Accelerating computationally-driven scientific discovery

From stochastic modeling of natural phenomena to intelligent cloud services for parameter space exploration and model inference, we find new ways to support scientific discovery by integrating large-scale simulation, scalable data analysis and artificial intelligence.

Modeling & stochastic simulation

A core approach is to model and simulate complex systems using stochastic descriptions. Stochastic chemical kinetics, agent-based models and Kinetic Monte Carlo are specialities.

Read More

Machine Learning & Optimization

Applied machine learning and optimization are at the core of our toolbox for constructing intelligent scientific software to probe natural phenomena, and to develop models from data.

Read More

Cloud computing & data engineering

Our research range from development of new ways to manage large and fast data to cloud native solutions for highly scalable interactive simulation workflows.

Read More

Computational Systems Biology

A prominent application area in the lab is computational systems biology. Systems biology is an interdiciplinary field where mathematical modling, simulation and advanced analytics are combined with cell biology to model and understand for example gene regulation on a system level. This is challenging from a simulation point of view since models often need to take into account molecular interactions and movement on a subcellular scale as well as cell-cell interactions between millions of cells. We develop both multiscale models to simulate such systems, and new smart methods and software that bridge simulation and artificial intelligence to disentangle the complex interactions that lead to qualitatively .

News

2 open positions in my lab: PhD student and Lecturer; focus on AI/machine learning on large-scale data #pharmbio https://t.co/RokZKm3c3A

It’s so important to be honest about the fact in academia, rejection β€” from grants to papers to jobs β€” is just part of the process. Like anyone in academia, I have dealt with my fair share of rejections. 1/7 https://t.co/qhszL8gtFM

Our latest work on using human-in-the loop ML to explore high-dimensional parameter spaces for stochastic biochemical models is now out https://t.co/XzBj1Xakj7 Stay tuned for more smart tools like this in next-generation #StochSS @FredrikWrede @UCSBengineering @UU_University

Join us in the @strategiskaSSF project HASTE! https://t.co/cd74JmffPA @UU_University

Load More...