From June 25 to 28 2024, ScaDS.AI will be hosting the 10th International Summer School and is looking forward to welcoming students, PhD students, researchers, and practitioners to discuss selected topics in the field of AI and big data.
Date and Location
The 10th International Summer School on AI and Big Data will take place from 25.06 – 28.06.2024 in Leipzig.
Mekong in Gondwanaland
Pfaffendorfer Str. 29
The program for the 10th International Summer School focuses on five big topic areas that ScaDS.AI is also currently working on:
To give you the opportunity to delve deeper into these areas and focus on different aspects, we have organized the agenda thematically. The idea is that people who are interested in or working on these topics can spend more connected time together and exchange ideas.
This year the International Summer School starts on a Tuesday with various contributions to Generative AI. The topics Protein Engineering & AI and Earth and Environmental Science will run simultaneously on Wednesday. Thursday will give you input on Mathematical Foundations of AI. Every day, you will have the chance to get insights on topics in the area of Responsible AI.
The program will include time for networking, talking to other participants, and of course, asking questions of the speakers. Another option to get in touch with other participants of the Summer School 2024 will be our social program in the evenings. We are planning to take you on a dragon boat trip on the rivers and channels in Leipzig. If you prefer walking, you can also take part in a city tour.
To give you an idea of the topics of the individual areas, we summarized them in short abstracts below:
Earth and Environmental Sciences
The most pressing questions of our time involve a profound understanding and description of the impacts of climate change risks. These include extreme weather events, biodiversity loss and hazardous near-surface environmental processes. Artificial Intelligence (AI) and big data analytics are becoming increasingly important in this context. Most parts of the Earth system are continuously monitored by sensors. AI can cope with both the volume of data and the heterogeneous data characteristics. For instance, satellites, drones, and sensor networks monitor the atmosphere, land and ocean surfaces, including air, water, soils, rocks, and biodiversity or even the deep Earth’s interior, with unprecedented accuracy. Furthermore, citizen science projects collect data with smartphone apps and enrich our data archives. All in all, the key in studying the Earth system will be a combination of the latest AI-driven method developments with the untapped data resources.
During the Summer School, we will bridge state-of-the-art AI research and data challenges in Earth System Sciences. The focus will rest on the opportunities and methodological advances arising in this broad context from different perspectives and considering multiple environmental facets.
Protein Engineering and AI
The field of applied life sciences is seeing continuous advancements and innovations in areas like drug discovery, diagnostics and biotechnology. Artificial intelligence (AI) is increasingly applied in these areas and can become a game-changer that will revolutionize research and development in the life sciences. For instance, AI algorithms are speeding up the discovery of new drugs by analyzing vast amounts of chemical and biological data. This expands the coverage of the chemical space by several orders of magnitude compared to in-house chemical compound collections.
Also, the design of tailor-made proteins for applications in medicine or biotechnology, which has been science fiction for many years, is now gaining momentum through the use of AI. Recent years have witnessed the uncovering of the protein universe by tools like AlphaFold and ESMFold as well as the opening of so far untapped chemical and protein spaces with the help of generative AI models. Ongoing integration of AI approaches will continue moving the life sciences field forward. It offers new opportunities for researchers and speeding up many steps of the discovery process.
During the Summer School, state-of-the-art AI methods for addressing life sciences challenges will be presented, covering the underlying algorithms as well as targeted applications. A number of expert speakers will present cutting-edge AI-driven drug discovery and protein design research highlights.
Generative AI is a field of artificial intelligence that empowers machines to create new, original content, whether it’s images, text, music, or even entire narratives. It has therefore driven many fields in the recent past, from science and industries even towards education and general societal movements. These AI models, often known as generative models, can analyze patterns and structures in existing data and use that knowledge to produce novel and creative outputs. They have wide-ranging applications, from generating realistic artwork to composing music and enhancing natural language understanding. Generative AI is at the forefront of innovation, enabling machines to mimic and, in some cases, surpass human creativity. We want to present at our summer school these recent developments in contributions from the scientific community and discuss future developments as well as implication to science and society in general.
The advancement of technical developments in the field of AI is not without consequences for society and individuals. For this reason, the area of “Responsible AI” is a central concern that must also be considered within the technical side of AI. Not only the question of biases, but also that of the epistemic prerequisites for mapping sections of our world in AI systems, is negotiated within the framework of Responsible AI.
At this year’s Summer School, there will be a thematic unit each day that will handle these questions and offer space for exchange.
Mathematical Foundations of AI
The last day of the summer school is dedicated to the mathematical foundations of Artificial Intelligence.
Various branches of mathematics (from linear algebra, calculus, and statistics to differential geometry, logic, and even category theory) play a crucial role in the development of artificial intelligence algorithms. A solid understanding of these principles not only helps in the development and implementation of models. It also enables practitioners to make informed decisions about model architecture and training procedures.
Our diverse lineup of presentations from researchers and industry professionals in the field is designed to provide insight into the application of mathematical principles in AI. It will expose participants to real-world applications to enhance the relevance of the learning experience.
One of our objectives is to provide opportunities to connect with fellow participants, instructors, and professionals in the field and establish networks that can be valuable for future collaborations and learning opportunities.