In various disciplines such as biology, physics, medicine, economics, chemistry, and geology, simulation and/or data analysis could be used to explore different processes. Simulating something means that instead of carrying out real-world experiments, the experiments take place virtually in a computer. Simulation is known as the third pillar of science (beside experiments and theory), and has revolutionised what you can study without expensive, bulky, and perhaps unethical experiments.
Relatedly, in order to truly realise the power of simulation models for deep analysis of phenomena or answering what-if scenarios, the study of optimisation methods is indispensable. Our research spans various aspects of simulation-driven optimisation, as well as simulation-based optimisation. In particular, the questions of scalability and data, computation-efficiency are of great importance within our research context.
Data analysis is a field within data science, and is a growing research area due to the very large amounts of data that are being collected from social media, mobile devices, sensors, patients etc. The methods that are used for data analysis are often, but not always connected with artificial intelligence. Our research activities herein span federated machine learning, large-scale data processing, cloud computing, cybersecurity of large-scale infrastructures, etc.
At the Department of Information Technology, we conduct research in the entire chain of what is needed to perform simulations and data analysis; to mathematically describe the phenomenon under investigation, to formulate a solution method to the mathematical problem, and finally to construct computer programs that efficiently implement the developed solution method to enable the simulation.
- Computational Science and Engineering (CSE): We construct computational methods for specific contexts such as glaciology or biomechanics.
- Numerical analysis (NA): We analyse and develop efficient numerical methods.
- Optimization: PDE-constrained optimisation, data-driven black box optimisation (Bayesian and surrogate-based methods, evolutionary optimisation), gradient-based methods.
- Machine learning and Bayesian statistics: deep learning, statistical sampling, design of experiments, likelihood-free parameter inference, time series analysis, expectation-maximisation.
- Computational Biology: deep learning for genomics, parameter inference of stochastic biochemical reaction networks, model exploration, stochastic simulation methods.
- Martin Almquist
- Torsten Andersson
- Doghonay Arjmand
- Sven-Erik Ekström
- Stefan Engblom
- Andreas Hellander
- Hans Karlsson
- Gunilla Kreiss
- Elisabeth Larsson
- Ken Mattsson
- Sandra May
- Murtazo Nazarov
- Carl Nettelblad
- Maya Neytcheva
- Anna Persson
- Stefan Pålsson
- Jarmo Rantakokko
- Emanuel Rubensson
- Prashant Singh
- Salman Toor
- Lina von Sydow
- The Göran Gustafsson award for young researchers was received by Elisabeth Larsson in 2007 and by Andreas Hellander in 2016.
- 1TD342: Introduction to Scientific Computing
- 1TD352: Scientific Computing for Data Analysis
- 1TD354: Scientific Computing for Partial Differential Equations
- 1TD350: Advanced Numerical Methods
- 1TD056: Applied Finite Element Methods
- 1TD186: Computational Finance: Pricing and Valuation