The Data Science and AI division engages in research and education within a growing area encompassing data science, AI, and machine learning and their applications. With an increased demand for advanced information systems, computer applications, and autonomous decision-making in nearly all areas of society, data science and AI are increasingly becoming necessary aspects of research, education, industry, and society.
What are data science, AI, and machine learning?
Data science is concerned with the extraction of useful knowledge from large-scale data, both for a deeper understanding of the data and for decision support.
Artificial intelligence, or AI, is concerned with building intelligent systems based on machine learning, logic, or other models. It is a highly cross-disciplinary field, using methods from statistics and optimization, machine learning, algorithms (e.g., for inference and for handling large-scale data), and various other computational domains to build systems or machines to perform tasks typically requiring human intelligence.
Machine learning, or ML, is a subfield of AI and focuses on the development of algorithms and models that can “learn” from data without being explicitly programmed. Through capturing the underlying information in the data, ML models can learn to perform complex tasks without explicitly programmed rules. Relative to AI, ML focuses more on foundations in algorithms, optimization, and mathematical statistics.
Research
Most of the research at Data Science and AI is conducted in cooperation with partners in academia, industry, public, and cultural sectors; and has many synergies with research in following machine learning and AI related areas:
- algorithm theory (design, complexity, randomness)
- optimization and operations research (scheduling, routing)
- machine learning research
- health informatics, bioinformatics and computational biology
- AI for scientific applications (i.e., physics, chemistry)
- natural language processing (text analysis, representation learning, multimodality)
- autonomous vehicles
- mathematical modelling and problem solving
- computational arts, music, and games
- neuro-symbolic AI
You will find more information about our research and research groups under each page:
- Healthy AI lab
- AI Laboratory for Molecular Engineering
- AI and Machine Learning in the Natural Sciences
- NLP @ DSAI
- Global AI lab
- Machine Learning and Decision Making Lab
- AI in Computational Arts, Music, and Games
- King lab
- Neuro-Symbolic AI
Centers and Collaborations
Several research projects are supported by:
- WASP (Wallenberg AI, Autonomous Systems and Software Programme)
- WASP-HS
- CHAIR (Chalmers AI research initiative)
- Swedish Research Council
DSAI seminars
Learn more about the research areas of the DSAI division in the weekly DSAI seminars. The seminars are open to the public with research talks organized by researchers at the division and are advertised here.
The seminars are held every Monday at 2 pm usually in Analysen, EDIT-building, and via Zoom (password: mondays23). Video recordings of previous lectures of the seminar can be accessed via the "DSAI Seminars" channel on the Chalmers Play website.
Organizers are Alexander Gower and Richard Beckmann. Feel free to reach out if you have any questions or ideas related to the seminars. You can also contact them if you wish to subscribe to email updates on the seminars.
Teaching
The division provides a range of courses in data science, AI, and machine learning. We also offer courses in mathematical modeling, algorithms, optimization, and related fields. We do a substantial part of the teaching in the Data Science and AI Master’s program at Chalmers and the Applied Data Science Master’s program at the University of Gothenburg. Full course lists are available on the masters program pages. Additionally, we provide the following introductory courses to all students:
- Introduction to Data Science and AI (GU, Chalmers)
- Applied Machine Learning (GU, Chalmers)
- Machine Learning and AI through Artistic Innovation (TRA385)
- On the PhD level, we offer courses in the WASP AI Graduate School.
