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 PhD 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.
Kun Zhang
Acting Department Chair, Director of Center for Integrative Artificial Intelligence (CIAI), and Visiting Professor of Machine Learning
“
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
READ BIOFaculty Members
Research Centers
Center for Integrative Artificial Intelligence (CIAI)
Related
Uncovering causal relationships in multimodal biological data: A new framework presented at ICLR
Yuewen Sun's research seeks to identify latent causal variables that underlie different modalities and the relations between.....
Read More
New test that recovers hidden relationships in data to be presented at ICLR
Boyang Sun explains how his team's research into variables addresses a fundamental problem in machine learning and.....
Read More
MBZUAI and Berkeley explore the future of machine learning
Machine learning pioneer Michael I. Jordan was among the speakers discussing the cutting-edge ideas shaping the field.
Read More
