Doctor of Philosophy in

Assumed knowledge or preparation: Coursework plus 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: Basics of linear algebra, calculus, probability and statistics, and basic machine learning concepts. Proficiency in Python. Linear algebra, mathematical analysis, algorithms. At least intermediate programming skills are necessary. 

Course description: This course provides an introduction to both foundational machine learning and advanced deep learning techniques. It will cover both essential machine learning topics including classification, regression, clustering, and dimensionality reduction, alongside key deep learning concepts and methods including the universal approximation theorem, different neural architectures (convolutional neural networks, recurrent neural networks, transformer), and the recipe for training deep neural networks. Particularly, lab sessions are provided to gain practical, hands-on experience of building and training these models, from basics like data preparation, model implementation, to advanced specifics including regularization, normalization, and optimization. 

Assumed knowledge: Algorithms and data structure, or equivalent (similar to CS701/7101) 

Course description: This course covers a broad overview of the many diverse types of data structures, including persistent, retroactive, geometric data structures, like a map, and temporal data structures, as in storage that happens over a time series. A comprehensive study of these data structures is a vital component of this subject. It also covers dictionaries, static trees, strings, succinct structures, and dynamic graphs. Finally, the course will cover the major directions of research for a wide variety of such data structures. 

Assumed knowledge: A background in basic machine learning concepts, as well as an advanced background or prior course in computer organization and digital design. 

Course description: This course offers an in-depth examination of the intricate relationship between advanced artificial intelligence and sophisticated computer systems. Building on foundational principles, it focuses on cutting-edge methodologies for enabling highly efficient AI workloads and leveraging AI techniques to fundamentally enhance system performance. The curriculum delves into advanced topics in system architecture, hardware-software co-optimization for AI accelerators, sophisticated resource management in large-scale AI deployments, and adaptive, AI-driven system optimization. Through critical analyses of seminal research, hands-on engagement with state-of-the-art frameworks, and a significant research project, students will explore open problems and emerging frontiers in scaling AI applications, maximizing system efficiency, and deeply integrating AI into heterogeneous computing environments. By engaging with contemporary case studies and pioneering research challenges, students will cultivate the advanced analytical and synthesis skills essential for leading contributions to the rapidly evolving field of AI systems in both academia and industry. 

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 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. 

PhD Internship (up to four months)

Assumed knowledge: Algorithms and data structure (for example, CS701/CS7101), or equivalent.  

 Course description: The course covers the following topics: –
The theory of NP-completeness and its relationship to the complexity classes P and NP.
– Circuit complexity and alternations.
– SAT, the complexity of counting, and algebraic circuit complexity.
– Circuit complexity lowerbound, hardness vs randomness, ironic complexity, and interactive proof systems. 

Assumed knowledge: Algorithms and data structure (for example, CS701/CS7101), or equivalent.  

Course description: Randomized algorithms went from being a tool in computational number theory to finding widespread application in many types of algorithms. Two benefits of randomization have spearheaded this growth: simplicity and speed. This course discusses the basic and advanced concepts of randomized algorithms. Specifically, it includes random sampling, tail inequalities, probabilistic methods, algebraic methods, and random walks. Further, it also covers linear programming, graph algorithms and approximate counting topics. 

Assumed knowledge: Algorithms and data sructure (for example, CS701/CS7101), or equivalent.  

Course description: This course covers the topic of polyhedra, including various mathematical concepts and algorithms such as Farkas lemma, duality, complementary slackness, and decomposition of polyhedra. The course also covers topics like integer polyhedra, matrices, matching (bipartite and non-bipartite), graphs, matroids, polymatroids and submodular functions. The course will also cover the application of these concepts in machine learning. 

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: 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: Algorithms and data structure (for example, CS701/CS7101), or equivalent.  

 Course description: The course covers the following topics: 

• The theory of NP-completeness and its relationship to the complexity classes P and NP. 

• Circuit complexity and alternations. 

• SAT, the complexity of counting, and algebraic circuit complexity.  

• Circuit complexity lowerbound, hardness vs randomness, ironic complexity, and interactive proof systems. 

Assumed knowledge: Algorithms and data structure (for example, CS701/CS7101), or equivalent.  

Course description: Randomized algorithms went from being a tool in computational number theory to finding widespread application in many types of algorithms. Two benefits of randomization have spearheaded this growth: simplicity and speed. This course discusses the basic and advanced concepts of randomized algorithms. Specifically, it includes random sampling, tail inequalities, probabilistic methods, algebraic methods, and random walks. Further, it also covers linear programming, graph algorithms and approximate counting topics. 

Assumed knowledge: Algorithms and data structure (for example, CS701/CS7101), or equivalent.  

Course description: This course covers the topic of polyhedra, including various mathematical concepts and algorithms such as Farkas lemma, duality, complementary slackness, and decomposition of polyhedra. The course also covers topics like integer polyhedra, matrices, matching (bipartite and non-bipartite), graphs, matroids, polymatroids and submodular functions. The course will also cover the application of these concepts in machine learning. 

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 projects around data capture and geometry processing.  

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: 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: 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.