The advent of large-scale data on the Web and elsewhere poses new challenges and opportunities. Concepts, models, and algorithms from several fields, including database systems, information retrieval, natural language processing, statistical learning, and data mining can help us to analyze and learn from this data.
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.
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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
Simon Razniewski leads the Knowledge Base Construction and Quality area at the Databases and Information Systems Department of the Max Planck Institute for Informatics. A main objective of his research is construct domain-specific knowledge bases, incorporating all relevant stages such as entity recognition, taxonomy construction or fact extraction, and to extend traditional knowledge base models, for instance by adding counting quantifiers, negative information, or quality metadata. Besides that, he is interested in applications of knowledge bases, in particular in areas such as question answering and image enrichment, and in the extraction and consolidation of common-sense knowledge, e.g. appearance and properties of everyday objects. Read more
Mariya Toneva’s research is at the intersection of Machine Learning, Natural Language Processing, and Neuroscience. Her group bridges language in machines with language in the brain, with a focus on building computational models of language processing in the brain that can also improve natural language processing systems. Prior to joining MPI-SWS, she is conducting research as a C.V. Starr Fellow at the Princeton Neuroscience Institute. She received her Ph.D. in a joint program between Machine Learning and Neural Computation from Carnegie Mellon University. Read more
Gerhard Weikum is a Research Director at the Max Planck Institute for Informatics, where he leads the Databases and Information Systems Department. He is also an adjunct professor in the Department of Computer Science of Saarland University, and a Principal Investigator of the Cluster of Excellence on Multimodal Computing and Interaction. The long-term objective of his research is to develop methodology for knowledge discovery: collecting, organizing, searching, exploring, and ranking facts from a wide array of structured, semistructured, and textual information sources, which may exhibit varying levels of credibility. His group’s approach towards this goal combines concepts, models, and algorithms from several fields, including database systems, information retrieval, statistical learning, and data mining. Read more
Searching, Mining, and Learning with Informal Text
Andrew Yates in a senior researcher in the Databases and Information Systems Department at the Max Planck Institute for Informatics, where he leads the Searching, Mining, and Learning with Informal Text research group. In contrast to authoritative information sources, like encyclopedias, news articles, and academic papers, much of the information available on the Web is contained in informal text that requires different strategies to interpret. His research group aims to develop methods for searching, mining, and learning with such text so that it may be integrated with other knowledge. This goal spans both information retrieval and natural language processing tasks, such as mining health-related claims from social media, extracting information from dialogue, and learning to identify relevant spans of text. On the information retrieval side, the group is particularly interested in leveraging recent advances in deep learning to develop more powerful retrieval models and to learn fine-grained types of relevance, including task-specific and passage-level relevance. Read more
Emilio Zagheni is a scientific director at the Max Planck Institute for Demographic Research (MPIDR), where he heads the Department of Digital and Computational Demography. Zagheni is best known for his work on combining digital trace data and traditional sources to track and understand migrations and to advance population science.
The main goal of the Department of Digital and Computational Demography is to advance fundamental population science, through the lens of digital and computational perspectives, for the benefit of everyone. Thematically, a first primary focal area, addressed by the Laboratory of Migration and Mobility, is on measuring, understanding, and predicting the causes and consequences of migration. A second primary focal area, addressed by the Laboratory of Population Dynamics and Sustainable Well-Being, is on monitoring, understanding, and predicting the factors that shape people’s well-being across space, time, and demographic characteristics, and as they relate to mortality and health, fertility, social and economic processes, and sustainable development. Read more
