Machine Learning Research
Machine Learning Research
Machine learning researchers working at MBZUAI study algorithms and models that enable systems to learn from data, recognize patterns, and make predictions or decisions with minimal human intervention.
Our researchers focus on adaptable, explainable AI models to tackle challenges in healthcare, energy, and smart cities.
Our students, supervised by our world-class faculty, get unique hands-on experience by developing and evaluating algorithms on complex real datasets that can be used for many enterprise applications such as robotics, smart cities, and understanding of the human genome.
The University offers Ph.D. and M.Sc 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.
Kun Zhang
Associate Department Chair of Machine Learning (Research), Director of Center for Integrative Artificial Intelligence (CIAI), Visiting Professor of Machine Learning
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One of our main goals is creating disruptive solutions and technologies, powered by AI, that unlock the secrets of science across all areas.
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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
READ BIOFaculty Members
Tianyi Zhou
Visiting Assistant Professor of Natural Language Processing and Machine Learning
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Steve Liu
Associate Vice President of Research and Professor of Computer Science and Machine Learning
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Kun Zhang
Associate Department Chair of Machine Learning (Research), Director of Center for Integrative Artificial Intelligence (CIAI), Visiting Professor of Machine Learning
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Martin Takáč
Associate Department Chair of Machine Learning (Education), Associate Professor of Machine Learning
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Research Centers
Center for Integrative Artificial Intelligence (CIAI)
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