Data Management and Machine Learning
One of the central challenges of our time is managing and gaining insights from massive amounts of data through a new research paradigm that is often referred to as "data science". At its core, data science is mainly composed of data management and machine learning – two areas that are well-represented in the department with substantial interaction and collaboration between researchers. The faculty’s research covers all aspects of the data value chain: the generation and acquisition of data, data organisation and storage, data processing, and learning from data making predictions and decisions.
Areas of research
artificial intelligence, big data, cloud computing, data analytics, databases, data mining, data science, enterprise computing, machine learning, medical informatics, natural language understanding
Gustavo Alonso
Full Professor
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databases, distributed systems, enterprise computing, system aspects of programming languages, multicore, FPGAs
Valentina Boeva
Assistant Professor
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bioinformatics, biomedical data analysis algorithms, computational genomics, epigenetics, cancer research, machine learning
Yannis Chronis
Assistant Professor
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databases, data management for AI, hardware-software codesign
Ryan Cotterell
Assistant Professor
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natural language processing, computational linguistics, machine learning
Niao He
Associate Professor
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large-scale optimization, machine learning, reinforcement learning, probabilistic inference
Torsten Hoefler
Full Professor
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efficient and secure datacenter architecture and networking, cloud computing, climate simulations, large-scale machine learning, quantum and high-performance computing
Timothy Roscoe
Full Professor
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operating systems, distributed systems, networking, enterprise computing
Mrinmaya Sachan
Assistant Professor
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machine learning for natural language processing, knowledge discovery and reasoning
Bernhard Schölkopf
Full Professor
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machine learning, causal inference, applications in the sciences
David Steurer
Associate Professor
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complexity theory, approximation algorithms, convex optimization, parameter estimation, tensor methods
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