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
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
