Meetings/Workshops on Scientific Computing in the United States (USA)

Society of Exploration Geophysicists (SEG)

This workshop is designed to showcase the successes and challenges of practising machine learning and data analytics in the geoscience domain to improve accuracy and efficiency of algorithms and/or workflows. For example, in addition to existing applications such as seismic geobody identification and well log data analysis, what other challenging geoscience problems can be formulated and solved effectively by machine learning? How can machine learning algorithms and workflows be tailored to meet the specific physical constraints of geoscience data? How can we fully exploit the power of machine learning and physics-based approaches?

The SIAM Conference on Parallel Processing for Scientific Computing is sponsored by the SIAM Activity Group on Supercomputing.

The SAMO conference is devoted to advances in research on sensitivity analysis methods and their interdisciplinary applications. The SAMO conferences are held every three years. The aim of the SAMO conferences is to bring together users of sensitivity analysis in all disciplines of science. Sensitivity analysis methods are powerful tools in physics, operations research, chemistry, biology, engineering, environmental science, nuclear and industrial safety, economics and finance.

Probability and Statistics, Game Theory,    Modeling and Simulation

Society of Exploration Geophysicists (SEG)

This workshop will discuss the challenges, opportunities, and trends related to the adoption of ML in geoscience research and industrial workflows. Professionals from academia, environmental, energy, and technology companies will present applications and case studies, promote discussion, and propose practical solutions to take greater advantage of ML and AI methods.

Geophysics and Geology,    Neural Networks and Artificial Intelligence, Machine Learning

American Statistical Association

SDSS 2022 will offer many occasions to learn about new data tools and methodologies, see data science in action, and network with experts.

Beyond Big Data: Influencing Science, Technology, and Society

Santa Fe Institute (SFI)

The Graduate Workshop in Computational Social Science Modeling and Complexity brings together a group of advanced Ph.D. students and a small faculty for an intensive two-week study of computational social science modeling and complexity. Participants will learn how to apply tools and approaches from complex systems and computation to questions in the social sciences, while advancing their independent research and working collaboratively with fellow participants. The Workshop convenes in-person and consists of seminars, independent research, formal and informal discussions, and a hackathon-style challenge.

Sociology,    Multidisciplinary or General Events in Informatics

Santa Fe Institute (SFI)

The Foundations and Applications of Humanities Analytics (FAHA) workshop is intended for humanities scholars who are in the early stages of applying quantitative and computational methods in their research. While primarily geared towards current PhD students, we welcome applications from those in both earlier and later stages of a research career. The residential, one-week workshop empowers scholars to apply computational tools in their own research and teaching. Participants must complete the FAHA online course prior to the workshop. The in-person workshop will seminars and tutorials, discussions, and the opportunity to workshop one’s research projects, with the guidance of program faculty. The small, supportive group of fellow participants provide a scholarly community with whom to learn and collaborate on novel humanities analytics projects.

Sociology,    Multidisciplinary or General Events in Informatics

Computational Physics and Numerical Simulation,    Applied Mathematics (in general)

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

Applied Mathematics (in general),    Information Theory, Foundations of Computer Science

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.

Applied Mathematics (in general),    Microscopy

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.

Applied Mathematics (in general),    Microscopy

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.

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 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.