Conferences  >  Informatics  >  Scientific Computing and Data Science

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

Applied Mathematics (in general),    Microscopy

The Research Institute for Mathematical Sciences (RIMS)

Sarvasumana Association

We are pleased to announce our 3rd International Conference on Bioinformatics and Data Science (ICBDS – 2022) and 9th International Conference on Public Mental Health and Neurosciences (ICPMN – 2022) which will be a unique conference where we will connect Biological Function through Computational Genomics to the world of integrated medicine and therapeutics. Functional genomics is a field of molecular biology that attempts to describe gene (and protein) functions and interactions. This science aims to understand the complex relationship between genotype and phenotype on a global (genome-wide) scale of different biological processes. Most researchers now study genes or regions on a “genome-wide” scale (i.e. all or multiple genes/regions at the same time), with the hope of narrowing them down to a list of candidate genes or regions to analyze in more detail. There are several specific functional genomics approaches depending on what we are focused on DNA level (genomics and epigenomics), RNA level (transcriptomics), protein level (proteomics), metabolite level (metabolomics) and phenotype level (phenomics). The recent trends in gene and genome editing technologies, promising genomic information can be modulated in the areas of medicine, agriculture and environment.

Event Secretariat;     Email: sarvasumana@gmail.com

Big data analysis in Bioinformatics Mining with Data Clouds in Bioinformatics Statistical learning in Bioinformatics Bayesian Models and Methods Computational Biology and Neuroscience Artificial Intelligence (IOT) in Bioinformatics Natural Language Processing Brain Machine Interface Artificial Neural Networks Deep Learning in Bioinformatics Bio-Nano-informatics Yogic-Sciences Cultural* and applied psychology Spirituality* and yoga therapy Creative articles*

It is an annual meeting dedicated to statistical methods for post genomic data analysis. The aim of the workshop is to present work from mathematical to applied statistics, but also new areas in high throughput biology that could need new statistical developments. The workshop is usually organized around 3 to 4 invited speakers, and 3 to 4 invited sessions, and one session of contributed abstracts (oral presentations and posters). As SMPGD is especially interested in the statistical, mathematical, algorithmics or modelling questions raised by modern biology, presentations are expected to focus on these points.

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

Society for Industrial and Applied Mathematics (SIAM)

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

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

As the world population lives more and more in large urban areas, and as we are about to reach critical levels of greenhouse gases concentrations, the changing atmospheric composition has increasingly important economic, environmental and health impacts. It is thus becoming important to better quantify air pollution and its sources, using all the available information from observations to computer models, and use it in a synergistic way to maximize the information content – this is what data assimilation and inverse modeling aim for. This interdisciplinary workshop brings together engineers and researchers from numerical mathematics, statistics, and environmental sciences, to develop and innovate on the assimilation and inverse methods to address the specific issues related to atmospheric composition and chemistry. It will also be a forum to train new scientists in this emerging field, and to promote the research towards new operational monitoring products.

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.

Metallurgy and Materials Science,    Applied Physics (general)

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.

Embedded Systems & Ubiquitous Computing, RFIDs,    Computing Hardware

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.

Condensed Matter Physics and Materials,    Computational Physics and Numerical Simulation

CIRM – Centre International de Rencontres Mathématiques

The workshop will bring together both experienced researchers and promising young scientists from different scientific backgrounds, interested in the interplay between theoretical and applied research. It will promote the participation of male and female researchers with a good gender balance among speakers. The conference will place an emphasis on the connections between the areas and on directions for future research. We expect a high-level and high-impact event which will inspire new collaborations and will open new research directions.

Modeling and Simulation,    Complex Systems, Chaos and Self-Organisation

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.

Condensed Matter Physics and Materials,    Computational Physics and Numerical Simulation

Isaac Newton Institute for Mathematical Sciences (INI)

In tandem with the workshop on Mathematical Foundations, this week-long workshop will focus on algorithms and methodology to address key tasks in data-driven engineering. A particular focus of this workshop will be on highlighting computational challenges in the application areas of the DDE programme (aeronautics, chemical engineering, and civil and environmental engineering) and showcasing promising emerging tools.

data assimilation and statistical inverse problems, reinforcement learning and control, model order reduction, bridging mechanistic and data-driven models and methods

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.

Casa Matemática Oaxaca (CMO)

With the advances of high-throughput technologies, genomics and related fields are hitting the thick data era. Many problems in biology cannot be studied with traditional and exact methods, and the development and application of tailored methods to analyze rich and large-scale biological datasets are still lagging behind. In addition, diverse layers of knowledge in genomics, such as transcriptomics, epigenomics, proteomics and metabolomics need to be integrated and studied jointly with multi-omics approaches to unfold the complex mechanisms of biological systems and organisms. Sparked with creative solutions from data science, computational and data scientists can contribute to the development and implementation of innovative solutions to extract, wrangle, visualize, analyze and store complex data resulting from high-throughput technologies with efficient, transparent and reproducible analytical pipelines.

Probability and Statistics, Game Theory,    Systems Biology and Computational Biology

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.

Probability and Statistics, Game Theory,    Information & Knowledge Management, Big Data Computing

ICMS - International Centre for Mathematical Sciences

This workshop is part of the Isaac Newton Institute programme on The mathematical and statistical foundation of future data-driven engineering. A particular focus of this workshop will be on the underlying mathematical and statistical foundations required to make computational tools efficient and transferable.

data assimilation and statistical inverse problems, reinforcement learning and control, model order reduction, bridging mechanistic and data-driven models and methods.

Isaac Newton Institute for Mathematical Sciences (INI)

In tandem with the workshop on Computational Challenges and Emerging Tools, this week-long workshop will focus on algorithms and methodology to address key tasks in data-driven engineering. A particular focus of this workshop will be on the underlying mathematical and statistical foundations required to make computational tools efficient and transferable.

data assimilation and statistical inverse problems, reinforcement learning and control, model order reduction, bridging mechanistic and data-driven models and methods

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

Probability and Statistics, Game Theory,    Numerical Analysis and Computational Mathematics

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.

European Geosciences Union (EGU)

With the birth of geosciences and then of computational geosciences, people's attitude towards the world has changed. People are no longer passive observers but rather more proactive players in their environment and the knowledge gained from computational geosciences has been used to save lives and prepare for future scenarios. Now, with the Exascale computing era dawning upon us, even more accurate, clearer and faster data will be within our reach.

Geophysics and Geology,    Computational Physics and Numerical Simulation

MIAPbP - Munich Institute for Astro-, Particle and BioPhysics

Domains such as cosmology, astrophysics, nuclear or particle physics rely heavily on the use of stochastic simulation to design data analyses and perform inference with respect to fundamental physics theories. With the advances of Deep Learning, Bayesian inference techniques, a new frontier has come into view that focuses on the extension of traditional scientific programming along the two distinct directions of differentiable programming and probabilistic programming. The former plays an important role in navigating high-dimensional spaces, while the latter can manifestly incorporate generative modeling and inference into scientific data analyses. Both promise to expand the science reach of data-intensive domains through, e.g., the integration of domain knowledge and symmetries into neural networks, algorithmic robustness, alignment of science and training optimization goals, uncertainty quantification, causal inference, and more complete inference. However, most production scientific pipelines are not yet compatible with these techniques and their widespread adoption would represent a deep change in the scientific software landscape. This program will provide the opportunity for researchers from Computer Science and Fundamental Physics to develop applications as well as strategies for integrating these paradigms into existing scientific workflows and to close the gap of what is theoretically possible and practically deployed.

Computational Physics and Numerical Simulation,    High Energy Physics, Particles and Fields

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Neural Networks and Artificial Intelligence, Machine Learning,    Information & Knowledge Management, Big Data Computing

The 11th International Conference on Isogeometric Analysis (IGA 2023) will be organized in Lyon, France, on 18-21 June 2023. IGA 2023 is a meeting place for scientists focusing on isogeometric methods : researcher developing computational methods, engineers addressing challenging applications, ... The conference is one of the thematic conferences of the European Community on Computational Methods in Applied Sciences (ECCOMAS).

Email: https://iga2023.sciencesconf.org/

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

Numerical Analysis and Computational Mathematics,    Applied Mathematics (in general)

MIAPbP - Munich Institute for Astro-, Particle and BioPhysics

We are happy to announce that the Differentiable and Probabilistic Programming for Fundamental Physics program includes a three day workshop. The workshop will contain talks about recent advancements in differentiable programming (DP) and probabilistic programming in the context of Physical Sciences. The workshop's diverse set of invited speakers will cover DP/ProbProg users and experts, toolmakers and authors of promising technologies. The workshop aims to explore the potential science impact of DP/ProbProg and to understand the steps towards, and challenges of, a successful implementation of these paradigms in fundamental physics.

Computational Physics and Numerical Simulation,    High Energy Physics, Particles and Fields

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

Cells are the fundamental building blocks of life. Recent advancement in biotechnology has allowed us to peek inside this every cell for better understanding of biology and human disease. Single-cell technology also generates big and complex data and brings about new data science challenges for computational and life scientists. The objective of this workshop is to bring together international leaders in diverse disciplines including mathematical, statistical, computational, biological and medical in a collaborative atmosphere to develop the collaborative capacity that will tackle various underlying data science challenges in single-cell research.

Molecular Biology,    Systems Biology and Computational Biology

National and Kapodistrian University of Athens (NKUA) and Littoral Côte d’Opale (ULCO) University in France.

The aim of this conference is to bring together researchers working in numerical analysis, scientific computation and applications. Participants will present and discuss their latest results in this area.

LMPA, ULCO, 50 rue F. Buisson, Calais France;     Phone: [0626622981];     Email: jbilou@univ-littoral.fr

Numerical and Multilinear Algebra, Maching Learning, Optimization, Numerical Methods and Analysis of PDEs, Approximation, Domain Decomposition methods, Inverse and Ill-posed problems for image processing, Computational Statistics.

Numerical Analysis and Computational Mathematics,    Applied Mathematics (in general)

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

In this workshop, we will explore how the research realms of generative modelling and 3D neural representations can benefit from each other, as well as explore novel applications in different areas of science and technology.

Modeling and Simulation,    Neural Networks and Artificial Intelligence, Machine Learning

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.

Courses and Events for Math Students and Early Career Researchers,    Information & Knowledge Management, Big Data Computing

Multimedia, Computer Graphics and Visualization, Games,    Didactics of Scientific Disciplines

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;     Phone: [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.

SEIT Lab, Department of Computer Science, University of Cyprus

Euro-Par is the prime European conference covering all aspects of parallel and distributed processing, ranging from theory to practice, from small to the largest parallel and distributed systems and infrastructures, from fundamental computational problems to applications, from architecture, compiler, language and interface design and implementation, to tools, support infrastructures, and application performance aspects. We invite participants to submit original and unpublished works as posters and/or demos to the Euro-Par 2023 conference presenting the latest breakthroughs which have been developed in H2020, Horizon Europe, EuroHPC JU, national, regional and international R&D projects. Topics of interest should cover the conference scope and tracks (please visit the conference web site for more information about the topics covered by Euro-Par). The poster/demo sessions will provide an excellent opportunity for students and researchers to present early- stage research results and receive valuable feedback on their ongoing research from the community.

Phone: [+35722892693];     Email: george@ucy.ac.cy

Distributed, Edge and Cloud Computing,    High Performance Computing, Supercomputing, Parallel Computing

Banff International Research Station (BIRS) for Mathematical Innovation and Discovery

What could be a bigger source of "big data" than the entire Universe? Researchers in statistics and computer science are developing new methods to analyse the "big data" that companies and governments want to understand. To test out these methods they need big datasets that everyone can share --- and new astronomical telescopes just happen to be gearing up to generate observations of billions of stars and galaxies that fit the bill. Insights from statistics will help astrophysicists to get the most out of these observations. The Astrostatistics 2023 workshop will bring together statisticians and astrophysicists to learn the technical languages of the two fields and identify how to work together to combine the excitement of space with big data.

Astronomy, Astrophysics and Cosmology,    Probability and Statistics, Game Theory

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.

Metallurgy and Materials Science,    Applied Physics (general)

CIRM – Centre International de Rencontres Mathématiques

Modeling and Simulation,    Computational Physics and Numerical Simulation

CIRM – Centre International de Rencontres Mathématiques

Information & Knowledge Management, Big Data Computing,    Social Networks and Social Aspects of Information Technology

ICMS - International Centre for Mathematical Sciences

This workshop will gather researchers from the intersecting fields of inverse problems, uncertainty quantification, data analytics, machine learning, and related areas to discuss novel theory and new methods for big data challenges. A focused multifaceted research group gathered at one location is an excellent opportunity for established and early career researchers. The aim of this workshop is to foster a diverse, open, and friendly environment to advance this field, open new directions, and to stimulate new collaboration.

Information & Knowledge Management, Big Data Computing,    Applied Mathematics (in general)

CEA, Alternative Energies and Atomic Energy Commission

In addition to promoting the advances in the traditional topics of the conference, encompassing super-computing for nuclear applications and Monte Carlo simulation, SNA + MC2024 will also emphasize novel subjects with strong scientific, technological and societal stakes: the recent breakthroughs in High Performance Computing, environmental issues and energy transition.

Nuclear Energy,    High Performance Computing, Supercomputing, Parallel Computing