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The goal of this long program proposal is to bring together senior and junior applied mathematicians, physicists, chemists, materials scientists, engineers and biologists to discuss and debate on the current status and future perspectives of modern microscopy using computation, mathematics and modeling. Cryo-EM has revolutionized biology and life science (including very recently solving the 3D atomic structure of COVID-19, which has been greatly facilitating the development of the vaccines) and aberration-corrected electron optics and high brightness X-ray sources have transformed physical science imaging. The next steps in these fields will advance by orders of magnitude the temporal resolution and energy resolution, while maintaining atomic spatial resolution, in a variety of sample environments from near zero Kelvin in vacuum to temperatures of a thousand degrees in a highly corrosive atmosphere. These advances will transform research in macromolecules, materials, energy technologie

Angewandte Mathematik (allgem.),    Mikroskopie

Cognilytica

Enterprise Data & AI is excited to announce the October 2022 featured presentation ‘AI at Shell’ by featured guest speakers Prakalp Somawanshi, Principal AI Engineer and Naveen Kapoor, Senior Machine Learning Engineer at Shell on Thursday, October 6, 2022 from 11:30 AM - 1 PM ET!

Shell is an international energy company that aims to meet the world’s growing need for more and cleaner energy solutions in ways that are economically, environmentally, and socially responsible. Powering Progress sets out their strategy to accelerate the transition of our business to net-zero emissions. It has four main goals in support of their purpose, generating shareholder value, achieving net-zero emissions, powering lives and respecting nature. It is underpinned by their core values and focus on safety.

Digital technologies are improving the experience of their customers and making Shell a more efficient business. This digital transformation of the energy industry is improving efficiency and safety, and it is facilitating the increased use of renewable energy. Shell is actively working on a range of digital technologies, including robotics, 3D printing, cloud computing, advanced analytics, Blockchain, High Performance Computing and AI/ML. From machine learning to computer vision, deep learning to virtual assistants and autonomous vehicles to robotics, Shell has been focused on a range of technologies that have supported advances in AI. Many of the algorithms behind AI and machine-learning systems are not new but limited volumes of accessible data have hampered their application. Easy access to vast data volumes at Shell is making many AI algorithms smarter. Shell is developing and deploying Machine Learning / Deep Learning solutions, developing MLOps practices and applying engineering principles to scale and productionize developed solutions for different business units – Upstream, Downstream, Integrated Gas, Power, and New Energies. In this talk, Prakalp and Naveen will share their experience, learnings and the processes they follow in delivering AI technology solutions in collaboration with external technology providers.

Be sure to stick around for Q&A with Prakalp and Naveen!

Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen,    Erneuerbare Energien und Wäremekraftwerke

Cognilytica

Cognilytica is excited to host Kathleen Walch and Ron Schmelzer, Managing Partners at Cognilytica and their presentation ‘Intro to CPMAI Methodology for Project and Product Managers’ on Wednesday, October 19, 2022 at 1 PM ET!

Far too often, agencies, organizations, and consulting firms are running data and AI projects without taking the right steps to ensure their success. Agile and iterative approaches have become adopted best practices for application development projects, but why don’t we have something similar when it comes to advanced analytics, big data, and AI projects? Without using best-practices approaches that standardize steps for data preparation, data identification, and data protection, it’s hard to achieve the success you’re expecting, wasting money and resources, leading to failure. This session will walk through the Cognitive Project Management for AI (CPMAI) methodology to provide the foundation needed for project success, especially as you incorporate advanced analytics and AI projects. The CPMAI methodology is the established best practice for AI & ML projects, and increasingly will be demanded by organizations and agencies that plan to develop, procure, and deliver AI and advanced analytics projects. This session will provide real world examples of how CPMAI methodology allows individuals to gain the skills needed for AI project success. Be sure to stick around for Q&A at the end!

Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen,    Informations & Wissensmanagement, Big Data Computing

Developing multi-dimensional electron microscopy would have transformative impact in physics, chemistry, materials science, nanoscience and other fields. Advancing the field requires integration of state-of-the-art atomic electron tomography, ptychography, 4D scanning transmission electron microscopy and spectroscopic techniques as well as powerful computational algorithms and mathematical modeling. This workshop will provide the opportunity to present and exchange ideas, share data, and introduce new tools and develop new imaging paradigms needed in a variety of fields.

Angewandte Mathematik (allgem.),    Mikroskopie

Our world is changing rapidly. Complex challenges are suddenly arising alongside an urgent demand for answers. Leveraging HPC, skilled minds employ innovative technologies to respond to the call — driven by data, simulating possibilities, and unlocking new solutions. By harnessing the power of HPC, the SC community is unleashing a deeper understanding of our world at an unprecedented pace.

The implementation of advanced algorithms with user-friendly software packages has made the cryo-EM method straightforward in practice. Despite these exciting advances, major challenges still remain, such as higher throughput, mapping continuous deformations, reconstruction of small macromolecules, and in sample preparation. For pleomorphic structures, cryo-electron tomography (cryo-ET) is the method of choice, but it is typically limited to lower resolution compared to cryo-EM, and it remains a daunting challenge to determine macromolecular structures in situ at high resolution. Overcoming these major challenges in cryo-EM and cryo-ET requires significant advances in sample preparation, hardware development and computational algorithms. The goal of this workshop is to bring together leading experts in cryo-EM, biologists, applied mathematicians and physicists to discuss and debate the current challenges and future perspectives of this very exciting cross-disciplinary field.

Angewandte Mathematik (allgem.),    Mikroskopie

The focus of this year’s meeting is the opportunities and challenges in working with high-throughput phenotyping data. High-throughput phenotyping data encompasses, but is not limited to: omics, sensor and imaging data, electronic health records, activity monitoring, and sequencing. We will discuss the insights we gain into genetics of complex traits using diverse datasets, the challenges we need to overcome, and the opportunities for future research. Statistical, computational, and experimental methods, as well as results stemming from their application, will be presented.

Opportunities and Challenges in Using High-Throughput Phenotyping in Quantitative Genetics and Genomics

The Quantitative Genetics and Genomics GRC is a premier, international scientific conference focused on advancing the frontiers of science through the presentation of cutting-edge and unpublished research, prioritizing time for discussion after each talk and fostering informal interactions among scientists of all career stages. The conference program includes a diverse range of speakers and discussion leaders from institutions and organizations worldwide, concentrating on the latest developments in the field. The conference is five days long and held in a remote location to increase the sense of camaraderie and create scientific communities, with lasting collaborations and friendships. In addition to premier talks, the conference has designated time for poster sessions from individuals of all career stages, and afternoon free time and communal meals allow for informal networking opportunities with leaders in the field.

Leveraging High-Throughput Phenotyping Techniques to Study Complex Traits

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.

Institute for Pure and Applied Mathematics (IPAM), UCLA

UC Santa Barbara, Kavli Institute for Theoretical Physics (KITP)

This conference aims to explore the application of data-driven tools to learn about galaxy formation physics. It endeavors to maximize the gain from astrostatistics, data science, and machine learning for the galaxy formation field as a whole, by emphasizing the translation of data-driven results to physical understanding. This conference will focus on a sharing of expertise in data exploration and analysis tools, and an open discussion of how these may teach us about the physics of galaxy formation and evolution.

The Minerals, Metals & Materials Society (TMS)

The 7th World Congress on Integrated Computational Materials Engineering (ICME 2023) convenes leading researchers and practitioners to share the latest knowledge and advances in the discipline. This congress is the recognized hub of interaction among software developers and process engineers along the entire production chain, as well as for materials scientists and engineers developing new materials. This is the only congress dedicated to bringing all stakeholders together from across nations, disciplines, and organizations to focus on integration priorities and gaps that need to be addressed in order to advance the field. ICME 2023 will benefit researchers, software developers, metallurgists, materials scientists and engineers, process engineers, senior scientists, chief technology officers, and a variety of others working in R&D. Attendees will gain insights on recent advances and discuss opportunities to overcome challenges in the field.

Metallurgie und Werkstoffkunde,    Angewandte Physik (allgemein)

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.

Eingebettete Systeme, RFIDs,    Computerhardware

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.

Festkörperphysik,    Computerphysik und Numerische Simulation

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.

Festkörperphysik,    Computerphysik und Numerische Simulation

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.

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

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

Numerische Analysis,    Angewandte Mathematik (allgem.)

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.

Kurse und Veranstaltungen für Studenten der Mathematik,    Informations & Wissensmanagement, Big Data Computing

Mulitimedia und Computergrafik, Spiele,    Didaktik in der Wissenschaft

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.

Metallurgie und Werkstoffkunde,    Angewandte Physik (allgemein)