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Prof. Dr. Sayan Mukherjee

More information on Prof. Dr. Sayan Mukherjee is available on the website of the Alexander von Humboldt-Stiftung.

Department of Statistical Learning

The Department of Statistical Learning is located at the ScaDS.AI Dresden/Leipzig location at Leipzig University and the Max Planck Institute for Mathematics in the Sciences (MPI-MiS). Applied research and collaborations in biomedicine are mediated via the Interdisciplinary Centre for Bioinformatics (IZBI)  The department is directed by Prof. Dr. Sayan Mukherjee, Alexander von Humboldt Professor in Aritificial Intelligence. The research foci of the Department of Statistical Learning are:

  • methodological innovations for inference and learning
  • the mathematical foundations of inference and learning, and
  • applications to biology, biomedicine, and clinical challenges.


Methodological innovations for inference and learning

  • Integrating modern AI with stochastic modeling: Bayesian inference has been tremendously successful in a variety of applied problems across biological, social, and physical sciences. A key feature of Bayesian inference is a principled way of quantifying uncertainty in the inferential procedure. Recently modern AI methods such as deep neural networks have transformed predictive modeling, especially in the data-rich setting. We will integrate Bayesian (stochastic) modeling with modern AI (deep neural networks).
  • Inferential algorithms that scale: As more data is collected and as each observation contains more and richer measurements, classical inferential procedures fail to scale. The failure is especially problematic if there is a requirement to quantify uncertainty. The standard workhorse for Bayesian inference is Markov chain Monte Carlo (MCMC) and does not scale to massive data.

Mathematical foundations of inference and learning

  • The interface of geometry and topology with probability and statistics: There will be two themes driving this research. The first theme is importing ideas from modern geometry and topology to probabilistic modeling. The second theme is understanding the geometry and topology of random processes.
  • Inference and dynamics: There also will be two driving themes: Firstly, understanding the limits of inference and learning of dynamical systems. Secondly, developing inferential theory by considering inference as a dynamical system.

Machine learning and statistics for biomedical applications

  • Methods development: Often novel methodology is required to answer new questions or model new data types or modalities or to scale to modern genomic technologies. Methods have been developed across statistical genetics, quantitative genetics, cancer biology, molecular ecology, morphology, clinical decision making, and medical imaging.
  • Collaborative data analysis: Our group collaborates with clinicians and experimental biologists to address challenging and/or important problems.

More about the Department of Statistical Learning


The Department of Statistical Learning establishes a wide range of outreach activities. One of our team members, Dr Erika Roldan Roa has extensive experience in reaching out to the general public and thereby lowering the obstacles for getting in „real touch“ with mathematics and its related topics.

Outreach activities will include the Celebration of Mind event created in memory of Dr Martin Gardner.

funded by:
Gefördert vom Bundesministerium für Bildung und Forschung.
Gefördert vom Freistaat Sachsen.