas Strohmer, University of California, Davis
To be announced

A. Pedro Aguiar

A. Pedro Aguiar, Faculdade de Engenharia, Universidade do Porto
To be announced

Europe/Lisbon —

Steve Brunton

Steve Brunton, University of Washington

Many tasks in fluid mechanics, such as design optimization and control, are challenging because fluids are nonlinear and exhibit a large range of scales in both space and time. This range of scales necessitates exceedingly high-dimensional measurements and computational discretization to resolve all relevant features, resulting in vast data sets and time-intensive computations. Indeed, fluid dynamics is one of the original big data fields, and many high-performance computing architectures, experimental measurement techniques, and advanced data processing and visualization algorithms were driven by decades of research in fluid mechanics. Machine learning constitutes a growing set of powerful techniques to extract patterns and build models from this data, complementing the existing theoretical, numerical, and experimental efforts in fluid mechanics. In this talk, we will explore current goals and opportunities for machine learning in fluid mechanics, and we will highlight a number of recent technical advances. Because fluid dynamics is central to transportation, health, and defense systems, we will emphasize the importance of machine learning solutions that are interpretable, explainable, generalizable, and that respect known physics.