Conferences  >  Informatics  >  Scientific Computing and Data Science

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

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

California Institute of Technology (Caltech)

This annual workshop welcomes academics and industry practitioners at the interface of theoretical physics, mathematics, and computer science to discuss research directions centred on the application of machine learning and data science to open problems in fundamental theory, and equivalently the application of techniques from theory to describe the successes of machine learning.

CARE College of Engineering, Tamil Nadu, India

In this digital era, Internet of Things [IoT], big data and computer networks are dedicated to provide new innovative contributions focused towards the development and deployment of wide range of intelligent and networked information systems. Smart IoT and big data platform allows the networked devices to communicate along with the core values of volume, variety and velocity which in turn leads to the integration of Big data and IoT with advanced machine learning and other advanced techniques. This International Conference on Computer networks, Big Data and IoT [ICCBI 2023] will provide an interdisciplinary platform to bring together scientists, researchers and academicians to present and exchange the ideas of the latest research works and results related to IoT, big data and computer networking topics.

Phone: [9894161843];     Email: icocbi.conf@gmail.com

Internet of Things AI and Machine Learning for the IoT Energy Efficiency and Sustainability in IoT Smart Sensing, Signal Processing and Analytics Human-Machine Interaction and IoT M2M/IoT Services over Distributed Networks Cloud/Fog/Edge-enabled Big Data Intelligence over IoT 6G-Enabled Internet of Things Security, Privacy, Intrusion Detection for the Internet of Things Big Data Multi-Modal Perceptual Big Data Analysis Intelligent Data Mining Machine Learning on Big Data Data Science and Scalable Computing Predictive Analytics and Simulation Big Data as a Service (BDaaS) Platform and Technologies Big Data System Reliability, Dependability, and Availability Knowledge Extraction, Discovery, and Analysis Security and Privacy-Aware Data Management Cloud Computing Cloud-centric Network Architectures Social and Mobile Cloud Applications Cloud Service Adaptation and Automation Fault Tolerance, Availability, and Reliability Distributed Ledger Technologies to Cloud Sustainable Cloud Computing Hybrid-clouds/Multi-clouds/Terminal-Edge-Cloud Integration Cloud Computing Virtualization

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.

CIRM – Centre International de Rencontres Mathématiques

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

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Many big data sets in various application domains have complex relationships, which can be modelled as graphs, consisting of entities and relationships between them. Consequently, graphs are extensively studied in both Mathematics and Computer science. In particular, planar graphs, which can be drawn without edge crossings in the plane, form a distinguished role in Graph Theory and Graph Algorithms. Many structural properties of planar graphs are investigated, in terms of excluded minors, low density, and small separators, which lead to efficient algorithms for planar graphs. Consequently, fundamental algorithms for planar graphs have been discovered. However, most real-world graphs, such as social networks and biological networks, are nonplanar. For example, the scale-free networks, which are used to model web graphs, social networks and biological networks, are globally sparse nonplanar graphs, with locally dense clusters and low diameters. To understand such real-world networks, we need to solve fundamental mathematical and algorithmic research questions on beyond-planar graphs, which generalize the notion of planar graphs, in terms of topological constraints or forbidden edge crossing patterns.

Graph Theory and Combinatorics,    Information & Knowledge Management, Big Data Computing

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The global fight against the COVID-19 pandemic has presented massive challenges and real opportunities for data science and visualization research and technology. Epidemiologists, virologists, infection control experts and public health officials are required to make sense of information from a huge variety of data sources. Physicians and medical researchers have access to rapidly changing medical data as the pandemic evolves. Political stakeholders (supported by economists, public health officials, and other experts analyzing the data) make decisions on which action to take, balancing the impact on public health with social and economic impacts. The general public is regularly presented with visualization, through news, conventional and social media, and government publications, and these inform their actions for the next days and weeks.

The main objective of this advanced school is to train young researchers in biology, biophysics, pharmacology, etc. to the use of X-ray (XR) and neutron scattering structural techniques (crystallography, small-angle scattering, reflectometry), ranging from experiments to complete and in-depth analysis of the data, based on mathematical and modeling approaches (e.g. analytical fits, ab initio modeling, artificial intelligence, Bayesian and multimodal methods). The complementarity of these scattering techniques with other structural biology methods, such as NMR, will be considered as well.

Applied Physics (general),    Courses and Events for Physics Students

The Alan Turing Institute

Hosted by The Alan Turing Institute, AI UK 2024 will be an in-depth exploration of how data science and AI can be used to solve real-world challenges. Our diverse programme has been thematically structured around the latest innovations from across the AI ecosystem. With a broad range of interactive content, expect to hear the latest thinking on foundational AI, digital twins, algorithmic bias, AI ethics – and much more.

Neural Networks and Artificial Intelligence, Machine Learning,    History and Philosophy of Science and Technology

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

This 10-day workshop consists of lectures blended with hands-on sessions designed to introduce users to or advance skills in materials modelling with WIEN2k (a solid-state density-functional theory package). The event targets graduate students, early-career scientists, experienced users. The WIEN2k code is one of the most accurate density functional theory (DFT) codes for solids and can be applied to all elements of the periodic table; it is currently the most popular all-electron DFT code for solids. Many materials characteristics can be calculated, ranging from the basic ones, such as the electronic band structure or the optimized atomic structure, to more specialized ones such as the nuclear magnetic resonance shielding tensor, the electric polarization, band topology. This event includes lectures that provide basic foundation, scientific talks that highlight illustrative examples on application of DFT to solving specific materials problems, and hands-on tutorials designed at the basic and advanced levels.

Condensed Matter Physics and Materials,    Computational Physics and Numerical Simulation

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

There is currently an insufficient transfer of state-of-the-art research knowledge from computer science to research software engineering, and vice versa, in part leading to an incomplete understanding in computer science of the domain-specific and general challenges in research software engineering. This interactive seminar therefore brings the computer science and software engineering research community and the research software engineering community together to define a common language, and apply it to improve reciprocal knowledge transfer.

Software Engineering,    Computational Physics and Numerical Simulation

Open Data Science Conference

The ODSC East in-person conference will feature 200+ speakers, instructors, and practitioners, 3,000 of your fellow data scientists, and expert-led, hands-on instruction in in-demand topics and tools, previous included:

NLP | Python | Responsible AI | TensorFlow | PyTorch | Conversational AI | OpenAI | Cybersecurity | Data Visualization | scikit-learn | MLOps | Data Engineering | Machine Learning | and much more

Email: iryna@odsc.com

Data Science, Artificial Intelligence

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Metabolomic data, usually from mass spectrometry or nuclear magnetic resonance spectroscopy, is highly complex and information rich. With continuing advances in metabolite detection technologies, the volume and complexity of data is ever increasing. Computational analysis of metabolomic data, from raw data processing to biological interpretation, is therefore fundamental to realizing the impact of metabolomics on a diverse array of application fields, e.g., environmental toxicity, industrial biotechnology, and biomedicine. This Dagstuhl Seminar extends the Computational Metabolomics series to examine how we can enhance the utility and interpretation of metabolomics data.

Application Software,    Algorithms and Data Structures

Institute for Mathematical and Statistical Innovation

Materials informatics is an emerging field defined by the use of simulation tools combined with methods from data sciences and machine learning to better understand materials properties and design innovative materials. The models which are considered cover an extremely wide range, from Schrödinger’s equation, which describes matter at the (sub)atomistic scale, to the equations of continuum mechanics. Mathematical sciences play a key role in materials informatics, both to construct the databases used to train machine learning algorithms (since these databases are made of reference simulation results), and to harness them in order to extract the most relevant information. The aim of this program is bring together a diverse scientific audience, both between scientific fields (physical sciences, materials sciences, biophysics, etc) and within mathematics (mathematical modeling, numerical analysis, statistics and data analysis, etc), to make progress on key questions of materials informatics.

Condensed Matter Physics and Materials,    Metallurgy and Materials Science

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

Courses and Events for Math Students and Early Career Researchers,    Complex Networks

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)

The International Centre for Mathematical Sciences (ICMS)

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. 

Organized by AIDIC, The Italian Association of Chemical Engineering

Society for Industrial and Applied Mathematics (SIAM)

This conference is co-located with the SIAM Conference on Mathematics of Planet Earth (MPE24). This is the conference of the SIAM Activity Group on Life Sciences. The life sciences have become increasingly quantitative as new technologies facilitate collection and analysis of vast amounts of data ranging from complete genomic sequences of organisms to satellite imagery of forest landscapes on continental scales. As a consequence, mathematics and computational science have become crucial technologies for the study of complex models of biological processes.

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

Courses and Events for Math Students and Early Career Researchers,    History and Philosophy of Science and Technology

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

Courses and Events for Physics Students,    Events for IT Students and Early Career Researchers

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

Ecosystems, Environment and Sustainable Development,    Modeling and Simulation

Casa Matemática Oaxaca (CMO)

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

Condensed Matter Physics and Materials,    High Performance Computing, Supercomputing, Parallel Computing

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.

Pattern Recognition and Image Processing, Machine Vision,    Calculus, Differential Equations and Integration

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.

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

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

Society for Industrial and Applied Mathematics (SIAM)

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

Courses and Events for Math Students and Early Career Researchers,    Calculus, Differential Equations and Integration

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.

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

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.

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

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

Software Engineering,    Application Software

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

Complex Networks,    Graph Theory and Combinatorics