Causal and mathematical models are widely used for decision making and policy evaluation at both the micro and macro levels. For example, causal models using large datasets are used to evaluate treatment efficacy in HIV; mathematical models are used to simulate the effects of prevention or policy measures to improve health outcomes or reduce the spread of infectious diseases. Entities such as the World Health Organization and UNAIDS rely on these models to set wide-ranging and high-impact policy related to treatment and prevention of infectious disease.

Causal models tend to rely on large-scale cohort data, while mathematical models in many ways represent evidence synthesis. Important methodologic issues in the development, application, and interpretation of these models include the role of untestable assumptions, transportability of findings to specific populations of interest, model calibration and validation, and uncertainty quantification. The datasets used to develop these... (more)

The machine learning revolution is already having a significant impact across the social sciences and business, but it is also beginning to change computational science and engineering in fundamental and very varied ways.

We are experiencing the rise of new and simpler data-driven methods based on techniques from machine learning such as deep learning. This revolution allows for the development of radical new techniques to address problems known to be very challenging with traditional methods and suggests the potential dramatic enhancement of existing methods through data informed parameter selection, both in static and dynamic modes of operation. Techniques are emerging that allows us to produce realistic solutions from non-sterilized computational problems in diverse physical sciences.

However, the urgent and unmet need to formally analyze, design, develop and deploy these emerging methods and develop algorithms must be addressed. Many central problems, e.g., enforcement of... (more)

The powerful communication between mathematics and other scientific communities can be challenging. Notably, the interface between nonlinear algebra and computer vision has seen a boost of activity, giving rise to the Algebraic Vision community. At ICERM, this interface was jump-started at the Vision Working Group of the 2018 Nonlinear Algebra Program, when leading researchers came together to formulate new problems and solutions to push the field forward. We would like this activity to continue into the 2019 Computer Vision Program at ICERM, in the form of a research cluster, further bridging the fields to new levels.

This will be a hands-on workshop focused on a specific computational problem: enumerating all isomorphism classes of abelian varieties of of dimension g over a finite field of cardinality q, for a suitable range of integers g and q. Isogeny classes of abelian varieties over finite fields have been previously classified by Weil polynomials and can be found in the L-functions and Modular Form Database. The goal of this workshop is to refine this to the level of isomorphism classes, and, whenever possible, to construct explicit representatives for each isomorphism class. By exploiting recent theoretical and computational advances and assembling an appropriate team of experts, we hope to make rapid and substantial progress during this short, focused workshop and to have results available in advance of the conference on the Arithmetic of Low-dimensional Abelian Varieties that will take place at ICERM in June.

This is a closed workshop that will not be accepting applications.(more)

Computer vision is an inter-disciplinary topic crossing boundaries between computer science, statistics, mathematics, engineering and cognitive science.

Research in computer vision involves the development and evaluation of computational methods for image analysis. This includes the design of new theoretical models and algorithms, and practical implementation of these algorithms using a variety of computer architectures and programming languages. The methods under consideration are often motivated by generative mathematical models of the world and the imaging process. Recent approaches also rely heavily on machine learning techniques and discriminative models such as deep neural networks.

Problems that will be considered in the program include image restoration, image segmentation, object recognition and 3D reconstruction. Current approaches to address these problems draw on a variety of mathematical and computational topics such as stochastic models, statistical methods,... (more)

Recent advances in machine learning have had a profound impact on computer vision. Simultaneously, success in computer vision applications has rapidly increased our understanding of some machine learning techniques, especially their applicability. This workshop will bring together researchers who are building a stronger theoretical understanding of the foundations of machine learning with computer vision researchers who are advancing our understanding of machine learning in practice.

Much of the recent growth in the use of machine learning in computer vision has been spurred by advances in deep neural networks. At the same time, new advances in other areas of machine learning, including reinforcement learning, generative models, and optimization methods, hold great promise for future impact. These raise important fundamental questions, such as understanding what influences the ability of learning algorithms to generalize, understanding what causes optimization in learning to converge... (more)