Conférences  >  Informatique  >  Calcul scientifique et science des données  >  États-Unis

The aim of this program is to foster the development of new mathematical tools and formalisms that will enable a new generation of ultra-scalable algorithms for a broad range of applications in computational materials science. Topics of interest will include strategies for scalable single-scale simulations, novel massively-parallel scale-bridging algorithms, and integration of extreme-scale computing into experimental and data science workflows. The program will bring together applied mathematicians, materials scientists, computer scientists, and method developers interested in unlocking the potential of upcoming exascale architectures through novel mathematical approaches.

Tiny machine learning is broadly defined as a fast growing field of machine learning technologies and applications including hardware, algorithms and software capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below, and hence enabling a variety of always-on use-cases and targeting battery operated devices.

Système embarqué & informatique ubiquitaire, RFIDs,    Matériel informatique

Institute for Pure and Applied Mathematics (IPAM), UCLA

The vast majority of the computing power available to the materials science community is consumed by a relatively small number of workhorse methods, such as molecular dynamics and density functional theory. These methods have been adapted to run on parallel platforms for decades, but the focus has firmly been on weak-scaling, i.e., on scaling the problem size with the number of processors. While high-performance weak-scaling implementations of these methods are extremely valuable, the focus on increasing length-scales limits opportunities for scientific discovery. Transformative impact requires the capability to leverage computing resources to simultaneously and flexibly increase length scales, time scales, and accuracy. Increasing simulation timescales requires a deep understanding of the mathematics of rare in order to inform novel methods that are specially tailored from the start, as well as strong-scaling computational engines that can leverage large computational resources on problems of relatively small sizes. This requires a dramatic rethinking of how basic algorithms in materials science are derived and implemented. Similarly, the exponential increase in computer resources now enables very high accuracy simulations with methods such as coupled clusters or quantum Monte Carlo. These methods are extremely powerful, but they tend to scale poorly with the number of electrons. The development of new flexible methods where accuracy can be systematically adjusted in order to modify the tradeoff between size and time scales would therefore be extremely beneficial. This workshop will focus on recent development of new mathematical approaches to intensive calculations at massive scale with a focus on new ways to improve scalability (both weak and strong) and extend simulations along the size, time, and accuracy axes simultaneously.

Part of the Long Program New Mathematics for the Exascale: Applications to Materials Science, Integration of direct simulations, online data analysis, and experimental data. Mathematical methods for data assimilation. Large-scale inverse problems. Computation-aided online experimental design at massive scales. Active exploration of chemical space using massive quantum calculations. Workflow infrastructure. Integration of numerically-intensive calculations with ML/data-science at scale.

Physique de la matière condensée et des matériaux,    Physique numérique

Institute for Pure and Applied Mathematics, UCLA.

Quantitatively predicting the properties of “real” structural materials is an extremely challenging endeavor, as macroscale properties depend on characteristics of the material at every scale, from nanometers (short range order, point defects, etc.), to micrometers (grain size, extended defects, etc.), to centimeters (texture, etc.). Similarly, relevant timescales range from atomic vibrations (picoseconds) to microstructural evolution times (hours to years). This extreme breadth of size and time scales makes the accurate simulations with fully-resolved atomic-scale tools (e.g., molecular dynamics) hopeless. Practical solutions must therefore rely on scale-bridging approaches that systematically upscale the lower scale physics into computationally tractable higher-scale constructs. The premise of this workshop is that extreme-scale computing can breathe new life into the field of multiscale modeling by addressing the problems identified above with brute force computing. This workshop will focus on new mathematical approaches to multiscale/multiphysics modeling, with a particular emphasis on the many theoretical and numerical challenges faced at the exascale. The goal is to bring together specialists in a range of massively parallel algorithms and researchers interested in improving the scalability of current techniques.

Part of the Long Program New Mathematics for the Exascale: Applications to Materials Science, Integration of direct simulations, online data analysis, and experimental data. Mathematical methods for data assimilation. Large-scale inverse problems. Computation-aided online experimental design at massive scales. Active exploration of chemical space using massive quantum calculations. Workflow infrastructure. Integration of numerically-intensive calculations with ML/data-science at scale.

Physique de la matière condensée et des matériaux,    Physique numérique

Institute for Pure and Applied Mathematics (IPAM)

One of the key changes in the landscape of high-performance computing in materials science in the last decade is the explosion of high-throughput applications where massive amounts of relatively small quantum calculations fuel the rapid exploration of the chemical space of materials. Such high throughput workflows are prime candidates for the efficient exploitation of exascale resources. This workshop proposes to build on these early successes and to take the next logical step by exploring the challenges and opportunities associated with integrating massive-scale compute-intensive simulations into a wider range of complex scientific workflows. The underlying goal is to identify and explore opportunities to maximize the impact of large-scale computing by improving its integration with various aspects of the scientific enterprise. This workshop will aim at developing new mathematical and computational approaches that enable the inclusion of massive-scale computing into complex scientific workflows.

Part of the Long Program New Mathematics for the Exascale: Applications to Materials Science, Integration of direct simulations, online data analysis, and experimental data. Mathematical methods for data assimilation. Large-scale inverse problems. Computation-aided online experimental design at massive scales. Active exploration of chemical space using massive quantum calculations. Workflow infrastructure. Integration of numerically-intensive calculations with ML/data-science at scale.

Princeton University

International Conference on Statistical Foundations of Data Science and their Applications will be held at Princeton University in May 8-10, 2023. The conference will bring together collaborators and leading researchers in statistics and data science. The conference will provide an excellent forum for scientific communications and promote collaborations among researchers in statistics and data science. The program covers a wide range of topics presenting recent developments and the state of the art in a variety of modern research topics on statistics and data science as well as their applications.

Statistique, Théorie des jeux,    Gestion des connaissances, Big Data Computing

ICERM

This workshop will focus on the intersection of mathematics, statistics, machine learning, and computation, when viewed through the lens of optimal transport (OT). Mathematical topics will include low-dimensional models for OT, linearizations of OT, and the geometry of OT including gradient flows and gradient descent in the space of measures. Relevant statistical topics will include reliable and efficient estimation of OT plans in high dimensions, the role of regularization in computing OT distances and plans, with applications to robust statistics, uncertainty quantification, and overparameterized machine learning. Computation will be a recurring theme of the workshop, with emphasis on the development of fast algorithms and applications to computational biology, high energy physics, material science, spatio-temporal modeling, natural language processing, and image processing.

Program Staff;     Tél.: [1-401-863-5030];     Email.: programstaff@icerm.brown.edu

Statistique, Théorie des jeux,    Mathématiques numériques

The CHEP conferences address the computing, networking and software issues for the world’s leading data‐intensive science experiments that currently analyze hundreds of petabytes of data using worldwide computing resources. The Conference provides a unique opportunity for computing experts across Particle and Nuclear Physics to come together to learn from each other and typically attracts over 500 participants. The event features plenary sessions, parallel sessions, and poster presentations; it publishes peer-reviewed proceedings. The focus of the conference evolves with time to highlight changing technologies and major scientific initiatives.

Institute for Pure and Applied Mathematics (IPAM), UCLA

The final workshop of this program will be an event designed to promote the generation of tangible products from the long program and to explore the best practices in setting up a computational co-design process that ranges from derivation of the key formalisms, to the design of the computational approach, and finally to its implementation. The vision of the program is that the long-term participants will self-organize into working groups designed to identify, analyze, and solve key problems that impede the effective use of extremescale computing in materials science. In order to ensure that these advances make their way into the hands of the community at large, this IPAM “hackathon” will gather code developers, mathematicians, method developers, and computer scientists and engineers from the computer vendors for a week of discussion, hands-on development, and implementation of the new ideas generated during the program. A key objective will be to ensure the transition from “research” codes into “production” codes that can be used and further improved by the community. As such, significant parts of the workshop will involve participants organizing into working groups in order to solve (or take the first steps in solving) practical issues that will have been raised throughout the program. The workshop will include a short series of talks where lead developers of various projects will share their experience and processes when attacking new problems related to evolving architectures, either in terms of new mathematical formalism, increasing computational scales, and architectural details.

Part of the Long Program New Mathematics for the Exascale: Applications to Materials Science, Integration of direct simulations, online data analysis, and experimental data. Mathematical methods for data assimilation. Large-scale inverse problems. Computation-aided online experimental design at massive scales. Active exploration of chemical space using massive quantum calculations. Workflow infrastructure. Integration of numerically-intensive calculations with ML/data-science at scale.

ICERM

How can quantitative science inform policy decisions? This workshop is designed to help mathematicians and data scientists leverage their expertise to contribute positively to social justice efforts ranging from small town issues to global concerns. It is intended for those interested in data science, mathematical modeling, and policy interventions. The main technical skill that the workshop will focus on is data storytelling, specifically with an eye towards policy. Data storytelling includes a mix of rhetorical and technical skills. On the technical side, these skills include data visualization, conclusion description, crafting human stories from data (example: the “middle third” technique), all done effectively and ethically, with an eye toward the audience and purpose of the communication. Participants will be exposed to other technical skills through four projects, each of which will have its own project leadership team. In advance of the workshop, participants will be asked to review the project topics and the technical skills each project is meant to develop and will be asked to rank order their group preferences. The plan over the 4-day period is broken into two days of presentations with active participation and exercises, one day of group work on a team project, and one day of presentations/feedback/next steps.

Program Staff;     Tél.: [1-401-863-5030];     Email.: programstaff@icerm.brown.edu

ICERM

In Summer 2023, ICERM hosts the second of two summer programs entitled The Social Justice and Data Science Summer Research Program. This program aims to increase interest, research training, and capacity for data science for social justice, and to develop both quantitative and qualitative approaches to those professional practices that call for community engagement, critical inquiry, and interdisciplinary cooperation. Building off of Summer 2022's program, which included a workshop on network science and analysis as well as foundational conversations with community partners, the Summer 2023 program will advance the mathematics community's understanding of the complexity of computational social justice work through three emphasis areas (1) policy, (2) education, and (3) community-driven research. As a new field emerges at the face of computational and applied mathematics and social justice, this requires new methods for working across community lines. In order to address the novel and interdisciplinary problems arising out of community needs, participants will work together to develop new or refine existing computational methods whose applications may be broader than the original problem. The organizers are committed to working with humility and in solidarity with one another and with the local community. The program will include engagement with the local community and invest in the education of the next generation of researchers by driving the development and direction of new computational methods for quantitative social justice research. Researchers with expertise and interests in using mathematical models and/or data science to examine social justice issues in policy and/or education are particularly encouraged to apply. The organizers also seek applications from researchers with specialties in digital humanities, computational social science, and data science education.

Program Staff;     Tél.: [1-401-863-5030];     Email.: programstaff@icerm.brown.edu

Mathématiques numériques,    Mathématiques appliquées (en général)

The International Conference on Mass Data Analysis of Images and Signals in Artificial Intelligence and Pattern Recognition MDA 2023 with Applications in Medicine, r/g/b Biotechnology, Food Industries and Dietetics, Biometry, Agriculture, Drug Discovery, and System Biology is held on yearly basis.

Applications in Medicine, r/g/b Biotechnology, Food Industries and Dietetics, Biometry and Security, Agriculture, Drug Discover, and System Biology

Mathematical Sciences Research Institute (MSRI)

This summer school will introduce graduate students to sketching-based approaches to computational linear and multi-linear algebra. Sketching here refers to a set of techniques for compressing a matrix, to one with fewer rows, or columns, or entries, usually via various kinds of random linear maps. We will discuss matrix computations, tensor algebras, and such sketching techniques, together with their applications and analysis.

Cours et évènements pour étudiants en mathématiques,    Gestion des connaissances, Big Data Computing

Multimédia et infographie, Jeu vidéo,    Pédagogie de la science

ICERM

This workshop is designed to highlight the intersections between data science and social justice in K-16 education. The goals of this workshop are to 1) explore, and identify avenues to expand, current research on and methods for using data science in education; 2) raise awareness about research on issues of social justice in education and teaching pedagogy; and 3) work with community partners to create, evaluate and disseminate new K-16 curricular materials. The week will focus on integrating computational social justice research methods, open problems, and best practices across different levels of training. Participants will collaborate on curating professional artifacts, such as: articulating collections of open problems in data science research in social justice; compiling a collection of computational social justice research projects; and developing course modules suitable for use in K-16 education. The results of these collaborations will be shared publicly by the organizers and participants to achieve the broadest impact. While the workshop will be open to and beneficial for all researchers and educators who teach computationally focused courses, priority will be given to early-career faculty and advanced graduate students.

Program Staff;     Tél.: [1-401-863-5030];     Email.: programstaff@icerm.brown.edu

The PEARC conference series is sponsored by the Association for Computing Machinery (ACM) and its HPC and Applications special interest groups (SIGAPP and SIGHPC).

The organizers of the XVI International Conference on Stochastic Programming (ICSP2022) are honored to host the tri-annual Conference at the University of California, Davis Graduate School of Management. The ICSP is the premier event of the Stochastic Programming Society (SPS) , a technical session of the Mathematical Optimization Society that brings together researchers who work on decisions under uncertainty, practitioners in the industrial and institutional sectors share recent theoretical and applied results. The conference aims to present the state-of-the-art in this field as well as neighboring scientific areas.

The Minerals, Metals & Materials Society (TMS)

The 3rd World Congress on High Entropy Alloys (HEA 2023) is a cross-disciplinary technical forum designed to share the latest research advances in single-phase and multiphase metallic, intermetallic, and ceramic high entropy materials for functional or structural applications. HEA 2023 will feature highly focused technical talks on topics that include, but are not limited to, fundamental theory of alloy design, computational modeling and simulation, properties, processing, and applications of high entropy alloys.

Métallurgie et science des matériaux,    Physique appliquée (en general)