Doctor of Philosophy in

Assumed knowledge: Course work plus a pass in the qualifying exam.

Course description: Ph.D. thesis research exposes students to cutting-edge and unsolved research problems, where they are required to propose new solutions and significantly contribute towards the body of knowledge. Students pursue an independent research study, under the guidance of a supervisory panel, for a period of three (3) to four (4) years. Ph.D. thesis research helps train graduates to become leaders in their chosen area of research through partly supervised study, eventually transforming them into researchers who can work independently or interdependently to carry out cutting edge research.

Course description:
This course focuses on the key psychology and human factors related to HCI design. The course provides students with a deep understanding of how the field has evolved and how interdisciplinary research has shaped the way humans interact with technology over the years. The course presents an overview of the latest AI applications in the field and an analysis of the positive and negative psychological implications of technologies. Students learn the key aspects of cognition and perception and design systems that are intuitive and optimized for adoption and learning.

Course description: This course introduces students to the foundational concepts and contemporary approaches in human-computer interaction (HCI), interaction science and design (IxD), and user experience (UX) design. Through a combination of theoretical exploration and hands-on design workshops, students will gain practical knowledge of how to design and evaluate interactive technology experiences with a user-centered approach. The course covers key methodologies in HCI, IxD, and UX design, from qualitative user research, use case and persona development, to prototyping, testing, and iterative design. We will also introduce design approaches such as speculative design, futures design, critical design, and design fiction.

Course description: This course covers four key aspects oy effective HCI design, namely tools, techniques, and platforms available in HCI, and the principles for using them effectively. The course covers a broad set of advanced AI design tools and techniques, including emerging AI design tools. This course provides students with a comprehensive understanding of the tools, techniques, and platforms used in modern design processes. It explores the integration of creative design thinking, technical proficiency, and digital collaboration to support effective design development across disciplines. The course emphasizes iterative design, user-centered approaches, and cross-platform workflows to prepare students for professional design practice in digital and physical environments.

Course description: In HCI, accessibility, inclusive and universal design principles aim to make digital experiences usable, equitable, and enjoyable for everyone, regardless of their abilities, backgrounds, or circumstances. Accessibility refers to the design of products, environments, or services that are usable by people with disabilities. Inclusivity in design ensures that users with diverse cultural, social, and economic backgrounds can equally access technologies. Students will learn how to design, develop, and evaluate interactive AI technologies that are usable by people with varying abilities, backgrounds, and contexts. The course emphasizes ethical, human-centered, and data-aware approaches to ensure fairness, transparency, and accessibility in AI interaction.

Assumed knowledge: Prior to undertaking an internship opportunity, students must have successfully completed 24 credit hours.

Course description: The MBZUAI internship with industry is intended to provide the student with hands-on experience, blending practical experiences with academic learning.

Course description: The course teaches research methods applicable to scientific research in general, and AI research in particular. It covers various topics including scientific methods, measurement and metrics in experimental research, critical appraisal and peer review, public communication, and ethical issues in AI research. Students will gain knowledge in selecting, evaluating, and collecting data and suitable research methods to address specific research questions. Additionally, they will learn design thinking skills to connect their research-based topic to practicality. After completing the course, students will have the skills to develop a full research methodology that is rigorous, entrepreneurial, and ethical.

Assumed knowledge: Course work plus a pass in the qualifying exam.

Course description: Ph.D. thesis research exposes students to cutting-edge and unsolved research problems, where they are required to propose new solutions and significantly contribute towards the body of knowledge. Students pursue an independent research study, under the guidance of a supervisory panel, for a period of three (3) to four (4) years. Ph.D. thesis research helps train graduates to become leaders in their chosen area of research through partly supervised study, eventually transforming them into researchers who can work independently or interdependently to carry out cutting edge research.

Course description: The course teaches research methods applicable to scientific research in general, and AI research in particular. It covers various topics including scientific methods, measurement and metrics in experimental research, critical appraisal and peer review, public communication, and ethical issues in AI research. Students will gain knowledge in selecting, evaluating, and collecting data and suitable research methods to address specific research questions. Additionally, they will learn design thinking skills to connect their research-based topic to practicality. After completing the course, students will have the skills to develop a full research methodology that is rigorous, entrepreneurial, and ethical.

Assumed knowledge: Prior to undertaking an internship opportunity, students must have successfully completed 24 credit hours.

Course description: The MBZUAI internship with industry is intended to provide the student with hands-on experience, blending practical experiences with academic learning.

Assumed knowledge: Linear algebra, mathematical analysis, and algorithms. At least intermediate programming skills are necessary.

Course description: The course gives an introduction to the main topics of modern machine learning such as classification, regression, clustering, and dimensionality reduction. Each topic is accompanied by a survey of key machine learning algorithms solving the problem and is illustrated with a set of real-world examples. The primary objective of the course is giving a broad overview of major machine learning techniques. Particular attention is paid to the modern Python machine learning libraries which allow solving efficiently the problems mentioned above.

Assumed knowledge: Basics of linear algebra, calculus, probability and statistics, and basic machine learning concepts. Proficiency in Python.

Course description: This course covers key concepts and methods in deep learning. Students will begin by learning the foundational principles of deep learning, including the universal approximation theorem, strategies for modeling complex patterns using neural architectures, and the specifics of training deep networks. The course then introduces a range of deep models, including convolutional neural networks, recurrent neural networks, and transformer-based architectures. Students will gain hands-on experience in building and training deep neural networks across various domains such as computer vision, medical imaging, and natural language processing.

Course description: This course provides a comprehensive introduction to entrepreneurship in the artificial intelligence (AI) era, focusing on transforming technical expertise into viable business ventures. Students will learn to identify market opportunities, validate ideas, develop prototypes, and communicate their vision effectively. The course emphasizes experiential learning through real-world applications, industry engagement, and culminates in a demo day presentation to investors and industry experts. Students will work in teams to develop and pitch AI-driven startup concepts while learning essential entrepreneurial skills, including design thinking, customer validation, storytelling, and fundraising fundamentals.

Course description: Human-computer interaction (HCI) research and design benefit immensely from emerging technology solutions (e.g. wearables, sensors, …) and Artificial Intelligence applications in the field. Emerging technologies enable new types of interaction designs, provide new methods for data collection and analysis, and assist in design of accessible and inclusive solutions. With the reduced cost and access to new technologies the traditional HCI design paradigms and design space can be extended and made available for new methodologies and technologies can be made available for various user groups. The course also reminds students of emphasizes the responsible application of these technologies and highlights the regulatory challenges their use may present.

Course description: This course focuses on information visualization -is the process of using graphical representations to convey data, trends, patterns, and relationships in a way that makes complex information accessible and understandable. Students will explore the use of charts, maps, and other interactive visual representations to present data in a manner that is easier to interpret than “raw” data alone. The course includes a focus on the use of the latest e tools for visualization of data for effective analysis, interpretation, interaction and collaboration with others. Students will learn to design projects that support effective data gathering, organization, analysis, and presentation. Emphasis will be placed on using structured narrative techniques to communicate insights clearly and maximize impact.

Course description: This course focuses on creating technology that can adapt to and serve the needs of users in a wide range of settings, cultures, environments, and situations. Users interact with technology in varied contexts in terms of physical environments, cultural backgrounds, or technological devices. Understanding and designing applications that are optimal for these different contexts is key to creating meaningful, effective, and accessible user experiences. Students will apply design thinking approaches to user-centered and contextualized interface design.

Course description: This course focuses on the intersection of creativity, digital arts, and AI, particularly in performance. The key concepts covered in this course includes critical understanding of the social, cultural, and political implications of AI in performance; understanding theories of embodiment and performance; designing interactive performance systems with sensors and actuators; exploring the use of AI for creating immersive and participatory performance experiences; and ethical considerations in embodied AI performance. Students will learn to critically evaluate the impacts of design choices in a cultural context.

Course description: In this course, students will explore the ethical, social, and cultural issues related to the design and use of interactive technologies. Through a combination of theoretical frameworks, case studies, and real-world applications, students will examine how human-computer interaction (HCI) affects users, communities, and society. Topics will include privacy, data security, accessibility, social inclusion, digital wellbeing, algorithmic bias, and the ethical responsibilities of HCI professionals. The course will also encourage students to consider the potential long-term societal impacts of emerging technologies such as AI, virtual reality, and the internet of things (IoT), and how HCI design decisions can contribute to or mitigate harm.

Assumed knowledge: Familiarity with fundamental concepts in probability, linear algebra, and statistics and programming. 

Prerequisite course/s: MTH7101 Mathematical Foundations of AI 

Course description: Probabilistic and statistical inference is the process of drawing useful conclusions about data populations or scientific truths from uncertain and noisy data. It is the foundation and an essential component of machine learning since machine learning aims to learn and improve from experience (which is represented by data). This course will cover the different modes of performing inference, including statistical modelling, data-oriented strategies, and explicit use of designs and randomization in analyses. Furthermore, it will provide in-depth treatment to the broad theories (frequentists and Bayesian) and numerous practical complexities for performing inference. This course presents the fundamentals of statistical and probabilistic inference and shows how these fundamental concepts are applied in practice. 

Assumed knowledge: Linear algebra and probability. Proficiency in Python. Basic knowledge of machine learning. 

Course description: This course focuses on building foundations and introducing recent advances in machine learning, and on developing skills for performing research to advance the state–of–the–art in machine learning. This course builds upon basic concepts in machine learning and additionally assumes familiarity with fundamental concepts in optimization and math. The course covers foundations and advanced topics in probability, statistical machine learning, supervised and unsupervised learning, deep neural networks, and optimization. Students will be engaged through coursework, assignments, and projects. 

Assumed knowledge: Students enrolling in this course should possess proficiency with high-school–level algebra and trigonometry, along with an introductory treatment of single-variable calculus (limits, basic differentiation, and elementary integration). Familiarity with manipulating simple vectors and matrices—as encountered in a first-year linear algebra or applied mathematics course—is beneficial but not strictly required; key concepts will be refreshed before deeper exploration. No prior coursework in probability or statistics is assumed.  

Course description: This seven-week course equips students with the essential mathematical tools required for modern artificial intelligence and machine learning study. Topics include single- and multivariate calculus (limits, derivatives, gradients, Jacobians, Hessians), linear algebra (vectors, matrices, eigenvalues, decompositions), and probability and statistics (discrete and continuous distributions, Bayes’ theorem, expectation, covariance, law of large numbers, central limit theorem, maximum-likelihood estimation). Emphasis is placed on conceptual understanding, geometric intuition, and hands-on problem solving that link theory to common AI algorithms such as optimization and probabilistic inference.