Machine Learning

Machine learning investigates and develops methods that allow computers to infer or recognize patterns using datasets of various sizes, whether for exploratory purposes or to accomplish specific tasks. It has applications in numerous areas, from information systems and bioinformatics to computer vision, robotics, and security, among others.

Groups and Researchers in this Field


Data Systems

Laurent Bindschaedler is a Research Group Leader at the Max Planck Institute for Software Systems, where he leads the Data Systems Group (DSG). Focused on applications, his group explores a wide range of topics at the intersection of systems, data management, and machine learning, such as systems for big data and machine learning, machine learning for systems, real-time analytics systems, and decentralized systems like blockchains. Laurent is known for building the Chaos graph processing system, which holds a record for the largest graph processed on a small cluster of commodity servers. The Data Systems Group is dedicated to advancing the field of data systems by developing innovative methods, tools, and technologies to manage and analyze large-scale data sets, thereby empowering organizations and researchers to unlock the full potential of their data for innovation, improved decision-making, and complex problem-solving. Read more

Laurent Bindschaedler

MPI-SWS, Research Group Leader
Personal Website

Perceiving Systems

Michael J. Black is one of the founding directors of the Max Planck Institute for Intelligent Systems, where he leads the Perceiving Systems Department. His research addresses a variety of topics relating to computer vision and perception: the statistics of natural scenes and their motion; articulated human motion pose estimation and tracking; the estimation of human body shape from images and video; the representation and detection of motion discontinuities; and the estimation of optical flow. His early work on optical flow has been widely used in Hollywood films. He also does research on neural engineering for brain-machine interfaces and neural prostheses. He is an honorary professor at the University of Tübingen, visiting professor at ETH Zürich, and adjunct professor (research) at Brown University. Read more

Michael J. Black

MPI-IS, Scientific Director
Personal Website

Machine Learning and Systems Biology

Karsten Borgwardt is a director at the MPI for Biochemistry. His research focuses on the fields of bioinformatics, biomarker discovery and personalized medicine. In the Machine Learning and Systems Biology department, big data analysis and biomedical research meet: They develop novel data mining algorithms to detect patterns and statistical dependencies in large datasets from biology and medicine. The group is working towards two central goals: To enable the automatic generation of new knowledge from big data through machine learning, and to gain an understanding of the relationship between the function of biological systems and their molecular properties. Read more

Karsten Borgwardt

MPI for Biochemistry, Scientific Director
Personal Website

Robust Machine Learning

Wieland leads the Robust Machine Learning group at the Max Planck Institute for Intelligent Systems. In the past few years, deep neural networks have surpassed human performance on a range of complex cognitive tasks. However, unlike humans, these models can be derailed by almost imperceptible perturbations, often fail to generalise beyond the training data and require large amounts of data to learn novel tasks. The core reason for this behaviour is shortcut learning, i.e. the tendency of neural networks to pick up statistical signatures sufficient to solve a given task instead of learning the underlying causal structures and mechanisms in the data. Our research ties together adversarial machine learning, disentanglement, interpretability, self-supervised learning, and theoretical frameworks like nonlinear Independent Component Analysis to develop theoretically grounded yet empirically successful visual representation learning techniques that can uncover the underlying structure of our visual world and close the gap between human and machine vision. Read more

Wieland Brendel

MPI-IS, Research Group Leader
Personal Website

Physics for Inference and Optimization

Caterina de Bacco is an Independent Research Group Leader at the Max Planck Institute for Intelligent Systems in Tübingen. She is interested in understanding, optimizing and predicting relations between the microscopic and macroscopic properties of complex large-scale interacting systems. She likes to approach research by addressing application-oriented problems involving domain experts from different disciplines via developing models and algorithms derived from statistical physics principles. She studies large interacting systems following two main research directions---inference on networks and routing optimization on networks. Read more

Caterina de Bacco

MPI-IS, Research Group Leader
Personal Website

Human-Centric Machine Learning

Manuel Gomez Rodriguez is interested in developing machine learning and large-scale data mining methods for analysis and modeling of large real-world networks and processes that take place over them. His research comprises several dimensions: developing models of these networks and processes, assessing their theoretical properties and limitations; developing machine learning algorithms to fit the models and computational methods to influence processes over networks; and validating models and methods on gigabite- and terabyte-scale real-world datasets. Ultimately, he aims to provide computational tools with applications in a variety of domains, e.g. social and information sciences, economics, decision theory, causality, and epidemiology. Read more

Manuel Gomez Rodriguez

MPI-SWS, Faculty
Personal Website

Social Computing

Krishna Gummadi heads the Social Computing research group at the Max Planck Institute for Software Systems. He is broadly interested in understanding and building networked and distributed computer systems. Currently, the group's research focuses on social computing systems: an emerging class of societal-scale human-computer systems that facilitate interactions and knowledge exchange between individuals, organizations, and governments in our society. A few examples include social networking sites like Facebook, blogging and microblogging sites like LiveJournal and Twitter, and content sharing sites like YouTube, among many others. Through user studies, examining data, and building systems, the group aims to understand, predict, and control the behavior of their constituent human users and computer systems. Read more

Krishna Gummadi

MPI-SWS, Faculty
Personal Website

Social Foundations of Computation

Moritz Hardt is a scientific director at the Max Planck Institute for Intelligent Systems, where he leads the Social Foundations of Computation Department. His research contributes to the scientific foundations of machine learning and algorithmic decision making with a focus on social questions. His research interests span four areas: (1) Applying machine learning in social and economic contexts, (2) formulating social and dynamic actions as mathematical models, (3) examining the validity and reliability of statistical methods and the construction of datasets within scientific communities, and (4) the pursuit of normative goals, and in particular, how to formulate values and norms mathematically. Hardt is co-founder of the conference "Fairness, Accountability, and Transparency in Machine Learning." He is co-author of "Fairness and Machine Learning: Limitations and Opportunities" (MIT Press, 2022) and "Patterns, Predictions, and Actions: A Story About Machine Learning" (Princeton University Press, 2022). Read more

Moritz Hardt

MPI-IS, Scientific Director
Personal Website

Coordinative Intelligence

The research focus of Prof. Dr. Thomas Hofmann, who is a Max Planck Fellow at the MPI for Intelligent Systems, lies on deep learning – on its mathematical foundations as well as its applications. This includes contributions to optimization for machine learning, but also investigations into specific topics such as normalization and regularization techniques and understanding of generative models. Hofmann is one of the leading AI scientists in Europe, displaying a unique blend of theoretically principled yet often highly applicable research, and a track record for pioneering fields. At the MPI-IS in Tübingen, Hofmann leads a group on Coordinative Intelligence. The group interprets intelligence as a coordinative and communicative process. Read more