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

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

The summer school is taking place in Denmark and is the sixteenth summer school jointly organized by DIKU, DTU, and AAU.

Biomedical image analysis is a broad topic that covers a variety of disciplines in modern computer science, AI Research, medical sciences and biology. In this summer school, we aim at given the participant a good overview of the current state-of-the-art in several of the fundamental topics used in modern biomedical image analysis.

Mustererkennung und Bildverarbeitung, Maschinelles Sehen,    Veranstaltungen für Studenten der Informatik

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

The Energy Modelling Platform – Joint Global Training School 2024 (EMP-G) will be held from 12th to 23rd August. Hosted by the International Centre for Theoretical Physics (ICTP), this is a fantastic opportunity to get free training, access to discussion forums, and coaching skills in models and tools for all of your energy planning needs. Before the beginning of the event on 12th August, there will be a one-week advance tool training, from 5th to the 9th of August.

Kurse und Veranstaltungen für Physikstudenten,    Simulation

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

Kurse und Veranstaltungen für Physikstudenten,    Veranstaltungen für Studenten der Informatik

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

Ökosysteme, Umwelt und nachhaltige Entwicklung,    Simulation

University of Waterloo, Canada

The MCQMC conference series is a biennial meeting focused on Monte Carlo (MC) and quasi-Monte Carlo (QMC) methods in scientific computing. The conference attracts between 150 and 200 participants. Its aim is to provide a forum where leading researchers and users can exchange information on the latest theoretical developments and important applications of these methods. Recent conferences have attracted researchers in Markov chain Monte Carlo (MCMC). In a nutshell, MC methods study complex systems by simulations fed by computer-generated pseudorandom numbers. QMC methods replace these random numbers by more evenly distributed (carefully selected) numbers to improve their effectiveness. A large variety of special techniques are developed and used to make these methods more effective in terms of speed and accuracy. The conference focuses primarily on the mathematical study of these techniques, their implementation and adaptation for concrete applications, and their empirical assessment.

Statistik, Spieltheorie ,    Simulation

This is a School organized by the ErUM-Data-Hub with support from DIG-UM.

In this school you will learn how Python code can be accelerated. A focus will be placed on numeric NumPy-like array computations. In addition, running these array computations on hardware accelerators, i.e., GPUs, will play a key role in this school. The school is intended for young researchers - especially for PhD students - who regularly work with the scientific Python ecosystem. Requirements are good knowledge of the scientific Python ecosystem, basics of the C++ programming language are beneficial.

Kurse und Veranstaltungen für Physikstudenten,    Veranstaltungen für Studenten der Informatik

HEP Software Foundation (HSF)

PyHEP.dev is an in-person, informal workshop for developers of Python software in HEP to plan a coherent roadmap and make priorities for the upcoming year. It complements the PyHEP Users online workshop, which is intended for both developers and physicists.

Casa Matemática Oaxaca (CMO)

The RSS conference brings together the statistics and data science community from across the UK, Europe and around the world to share knowledge and learn about latest developments. With over 600 attendees from around 30 countries and all sectors using statistics and data, this is a must-attend event for professionals, researchers, students and everyone interested in the impactful application of statistics and data science.

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

Festkörperphysik,    Hochleistungsrechner, Parallelrechner

CNR - National Research Council of Italy

The pervasive influence of social media platforms has undeniably transformed the landscape of contemporary discourse. While these platforms facilitate open discussions on diverse topics, they simultaneously present significant challenges associated with misinformation and the manipulation of public opinion. With the advent of advanced Artificial Intelligence (generative AI) and Large Language Models (LLM), the dissemination of misinformation has become increasingly sophisticated, threatening the integrity of online discourse and eroding trust in institutions and among communities. It is crucial for policymakers, academics, and industry leaders to collectively address these issues. The goal of the summer school on "Artificial Intelligence for a Secure Society" is to empower individuals with the means to foster a more secure communities, both online and offline. By gathering experts from diverse fields, the program will tackle pressing challenges through the lens of AI. Participants will learn how artificial intelligence can be used to identify, analyze, mitigate, and counteract various threats, including misinformation, disinformation, fake news, orchestrated online campaigns, and information warfare.

Topics covered will include:

- Data Science

- Artificial Intelligence

- Cybersecurity

- Social networks Analysis

- Network Science

- Generative AI

- Trustworthy AI

- Explainable AI

- Neural Networks

- Machine/Deep Learning

Features of the Summer School:

- Keynotes

- Interactive lectures

- Hands-on group projects

- Networking opportunities with experts and peers

Who Can Apply:

The summer school is open to Postgraduate Students, PhD Students, and Researchers interested in Artificial Intelligence.

Deadlines:

- Early Registration: Jul 10, 2024

- Late Registration: Aug 10, 2024

Costs:

There are different participation fees for different solutions:

- Solution S1: € 1050

- Solution S2: € 800

- Solution S3: € 550

These fees are valid for early registration.

Registration costs € 150 more for late registration.

The registration fee includes:

- Attendance to all summer school talks and sessions

- Meals

- Participation to three social events

- Lodging (Single - S1, Double - S2, No Room - S3)

- Attendance certificate

For more information, please visit the summer school website https://ai4securesociety.sobigdata.it/ or contact the organizers at carmela.comito@icar.cnr.it and maurizio.tesconi@iit.cnr.it

General Chairs

- Carmela Comito (ICAR-CNR)

- Maurizio Tesconi (IIT-CNR)

- Giuseppe Manco (ICAR-CNR)

Organizing Committee

- Serena Tardelli (IIT-CNR)

- Angelica Liguori (ICAR-CNR)

- Beatrice Rapisarda (ISTI-CNR)

- Marco Avvenuti (Unipi)

- Massimo Guarascio (ICAR-CNR)

The event "Summer School on Artificial Intelligence for a Secure Society" was organized as part of the SoBigData.it project's offerings aimed at training new users and communities of the research infrastructure (SoBigData.eu).

“SoBigData.it receives funding from European Union – NextGenerationEU – National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilienza, PNRR) – Project: “SoBigData.it – Strengthening the Italian RI for Social Mining and Big Data Analytics” – Prot. IR0000013 – Avviso n. 3264 del 28/12/2021.”

The event is also supported by the project SERICS (PE00000014) under the NRRP MUR program funded by the EU - NGEU.

Email: beatrice.rapisarda@isti.cnr.it

Data Science, Artificial Intelligence, Cybersecurity, Social networks Analysis, Network Science, Generative AI, Trustworthy AI, Explainable AI, Neural Networks, Machine/Deep Learning

Informatik Ausbildung, Lehrgänge,    Veranstaltungen für Studenten der Informatik

Casa Matemática Oaxaca (CMO)

Our society faces various grand challenges that it urgently needs to address over the coming years, such as mitigating climate change, combating environmental damage, and providing effective, affordable healthcare for everyone. Data science and Artificial Intelligence are expected to play a key role in successfully tackling many of these challenges. While recent progress in AI was driven mostly by empirical developments, we are entering now a new phase in which questions of trustworthiness, scalability, fairness, privacy, reproducibility, and explainability emerge as key factors. Fundamentally new ideas and approaches are needed to address these open problems, which will require a close collaboration between mathematicians, statisticians, computer scientists, and engineers. This workshop features an interdisciplinary group of exceptional scientists from across the world to present recent developments, foster new interactions, and create novel ideas for the solution of the aforementioned data science problems.

Mustererkennung und Bildverarbeitung, Maschinelles Sehen,    Infinitesimalrechnung, Differentialgleichungen, Integralrechnung

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Modern algorithms for data analysis require the use of advanced probabilistic methods to achieve desirable scalability and accuracy guarantees. At the same time, modern data-analysis tasks require the use of advanced statistics to handle challenges such as testing for multiple hypotheses or identifying dependencies among data points, such as in time series or graphs. Probabilistic methods are also at the core theoretical computer-science areas, such as sublinear algorithms and average-case analysis. To obtain efficient data-analysis algorithms, probabilistic methods require careful balancing of theoretical and practical aspects. This Dagstuhl Seminar brings together researchers interested in statistical and probabilistic methods to design and analyze scalable algorithms for discovering knowledge in large and rich datasets. We plan to cover the following topics, among others.

Informations & Wissensmanagement, Big Data Computing,    Statistik, Spieltheorie

MODE (for Machine-learning Optimized Design of Experiments) is a collaboration of physicists and computer scientists who target the use of differentiable programming in design optimization of detectors for particle physics applications, extending from fundamental research at accelerators, in space, and in nuclear physics and neutrino facilities, to industrial applications employing the technology of radiation detection. Our aim to develop modular, customizable, and scalable, fully differentiable pipelines for the end-to-end optimization of articulated objective functions that model in full the true goals of experimental particle physics endeavours, to ensure optimal detector performance, analysis potential, and cost-effectiveness.

Computerphysik und Numerische Simulation,    Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen

The Hausdorff Research Institute for Mathematics (HIM)

The last decade has seen an explosion of available data. To analyze the enormous amount of accrued data, classical algorithms are often not appropriate: the data may not be accessible in its entirety (but perhaps only in an aggregate form) and computations may need to be distributed or done in an online setting. The data often comes with an additional geometric flavor, either from applications or from mapping the data to feature spaces. The workshop will focus on three classes of algorithms for dealing with massive geometric data, namely, streaming, distributed, and sublinear algorithms. The aim of the workshop to identify and explore new research directions at the interface of massive data models and computational geometry, through fruitful discussions leading to potential collaborations.

The CHEP conference series addresses the computing, networking and software issues for the world’s leading data‐intensive science experiments that currently analyse hundreds of petabytes of data using worldwide computing resources.

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.

Kernenergie,    Hochleistungsrechner, Parallelrechner

Commissariat à l’Energie Atomique et aux énergies alternatives (CEA, France)

In addition to promoting the advances in the traditional areas of interest for this conference, encompassing Monte Carlo simulation and high-performance computing (HPC) methods for nuclear applications, SNA + MC 2024 will also cover novel topics related to emerging scientific and technological challenges, in particular climate and environmental issues, and energy transition.

Kernenergie,    Hochleistungsrechner, Parallelrechner

Society for Industrial and Applied Mathematics (SIAM)

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

Kurse und Veranstaltungen für Studenten der Mathematik,    Infinitesimalrechnung, Differentialgleichungen, Integralrechnung

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The vision of NFDI4DS is to support all steps of the complex and interdisciplinary research data lifecycle in Data Science and Artificial Intelligence. The overarching objective of NFDI4DS is the development, establishment, and sustainment of a national research data infrastructure for the Data Science and Artificial Intelligence community. The key idea is to work towards increasing the transparency, reproducibility and fairness of Data Science and Artificial Intelligence projects, by making all digital artifacts available, interlinking them, and offering innovative tools and services. Within this Dagstuhl Research Meeting, the NFDI4DS partners will reflect on their progress so far, and will work on their long-term strategy.

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

The purpose is to maintain ISNET's mission of bringing the nuclear physics and statistical sciences communities together to report on the latest progress and to provide a vehicle for educating and enlightening the nuclear physics community in regard to the application of statistical methodologies that enable nuclear physics to reach more quantitatively rigorous scientific conclusions.

Uncertainty quantification and statistical analysis, Emulators and optimizations, Model mixing and data mining, Machine learning, Bayesian inference, Statistics techniques in nuclear experiments, New frontiers of nuclear physics

Statistik, Spieltheorie ,    Fachübergreifende Veranstaltungen für Physiker

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

In modern computational systems, the need to handle large-scale inputs imposes interesting computational challenges. Two such challenges are (1) the need to distribute the computation over multiple units, and (2) the dynamic nature of the input, which may undergo changes over time. A particular class of problems studied in these settings is when the input to the computational task is a huge graph. The field of dynamic graph algorithms addresses efficiently processing edge/vertex insertions/deletions in the input graph. In distributed graph algorithms, the input resides across multiple machines, and the goal is to solve the problem while minimizing the number of rounds of communication. Both of these rich research areas have been extensively studied since at least the 1980’s. We know of efficient algorithms for a large variety of tasks, such as shortest paths problems, coloring, subgraph finding, symmetry breaking, approximations, and many more. However, there are still fundamental problems with no known efficient solutions in some of these models, and even more where the exact complexity of computation is yet to be determined. In the recent years, a number of influential works show how transferring ideas from one of these models to the other provides progress on some of the long-lasting open problems. The goal of this Dagstuhl Seminar is to build a bridge between the two research communities of dynamic graph algorithms and distributed computing, by working together on joint research frontiers.

Hochleistungsrechner, Parallelrechner,    Edge/Cloud-Computing

Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste, Italy

Softwareentwicklung,    Computerprogramme

South American Institute for Fundamental Research (ICTP-SAIFR)

Kurse und Veranstaltungen für Physikstudenten,    Neuronale Netze und Künstliche Intelligenz, Maschinelles Lernen

Institute of Mathematical Statistics (IMS)

The objective of ICSDS is to bring together researchers in statistics and data science from academia, industry, and government in a stimulating setting to exchange ideas on the developments of modern statistics, machine learning, and broadly defined theory, methods, and applications in data science. There will be a student paper award competition, in addition to plenary sessions, and invited, contributed and poster sessions. Young researchers are particularly encouraged to participate, as a portion of the invited sessions will be designated for young researchers.

Statistik, Spieltheorie ,    Informations & Wissensmanagement, Big Data Computing

Institute for Computational and Experimental Research in Mathematics (ICERM)

In recent years, new opportunities have emerged for pattern detection and identification in applications using data-scientific approaches. These applications include spiral waves in cardiac dynamics, vegetation patterns in arid ecosystems, and cell organization in biological tissues. The aim of this workshop is to bring together researchers at the interface of these diverse aspects of pattern-formation theory, computation, and applications in order to share ideas and identify new challenges and open problems. The scientific program will focus on the use of dynamical systems methods in the study of pattern formation, as well as the integration of dynamics, data, and computation.

ICERM;     Email: info@icerm.brown.edu

dynamical systems, data science, pattern formation

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

Simons Laufer Mathematical Sciences Institute (SLMath)

In a growing number of applications, one needs to analyze and interpret data coming from massive networks. The statistical problems arising from such applications lead to important mathematical challenges: building novel probabilistic models, understanding the possibilities and limitations for statistical detection and inference, designing efficient algorithms, and understanding the inherent limitations of fast algorithms. The workshop will bring together leading researchers in combinatorial statistics, machine learning, and random graphs in the hope of cross-fertilization of ideas.

combinatorial statistics, random graphs, network inference, network reconstruction, detection, estimation

Komplexe Netzwerke,    Graphentheorie und Kombinatorik