Machine Learning Research

Machine Learning Research

Machine learning researchers working at MBZUAI investigate the development of algorithms which can improve automated cognition, perception, and action with experience by observations.

Our reseachers focus on both fundamental and applied research in machine learning. This can be used for many enterprise applications (such as business intelligence and analytics), effective web search, robotics, smart cities, and understanding of the human genome.

Our students, supervised by our world-class faculty, get unique hands-on experience by developing and evaluating algorithms on complex real datasets.

The university offers Ph.D. and master's degrees in machine learning with exceptionally advanced courses and outcomes.

Chair's message

The Machine Learning (ML) Department at MBZUAI is dedicated to imparting a world-class education in ML to our students. From foundational principles to advanced applications, our research-intensive education model will provide our students theoretical concepts to test under supervision from senior AI researchers in the field as they tackle real-world problems and produce meaningful results.

It is the task of the ML Department to engage in ML research by exposing our expert faculty, research staff, and students to problems faced by industry partners, and sponsored research. We leverage these relationships to ensure all researchers have access to the latest technology, emerging problems, and solutions. One of our main goals is creating disruptive solutions and technologies, powered by AI, that unlock the secrets of science across all areas.

department-chair

Kun Zhang

Acting Chair of Machine Learning, Professor of Machine Learning, and Director of Center for Integrative Artificial Intelligence (CIAI)

One of our main goals is creating disruptive solutions and technologies, powered by AI, that unlock the secrets of science across all areas.

It is my commitment to establish the ML Department as a major hub for ML expertise and solutions, not only in the region, but globally. We do this by embracing emerging concepts and applying sustainable ML algorithms to problems that we all face, in order to maximize our efforts and produce AI for good.

Kun Zhang

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Faculty members

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Eric Xing

President and University Professor

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Kun Zhang

Acting Chair of Machine Learning, Professor of Machine Learning, and Director of Center for Integrative Artificial Intelligence (CIAI)

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Martin Takáč

Deputy Department Chair of Machine Learning, and Associate Professor of Machine Learning

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Mohsen Guizani

Professor of Machine Learning

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Fakhreddine (Fakhri) Karray

Professor of Machine Learning

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Le Song

Professor of Machine Learning

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Jin Tian

Professor of Machine Learning

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Qirong Ho

Assistant Professor of Machine Learning

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Samuel Horváth

Assistant Professor of Machine Learning

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Salem Lahlou

Assistant Professor of Machine Learning

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Nils Lukas

Assistant Professor of Machine Learning

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Maxim Panov

Assistant Professor of Machine Learning

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Zhiqiang Shen

Assistant Professor of Machine Learning

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Praneeth Vepakomma

Assistant Professor of Machine Learning 

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Gus Xia

Associate Professor of Machine Learning

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Zhiqiang Xu

Assistant Professor of Machine Learning

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Eric Moulines

Affiliated Professor of Machine Learning

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Michalis Vazirgiannis

Affiliated Professor of Machine Learning

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Pengtao Xie

Adjunct Assistant Professor of Machine Learning

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Chih-Jen Lin

Affiliated Professor of Machine Learning

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Mingming Gong

Affiliated Associate Professor of Machine Learning

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Tongliang Liu

Affiliated Associate Professor of Machine Learning

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Yuanzhi Li

Affiliated Assistant Professor of Machine Learning

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Projects

Research centers

Center for Integrative Artificial Intelligence (CIAI)

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