Call for Papers - Scientific Track (IEEE technically co-sponsored)

Data Science stands on the frontline of new scientific discoveries and plays a significant role in all aspects of our everyday lives. The 9th Swiss Conference on Data Science - SDS2022 - brings together researchers, developers, opinion leaders, decision makers, and innovators with expertise in Data Science. The goal of SDS2022 is to foster the exchange of novel ideas for data science.

Topics of interest

We invite submissions of original research papers related to all areas of Data Science, especially best practice and use-case oriented research work with technical depth and practical applicability. We especially want to solicit work in four key areas in the coming year, namely:

• AI for Cybersecurity and Data Protection

Data collection and Artificial Intelligence (AI) algorithms are becoming the cornerstone of the cybersecurity industry. And at the same time we have to think about how we can ensure the security requirements of critical applications and the safety and confidentiality of data communication and processing.

• AI for Humanity

We already observe that AI-based innovations are powering some of the most cutting-edge solutions we use in our daily lives and in almost every field of industry. It is therefore only natural to use AI also to solve the challenges of humanity such as how to tackle climate change, health challenges (e.g., pandemics), the future of food, energy and transportation and other social good use cases.

• Developing, deploying and operating machine learning-based systems (MLOps)

Data Science is more than creating prototypes. MLOps focuses on managing and automating machine learning pipelines and therefore creating real and sustainable business applications. We will address the whole development-operations-life-cycle including pipeline maintenance, data collection & access, infrastructure, operational aspects, delivery to production and many more.

• Implementing socially-acceptable data-based applications

A wide range of Data Science solutions are in place. However, there are big differences between the technical implementation and the social perception of these solutions. Therefore, we want to understand deeper how to build socially accepted data-based applications.

Call for Participation - Business Track

Data Science stands on the frontline of new scientific discoveries and plays a significant role in all aspects of our everyday lives. The 9th Swiss Conference on Data Science - SDS2022 - brings together researchers, developers, opinion leaders, decision makers, and innovators with expertise in Data Science. The goal of SDS2022 is to foster the exchange of novel ideas for Data Science. Following the motto „together we move faster“, we also foster the expansion of the community and network of data scientists and engineers from academia and industry.

Topics of interest

We invite submissions of talks related to all aspects of Data Science, especially best practice and use-case oriented work with technical depth and practical applicability. We especially want to solicit work in four key areas in the coming year, namely:

• AI for Cybersecurity and Data Protection

Data collection and Artificial Intelligence (AI) algorithms are becoming the cornerstone of the cybersecurity industry. And at the same time we have to think about how we can ensure the security requirements of critical applications and the safety and confidentiality of data communication and processing.

• AI for Humanity

We already observe that AI-based innovations are powering some of the most cutting-edge solutions we use in our daily lives and in almost every field of industry. It is therefore only natural to use AI also to solve the challenges of humanity such as how to tackle climate change, health challenges (e.g., pandemics), the future of food, energy and transportation and other social good use cases.

• Developing, deploying and operating machine learning-based systems (MLOps)

Data Science is more than creating prototypes. MLOps focuses on managing and automating machine learning pipelines and therefore creating real and sustainable business applications. We will address the whole development-operations-life-cycle including pipeline maintenance, data collection & access, infrastructure, operational aspects, delivery to production and many more.

• Implementing socially-acceptable data-based applications

A wide range of Data Science solutions are in place. However, there are big differences between the technical implementation and the social perception of these solutions. Therefore, we want to understand deeper how to build socially accepted data-based applications.