Master of Science in

We study techniques for the design of algorithms (such as dynamic programming) and algorithms for fundamental problems (such as fast Fourier transform FFT). In addition, we explore computational intractability, specifically, the theory of NP-completeness. The key topics covered in the course are: dynamic programming; divide and conquer, including FFT; randomized algorithms, including RSA cryptosystem; graph algorithms; max-flow algorithms; linear programming; and NP-completeness.

This course uncovers the science behind computing by studying computation abstractly without involving any specifics of programming languages and/or computing platforms. Specifically, it studies finite automata which capture what can be computed using constant memory, the universal computational model of Turing machines, the inherent limits of what can be solved on a computer (undecidability), the notion of computational tractability, and the P vs NP question. Finally, the course also involves Boolean circuits, cryptography, polynomial hierarchy, rigorous thinking and mathematical proofs.

This course discusses the advanced concepts in operating system design and implementation. The operating system provides a convenient and efficient interface between user programs and the hardware of the computer on which they run.

This course covers widely applicable mathematical tools for computer science, including topics from graph theory, probability theory, information theory, and logic. It includes practice in reasoning formally and proving theorems.

This course provides a broad overview of bioinformatics for single cell omics technologies, 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 starts with an accessible introduction to basic molecular biology: the cell structure, the central dogma of molecular biology, the flow of biological information in the cell, the different types of molecules in the cell, and how we can measure them. This course then 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.

This course provides an overview of foundational principles and contemporary topics in information security. Students will examine system protection strategies, structural security frameworks, software resilience, and detection of security threats. The course integrates theoretical concepts with practical applications to enhance the understanding of securing complex information systems.

This course aims at uncovering the fundamental principles of programming language design, semantics, and implementation.

Parallel and distributed systems are ubiquitous in many applications in our daily life including AI, online games, social networks, web services and healthcare simulations. These systems distribute computation over many computing units because they must sustain massive workloads that cannot fit into a single computer. Designing efficient, easy-to-maintain and correct parallel and distributed systems is challenging. In this course, we specifically study distributed computing, consistency, remote procedure calls, logging, recovery, and MapReduce. Further, we will cover instruction-level parallelism, parallel programming, cache coherence, memory consistency, and synchronization implementation.

This course is an introductory course on data mining, which is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

This course is an introductory course on big data processing, which is the process of analyzing and utilizing big data. The course involves methods at the intersection of parallel computing, machine learning, statistics, database systems, etc.

As Machine Learning (ML) programs increase in data and parameter size, their growing computational and memory requirements demand parallel and distributed execution across multiple network-connected machines. In this course, students will learn the fundamental principles and representations for parallelizing ML programs and learning algorithms. Students will also learn how to design and evaluate (using standard metrics) and compare between complex parallel ML strategies composed out of basic parallel ML “aspects” and evaluate and compare between the architecture of different software systems that use such parallel ML strategies to execute ML programs. Students will also use standard metrics to explain how compilation and resource management affects the performance of parallel ML programs.

This course provides a comprehensive introduction to Natural Language Processing. It builds upon fundamental concepts in Mathematics, specifically probability and statistics, linear algebra, and calculus, and assumes familiarity with programming.

This course provides a methodological and an in-depth background on key core Natural Language Processing areas based on deep learning. It builds upon fundamental concepts in Natural Language Processing and assumes familiarization with mathematical and machine learning concepts and programming.

This course provides a comprehensive introduction to Speech Processing. It builds upon fundamental concepts in Speech Processing and assumes familiarity with Mathematical and Signal Processing concepts.

The course covers the mathematical foundation of robotic systems and introduces students to the fundamental concepts of ROS (Robot Operating System) as one of the most popular and reliable platforms to program modern robots. It also highlights techniques to formally model and study robot kinematics, dynamics, perception, motion control, navigation, and path planning. Students will also learn the interface of different types of sensors, read and analyze their data, and apply it in various robotic applications.

Master’s thesis research exposes students to an unsolved research problem, where they are required to propose new solutions and contribute towards the body of knowledge. Students pursue an independent research study, under the guidance of a supervisory panel, for a period of one year. Master’s thesis research helps train graduates to pursue more advanced research in their Ph.D. degree. Further, it enables graduates to independently pursue an industrial project involving research component.

This course focuses on teaching students how to develop innovative research-based approaches that can be implemented in an organization. It covers various research designs and methods, including scientific methods, ethical issues in research, measurement, experimental research, survey research, qualitative research, and mixed methods research. Students will gain knowledge in selecting, evaluating, and collecting data 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 topic that can be innovative, entrepreneurial, and sustainable and can be applied in any organization related to the topic of research.

M.Sc. Internship (up to six weeks)