Doctor of Philosophy in

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: This course will prepare students to produce professional-quality academic research and solve practical research challenges based on innovative and ethical research principles. This course will provide exposure to a variety of research topics related to AI, research integrity, AI ethics, and organizational challenges. Students will learn to assess their own research projects and scrutinize the research methods and metrics used in their research and critically examine the ethical implications of their work. They will learn about the peer-reviewing process, participate in reviewing their classmates’ work, and learn best-practice for oral and written presentation of research. After completing the course, students will have the skills to develop a research methodology and conduct research that is rigorous and ethical.

Assumed knowledge: Basics of linear algebra, calculus, and probability and statistics. Proficiency in programming (data structures, algorithms) and ROS/Gazebo. 

Course description: Motion planning is an integral component of robotic applications. It helps the robot to strategically decide on its future moves and when to take them. The course covers the state-of-the-art motion planning techniques along with their applications to different kinds of robots (e.g., ground, aerial, marine, humanoid, manipulator). It provides a theoretical in-depth analysis of such methods and teaches students how to implement them through several programming-based assignments. 

Assumed knowledge: Basics of linear algebra, calculus, trigonometry, and probability and statistics. Proficiency in Python and ROS/Gazebo. 

Prerequisite course/s: ROB701 Introduction to Robotics or equivalent 

Course description: The course covers the foundations of multi-robot systems. It introduces students to the state-of-the-art multi-robot research through a combination of classical teaching and seminar-style lectures and labs. Students will learn how to apply a consortium of techniques, such as stochastic processes, graph theoretic methods, geometric concepts, and optimization principles, to model, analyze, and drive multi-robot systems. 

Assumed knowledge: Basics of linear algebra, calculus, and probability and statistics. Proficiency in programming in Python or C/C++. Experience with ROS/Gazebo. 

Prerequisite course/s: ROB701 Introduction to Robotics or equivalent 

Course description: Humanoid robots have become more and more prevalent with the increase in the demand of services and human-assistive robots. This specialized course covers various advanced topics in the state-of-the-art of humanoid robots, such as their kinematics, dynamics, modeling, control, motion planning, object grasping and manipulation, perception, learning, and interaction with humans. The course provides a blend of theoretical in-depth analysis of such techniques and hands-on practice through simulation and hardware implementation. 

Assumed knowledge: Hands-on experience with Python and Pytorch, or equivalent language/library. Basics of linear algebra, calculus, and probability and statistics. 

Course description: This Ph.D. course focuses on the key advanced computer vision techniques utilized in autonomous robotics, such as image formation, feature detection and description, multiple view geometry, dense reconstruction, tracking, event-based vision, visual-inertial odometry, visual simultaneous localization and mapping (SLAM), locomotion concepts, and deep learning based visual positioning. 

Assumed knowledge or preparation: Coursework and a pass in 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.

Assumed knowledge or preparation: Coursework and a pass in 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 will prepare students to produce professional-quality academic research and solve practical research challenges based on innovative and ethical research principles. This course will provide exposure to a variety of research topics related to AI, research integrity, AI ethics, and organizational challenges. Students will learn to assess their own research projects and scrutinize the research methods and metrics used in their research and critically examine the ethical implications of their work. They will learn about the peer-reviewing process, participate in reviewing their classmates’ work, and learn best-practice for oral and written presentation of research. After completing the course, students will have the skills to develop a research methodology and conduct research that is rigorous 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: Programming in Python and Jupyter Notebooks. Familiarity with command line and GitHub. Basic artificial intelligence (AI)/machine learning (ML) knowledge.

Anti-requisite/s: CB703 Introduction to Single Cell Biology and Bioinformatics

Course description: This course introduces students to the diverse landscape of biological data, including its types and characteristics and explores the foundational principles of single-cell omics bioinformatics, encompassing key methodologies, tools, and computational workflows, with an emphasis on the development of foundation models for single cell omics data from a research perspective. Single cell omics technologies are a new and fast-growing family of biological assays that enables measuring the molecular contents of individual cells with very high resolution and is key to advancing precision medicine. The course covers essential bioinformatics aspects for working with single cell omics data.

Assumed knowledge: Understanding of basic image processing and computer vision concepts. Hands-on experience with Python and Pytorch or equivalent language/library.

Course description: This course provides a comprehensive introduction to Advanced computer vision techniques. The students will develop skills to critique the state-of-the-art computer vision research papers. The course aims at building foundation concepts for modern computer vision as well as developing expertise in several specialized areas of research in computer vision. The following topics will be covered in the course: (i) deep learning for computer vision; (ii) recent developments in convolutional neural networks and transformers; (iii) advanced techniques in object detection and segmentation; (iv) advanced vision applications such as medical image segmentation and remote sensing change detection; (v) development of efficient computer vision architectures; (vi) human centric vision; and (vii) introduction to vision language models and diffusion models.

Assumed knowledge: Linear algebra, numerical methods or equivalent hands-on experience with Python and C++ or equivalent language/library. Basic knowledge in computer vision.

Course description: The course exercises an in-depth coverage of special topics in 3D computer vision. The students will be able to critique the state-of-the-art methods on multi-view stereo, 3D reconstruction, 3D shape analysis, 3D deep learning and synthesis, students will have to implement papers to accomplish the following goals: (1) reproduce results reported in the papers; and (2) improve the performance of published peer-reviewed works. This course assumes that the students are familiar with the basic concepts of computer vision, linear algebra, and numerical methods.

Assumed knowledge: Linear algebra, C/C++ programming, computer vision, basic artificial intelligence (AI)/machine learning (ML) knowledge.

Course description: This course introduces 3D geometry processing, an important field that intersects computer vision, computer graphics, and discrete geometry. This course will cover the mathematical foundations for studying 3D surfaces from a discrete differential geometric standpoint and present the full geometry processing pipeline: from 3D data capture, mesh smoothing, surface reconstruction, parameterization, registration, shape analysis (correspondence, symmetry, matching), data-driven synthesis, interactive manipulation, to 3D printing. This course will offer practical coding exercises to understand basic geometry processing algorithms and exciting project around data capture and geometry processing.

Assumed knowledge: Basics of linear algebra, calculus, computer vision/machine learning, and probability and statistics demonstrated through relevant coursework. Proficiency in Python and Pytorch.

Course description: In the field of computer vision, models have typically been trained to perform well on a specific task or dataset by maximizing performance on a validation set. However, this approach only represents a small part of the types of scenarios that are of interest in real-world applications. In recent years, there has been growing interest in exploring different approaches to learning that can be applied in more diverse and dynamic environments. These approaches, which include lifelong learning, continual learning, meta-learning, transfer learning, multi-task learning, and out-of-distribution generalization, aim to enable models to be more robust, efficient, versatile, and well-behaved in non-stationary settings. This graduate course will focus on these emerging learning paradigms and how they can be applied to computer vision and multimodal learning tasks.

Assumed knowledge: Basics of linear algebra, calculus, computer vision/machine learning, and probability and statistics demonstrated through relevant coursework. Proficiency in Python and Pytorch.

Course description: Vision and language encode complementary information and have long been studied together. With the advent of large language models (LLMs), vision-language models are now more popular than ever and represent one of the most active areas of modern computer vision. This course will cover learning methods and joint models for image and text modalities and will address a wide range of problems including vision-language pre-training, text-based image search, image and video captioning, visual question answering, visual dialog, text-to-image synthesis as well as vision-language navigation and manipulation.

Assumed knowledge: Good understanding of basic reinforcement learning (RL). Basics of linear algebra, calculus, trigonometry, and probability and statistics. Proficiency in Python and good knowledge of Pytorch library.

Course description: The course covers advanced topics in reinforcement learning (RL). Participants will read the current state-of-the-art relevant literature and prepare presentations to the other students. Participants will explore how the presented methods work in simplified computing environments to get a deeper understanding of the challenges that are being discussed. Topics discussed include exploration, imitation learning, hierarchical RL, multi agent RL in both competitive and collaborative setting. The course will also explore multitask and transfer learning in RL setting.

Assumed knowledge: Basic knowledge of linear algebra, probability, and statistical inference. Basics of machine learning. Basics of Python (or Matlab) or Pytorch.

Course description: In the past decades, interesting advances were made in machine learning, philosophy, and statistics for tackling long-standing causality problems, including how to discover causal knowledge from observational data, known as causal discovery, and how to infer the effect of interventions. Furthermore, it has recently been shown that the causal perspective may facilitate understanding and solving various machine learning/artificial intelligence problems such as transfer learning, semi-supervised learning, out-of-distribution prediction, disentanglement, and adversarial vulnerability. This course is concerned with understanding causality, learning causality from observational data, and using causality to tackle a large class of learning problems. The course will include topics like graphical models, causal inference, causal discovery, and counterfactual reasoning. It will also discuss how we can learn causal representations, perform transfer learning, and understand deep generative models.

Assumed knowledge: Understanding of machine learning (ML) principles. Good knowledge of multivariate calculus, linear algebra, optimization, probability, and algorithms. Proficiency in some ML frameworks e.g., PyTorch and TensorFlow.

Course description: This course is an introduction to the core ideas and theories of statistical learning theory, and their uses in designing and analyzing machine learning systems. Statistical learning theory studies how to fit predictive models to training data, usually by solving an optimization problem, in such a way that the model will predict well, on average, on new data.

Assumed knowledge: Advanced calculus, and probability and statistics. Proficiency in programming. Foundation of machine learning. Good Knowledge of optimization tools.

Course description: The course focuses on building foundations and introducing recent advances in dimensionality reduction and manifold learning, which are key topics in machine learning. This course builds upon fundamental concepts in machine learning and assumes familiarity with concepts in optimization and advanced calculus. The course covers advanced topics in spectral, probabilistic, and neural network-based dimensionality reduction and manifold learning, as well as contrastive learning and disentangled representation learning. Students will be engaged through coursework, assignments, presentations, and projects.

Assumed knowledge: Familiarity with fundamental concepts in machine learning. Familiarity with writing machine learning programs.

Course description: Training the largest machine learning (ML) programs requires petaFLOPs (1015) to exaFLOPs (1018) of computing operations, as well as multiple terabytes (1012) of hardware accelerator memory. Accordingly, 100s to 1000s of these accelerators are needed to satisfy both the computing and memory requirements of the large-scale ML. This course covers systems architecture design, communication strategies and algorithmic modifications required to execute ML training in a parallel and distributed fashion across many network-connected hardware accelerators. In the first part of the course, students will learn a comprehensive set of principles, representations, and performance metrics for parallelizing ML programs and learning algorithms, as well as learn how to compare and evaluate different parallel ML strategies composed out of basic parallel ML “aspects”. In the second part of the course, students will apply these skills to read and critique peer-reviewed literature on parallel and distributed ML systems.

Assumed knowledge: Knowledge of linear algebra, calculus, probability, and statistics. Basics of computer vision/machine learning demonstrated through relevant coursework. Proficiency in Python and Pytorch or equivalent library

Course description: This course will provide students with a deep dive into key issues related to trustworthy and responsible machine learning. Students will learn about adversarial/poisoning and privacy/confidentiality attacks against machine learning systems and defense mechanisms to mitigate them. The course will approach adversarial machine learning through an optimization and game-theoretic framework. The emphasis on privacy-preserving computation for data science and machine learning would be through a formal mathematical notion of privacy called differential privacy. The course will also cover other ethical issues in machine learning such as fairness, safe unbiased and responsible content generation, watermarking for content authentication and ownership verification (provenance), and attacks such as deep fakes, their detection and verification of robustness of relevant defenses (multimedia forensics).

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: This Ph.D.-level course assumes familiarity with core concepts in probability theory, including random variables and basic stochastic processes. Students should have prior exposure to fundamental machine learning methods and neural networks, as well as comfort with Python programming, ideally using frameworks such as PyTorch. Basic understanding of ordinary differential equations (ODEs) and elementary numerical methods is advantageous but not strictly required. Advanced concepts such as measure theory, stochastic differential equations, and optimal transport will be briefly reviewed during the course, though some prior exposure would be beneficial to fully engage with the material.

Prerequisite course/s: ML8101 Foundations of Machine Learning

Course description: This advanced course offers an in-depth exploration of diffusion models, flow matching, and consistency models, essential tools for state-of-the-art generative AI. Beginning with foundational principles, students will gain rigorous understanding of diffusion processes, stochastic differential equations, and discrete Markov chains, ensuring a robust conceptual framework. We will examine classical and modern diffusion-based generative techniques, such as denoising diffusion probabilistic models (DDPM) and score-based generative modeling (SGM), alongside detailed mathematical derivations and convergence analysis. The course progresses to flow matching, elucidating the connections and contrasts with diffusion methods through explicit mathematical formulations, focusing on optimal transport theory, continuous normalizing flows, and numerical solutions of differential equations governing generative processes. We will then dive deeply into consistency models, analyzing their theoretical foundations, fast sampling techniques, and how they bridge diffusion and flow-based approaches. The course incorporates practical implementations and case studies in Python, ensuring students achieve both theoretical depth and applied proficiency. Upon completion, participants will be equipped with comprehensive knowledge of the mathematics, theory, and practical applications behind diffusion and flow-based generative models, preparing them for advanced research or industry innovation.

Assumed knowledge: Basic knowledge of probability theory and statistical inference. Familiarity with basic algorithms and programming.

Course description: This course provides a comprehensive introduction to probabilistic graphical models (PGMs), a powerful framework for representing and reasoning with uncertainty in complex systems. The course delves into the core concepts of representation, inference, and learning in graphical models. Topics include Bayesian networks (directed acyclic graphs), Markov networks (undirected graphs), and their extensions. The course will cover exact and approximate inference algorithms (e.g., variable elimination, message passing, sampling, variational methods) and learning paradigms for both parameters and model structure from data. The course will equip students with the theoretical foundation and practical skills to design, implement, and apply PGMs to real-world problems.

Assumed knowledge: Understanding of machine learning (ML) principles and basic algorithms. Good knowledge of multivariate calculus, linear algebra, optimization, probability, and algorithms. Proficiency in some ML frameworks, e.g., PyTorch and TensorFlow.

Course description: This graduate course explores a modern branch of machine learning: collaborative learning (CL). In CL, models are trained across multiple devices or organizations without requiring centralized data collection, with an emphasis on efficiency, robustness, and privacy preservation. CL encompasses approaches such as federated learning, split learning, and decentralized training. It integrates ideas from supervised and unsupervised learning, distributed and edge computing, optimization, communication compression, privacy preservation, and systems design. The field is rapidly evolving, with early production frameworks (e.g., TensorFlow Federated, Flower) and active research addressing both theoretical foundations and practical challenges. This course familiarizes students with key developments and practices including:
– Optimization methods for collaborative learning
– Techniques to mitigate communication and computation bottlenecks
– Handling data and systems heterogeneity
– Client and participant selection strategies
– Ensuring robustness, fairness, and personalization
– Preserving privacy in collaborative settings

Evaluation is based primarily on students’ paper presentations and a final project chosen by each student, encouraging hands-on engagement with cutting-edge research and applications.

Assumed knowledge: Understanding of calculus, algebra, and probability and statistics. Programming in Python or similar language.

Antirequisite course/s: NLP701 Natural Language Processing

Course description: This course focuses on recent research in natural language processing (NLP) and on developing skills for performing research to advance the state–of–the–art in NLP.

Assumed knowledge: Understanding of calculus, algebra, and probability and statistics. Programming in Python or similar language.

Course description: This course focuses on recent topics in natural language processing (NLP) and on developing skills for performing research to advance the state–of–the–art in NLP. Specifically, this course will cover fundamentals of LLMs such as transformers architecture, methods on training and evaluating LLMs via distributed training and efficiency methods, and application in multilinguality, translation, and multimodality.

Assumed knowledge: Understanding of calculus, algebra, and probability and statistics. Programming in Python or a similar programming language.

Course description: This course provides a comprehensive introduction to speech processing. It focuses on developing knowledge about the state of the art in a wide range of speech processing tasks, and readiness for performing research to advance the state of the art in these topics. Topics include speech production, speech signal analysis, automatic speech recognition, speech synthesis, neural speech recognition and synthesis, and recent topics in foundation models and speech processing.

Assumed knowledge: Understanding of calculus, algebra, and probability and statistics. Programming in Python or a similar programming language. Understanding of current natural language processing (NLP) methods.

Prerequisite course/s: NLP805 Natural Language Processing – Ph.D.

Course description: This course focuses on recent topics in natural language processing (NLP) and on developing skills for performing research to advance the state-of-the-art in NLP.

Assumed knowledge: Understanding of calculus, algebra, and probability and statistics. Programming in Python or a similar programming language.

Prerequisite course/s: NLP807 Speech Processing – Ph.D.

Course description: This course explores the cutting-edge techniques and methodologies in the field of speech processing. The course covers advanced topics such as automatic speech recognition, language modeling and decoding, speech synthesis, speaker identification, speech diarization, paralinguistic analysis, speech translation and summarization, multilinguality and low-resource languages, and spoken dialog systems. Students will delve into modern models and frameworks for the different speech tasks. The course emphasizes both theoretical understanding and practical implementation, fostering skills necessary for innovative research and development in speech technologies.

Assumed knowledge: Basic concepts in linear algebra, calculus, and probability and statistics. Programming skills in Python.

Co-requisite/s: NLP805 Natural Language Processing – Ph.D.

Course description: The course introduces students to the topic of vision-language generation and related emerging topics in the field. The course prepares them to perform research that advances the state of the art in this research area.