AI Algorithms and Methods

AI Algorithms and Methods are at the heart of Artificial Intelligence research and have dramatically impacted Data Science. The confluence of compute power, massive databases, and efficient algorithms to implement AI methods have impacted applications from engineering to the life sciences to social sciences. In spite of the success of AI across a wide range of applications, there remain many open problems and challenges. Interpretability, reliability, and uncertainty quantification in AI are requirements for many sensitive societal applications from biomedicine to decisions made by financial entities.

Application Areas

Research Focus

Much of the modern work in AI Algorithms and Methods has not developed models that can be easily interpreted, cannot be manipulated via adversarial algorithms, and capture the uncertainty in their predictions. A major focus of our research will be to develop novel scalable algorithms and methods to develop AI that is interpretable, reliable, and captures uncertainty. Another challenge we address is that many algorithms that quantify uncertainty and have reliability guarantees for moderately sized data do not scale to massive data, the computational cost is too high. Using ideas from modern AI, numerical methods, databases, and data structures we are developing innovative solutions that allow us to scale algorithms that work well for moderate size data to massive data.

We are also very interested in the societal implications of AI and the influence of society on AI algorithms. Our research includes ethical and fairness aspects of AI Algorithms. A related problem that is an active area of research is the issue of preserving privacy. For many applications, we may want to preserve privacy but often there is a cost to the accuracy of predictions when privacy is preserved, we are very interested in this tradeoff and understanding when there is a tradeoff.

A recent application domain of AI has been to inverse problems and physics-based modeling. An inverse problem is to infer a data-generating model from a set of observations: for example, calculating an image in X-ray computed tomography, source reconstruction in acoustics, or calculating the density of the Earth from measurements of its gravity field. Inverse problems are an active area of research with applications ranging from climate change to monitoring manufacturing processes.


We want the developed AI Algorithms to be used widely. In this context, we team up with researchers that have a real need for a novel methodology to enable novel cutting-edge science. We have several partnerships with the Universitätsklinikum Leipzig and the Fraunhofer Institute for Intelligent Analysis and Information Systems (FhG IAIS) being two of the more significant ones.

Portrait of Prof. Dr. Sayan Mukherjee

Prof. Dr. Sayan Mukherjee

Principal Investigator

Leipzig University, Duke University

Find out more about our research areas.