Artificial intelligence (AI) research seeks to create advanced systems that can perform complex tasks requiring human-level cognition and reasoning. Machine learning (ML), a key technique in modern AI, provides algorithms and models that allow computers to learn patterns from data. Modern ML encompasses methods ranging from classical statistical learning to deep neural networks, reinforcement learning, and generative modeling. AI and ML have applications in numerous areas, from natural language processing, information systems and bioinformatics to computer vision, robotics, and security, among others.
Groups and Researchers in this Field
Algorithms & Inequality
Rediet Abebe is a junior fellow at the Harvard Society of Fellows and an Andrew Carnegie Fellow. Her research examines the interaction of algorithms and inequality, with a focus on contributing to the scientific foundations of this area. Abebe has also co-founded numerous organizations, including the ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO), and the associated international research initiative. Abebe is the recipient of numerous awards and honours, including the Hector Endowed Fellowship by the European Laboratory for Learning and Intelligent Systems (ELLIS), MIT Technology Fellows 35 Innovators under 35, the ACM SIGKDD Dissertation Award, and an honorable mention for the ACM SIGecom Dissertation Award. Abebe is currently leading several large-scale evaluations of ML systems used in commercial, legal, and policy contexts. Read more
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
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
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
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
Meeyoung Cha is a scientific director of MPI-SP in Bochum, Germany. Her interests include data science and computational social science, with a focus on understanding social information and human-machine interactions. Meeyoung’s research on misinformation, poverty mapping, fraud detection, and long-tail content has received wide citations and best paper awards. She is the recipient of the Korean Young Information Scientist Award 2019, the AAAI ICWSM Test-of-Time! Award 2020, and the ACM IMC Test-of-Time Award 2022. Prior to joining MPI, Meeyoung was a chief investigator at IBS (2019-current), a faculty member at KAIST (2010-current), a visiting professor at Facebook (2015-2016), and a postdoctoral researcher at MPI-SWS (2008-2010). She received her Ph.D. in computer science from KAIST in 2008. Read more
Peter Dayan is a Director of the Max Planck Institute for Biological Cybernetics. His research focuses on decision-making processes in the brain, the role of neuromodulators as well as neuronal malfunctions in psychiatric diseases. Dayan has long worked at the interface between natural and engineered systems for learning and choice, and is also regarded as a pioneer in the field of Artificial Intelligence. Read more
Peter Dayan
MPI for Biological Cybernetics, Scientific Director 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
Jonas Geiping leads a joint research group at the Max Planck Institute for Intelligent Systems and the ELLIS Institute Tübingen. His group is interested in questions of safety and efficiency in modern machine learning. There are a number of fundamental machine learning questions that come up in these topics that we still do not understand well. In safety, examples are questions about the principles of data poisoning, the subtleties of water-marking for generative models, privacy questions in federated learning, or adversarial attacks against large language models. Can we ever make these models “safe”, and how do we define this? Are there feasible technical solutions that reduce harm?
Further, the research group is interested in questions about the efficiency of modern AI systems, especially for large language models. How efficient can we make these systems, can we train strong models with little compute? Can we extend the capabilities of language models with recursive computation? How do efficiency modifications impact the safety of these models? Read more
Sustainable, Programmable and Intelligent Computing Systems
Christina's research interests lie at the intersection of computer architecture, computer systems, high-performance computing, and sustainable computing. Her current research focuses on the hardware/software co-design of emerging applications, particularly AI/ML, with modern computing systems. She designs solutions across the entire system stack, from software down to hardware—including algorithms, compilers, runtime systems, programming frameworks, and hardware engines—leveraging cutting-edge technologies such as processing-in-memory and disaggregation. Her work targets improvements in performance, scalability, programmability, and sustainability. Before joining MPI-SWS, Christina was a Postdoctoral Researcher at the University of Toronto. She received her Ph.D. from the School of Electrical and Computer Engineering (ECE) at the National Technical University of Athens (NTUA) in Greece. Read more
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
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
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
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
Thorsten Holz is a Scientific Director at MPI-SP. His research spans the full breadth of systems security, including the analysis, modeling, design, implementation, and validation of complex systems. In recent years, his work has focused on the systematic identification and mitigation of security vulnerabilities. Thorsten's research has been recognized with several awards, including the Heinz Maier-Leibnitz Prize from the DFG in 2011, an ERC Starting Grant in 2014, and an ERC Consolidator Grant in 2022. From 2019 to 2021, he served as co-spokesperson of the Cluster of Excellence CASA - Cyber Security in the Age of Large-Scale Adversaries. He received his Ph.D. in Computer Science from the University of Mannheim in 2009 and holds a diploma in Computer Science from RWTH Aachen University. Read more
