Overview

Computing and information science (CIS) is the backbone of a modern technological society and the driving force behind today’s industries. It provides the data management, data analysis and computational capabilities needed to solve the world’s most challenging problems in every domain. The mission of the Computing and Information Science Program at Masdar Institute is to create CIS professionals who are familiar with the unique challenges and problems of sustainability and alternative energy, and who can effectively bridge the gap between information technology (IT) and related engineering disciplines.

Graduates of the Masdar Institute Computing and Information Science Program will have the range of skills necessary to take up technical or managerial positions in industry, and be capable of conducting independent, cross-disciplinary research. 

Program Goals
The Computing and Information Science Program at Masdar Institute aims to achieve the following goals:

  • Create leaders who are capable of developing and managing cutting-edge information technologies, thus increasing the knowledge force in Abu Dhabi and the surrounding region;
  • Impart in graduates an understanding of the value of technical scholarship, research, and service to society;
  • Produce graduates who are aware of the requirement for, and possess the ability to, engage in life-long learning; and
  • Collaborate across Masdar Institute to capitalize on and contribute to the Institute’s interdisciplinary nature to help solve important world problems.

Program Learning Outcomes
To achieve its program goals, the Computing and Information Science Program has defined the following targeted learning outcomes that have been adapted from the accreditation criteria stipulated by the ABET Computing Accreditation Commission (ABET, 2008). This adoption and adaptation of the IT goals will help ensure the highest standards as well as conformity to established international norms.
Upon graduation from the CIS Program, students will be able to:

  • Use and apply current technical concepts and practices in core computing and information technologies;
  • Analyze a problem, and identify and define the computing requirements appropriate to its solution;
  • Design, implement, and evaluate computer-based systems, processes, components, and programs both in teams and individually to meet desired outcomes;
  • Communicate effectively with a range of audiences; and
  • Recognize the need for, and have an ability to, engage in continuing professional development.

Objectives & Curriculum

Academics
The Computing and Information Science Program provides students from a variety of engineering and science backgrounds with a broad spectrum of computing and information science skills, as well as exposure to the problems of global climate change and renewable energy. The courses offered range from subjects with a high level of technical and computational content to those dealing with the managerial and organizational aspects of computing and information science. This mix of courses reflects the need for program graduates to be capable of filling both research and managerial/strategic roles in a variety of renewable and sustainable energy industries.

Research
Computing and information science research at Masdar Institute provides cutting-edge exploration of topics in computing and information science management, software development, data management, data processing and other related areas. The specific objectives of Masdar Institute Computing and Information Science Program research are as follows:

  • Developing breakthroughs in information technology that are directly relevant to the themes of energy and sustainability;
  • Promoting cross-disciplinary and collaborative research within Masdar Institute and with local industry in areas where IT plays a supporting or enabling role;
  • Establishing international research collaborations that position Masdar Institute and Abu Dhabi as global leaders in information technology and its application to energy and sustainability; and
  • Ensuring Masdar Institute’s high-impact research is at the forefront of the next wave of information technologies required to address global sustainability challenges.

Examples of Computing and Information Science Program research at the Institute include:

  • Data mining of online databases to reveal trends and developments relevant to alternative energies;
  • Applications of global information systems (GIS) to transportation and climate studies; and
  • Data storage and mediation technologies for distributed and scalable research data management.


Curriculum
All students for all programs are required to take four core program courses. In addition, each student must complete the following:

  • Three elective courses from any program with the approval of their advisor
  • One university core course titled Sustainable Energy: Technology, Policy, Economics  
  • 24 credits of thesis work

 
 Program Core courses

  • CIS501 Data Mining: Finding the Data and Models that Create Value
  • CIS502 Software Engineering
  • CIS507 Design and Analysis of Algorithms
  • CIS508 Distributed Computer Systems Engineering

Courses

CIS501 Data Mining: Finding the Data and Models that Create Value – 3 credits
This course covers topics such as data mining and machine learning, which assist managers in recognizing patterns and making intelligent use of massive amounts of electronic data collected via the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Additional topics studied as part of the course include: logistic regression; association rules; tree-structured classification and regression; cluster analysis; discriminant analysis; and neural network methods. The course also examines examples of successful applications in areas such as credit ratings, fraud detection, database marketing, customer relationship management, investments, and logistics. Students will gain practical experience in this area by using data mining software throughout the course.    
Prerequisites include background knowledge in mathematics and computer science/information technology. In particular, students will need to have a very good grasp of probability and statistics, software development in at least one general purpose programming language, but preferably more and algorithms.

CIS502 Software Engineering – 3 credits
This is primarily a reading and discussion graduate-level course focusing on issues in the development of software systems. The course aims to cover state-of-the-art software engineering research and technologies, while focusing on what has been tried in the past, what worked, what did not, and why. Topics may differ in each offering, but will be chosen from: software development processes; requirements and specifications; design principles and patterns; architecture; testing; formal analysis and reviews; quality management and assessment; product and process metrics; reuse; evolution and maintenance; team organization and people management; and software engineering aspects of programming languages.  
Prerequisites: FDN454 Algorithms, or equivalent.  


CIS507 Design and Analysis of Algorithms – 3 credits
This is an advanced programming course, focusing on techniques for the design and analysis of efficient algorithms, and emphasizing methods useful in practice. Topics include sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; greedy algorithms; graph algorithms; and shortest paths. Advanced topics may include network flow; approximation algorithms; and NP-completeness.
Prerequisites for this course include knowledge of computer programming; an undergraduate course in algorithms and data structures; an undergraduate course in discrete mathematics; and basic probability theory understanding.


CIS508 Distributed Computer Systems Engineering – 3 credits
This is a graduate-level course which presents advanced abstractions and techniques for engineering distributed and networked computer systems. Topics include concepts of consistency, fault tolerance, and security in distributed and networked computer systems.
Prerequisites: An undergraduate course in computer systems engineering and programming experience


CIS512 Information Security – 3 credits
The purpose of information security is to ensure confidentiality, integrity, and availability of information being stored and transmitted in an IT system/infrastructure. As governmental and business organizations rely more and more on IT in their day-to-day operations, securing their IT systems is becoming a more challenging issue. In this course, we will cover the latest concepts and techniques on information security including: cryptography, access control, security protocols, security measures in software, and security management.
Prerequisites: Undergraduate courses in discrete mathematics, statistics and probability programming skills (using any high-level programming language)


CIS600 – Master Thesis in Computer and Information Science – Total 24 credits
The thesis gives students an opportunity to develop and demonstrate their ability to carry out and document a reasonably comprehensive project requiring considerable initiative, creative thought, and a good deal of individual responsibility. The thesis may be a design project, an analytical paper, or experimental work of a technical nature.


CIS603 Multi-Agent Systems – 3 credits
This course is an advanced introduction to multi-agent systems, which deals with the analysis and design of distributed entities that interact with each other in both cooperative and non-cooperative domains.  Topics include cooperative and non-cooperative game theory, social choice, mechanism design, auctions, repeated games, distributed optimization, multi-agent learning and teaching, and other selected topics.
Prerequisites: One of the following courses: CIS507 Design and Analysis of Algorithms or CIS501 Data Mining: Finding the Data and Models that Create Value, or an equivalent course with the consent of the course instructor.


CIS604 Techniques in Artificial Intelligence – 3 credits
This course is a graduate-level introduction to the field of artificial intelligence (AI). It aims to give students a solid understanding of the main abstractions and reasoning techniques used in AI. Topics include: representation and inference in first-order logic; modern deterministic and decision-theoretic planning techniques; basic supervised learning methods; and Bayesian network inference and learning.
Prerequisites: CIS507 Design and Analysis of Algorithms, CIS501 Data Mining: Finding the Data and Models that Create Value or equivalents with consent of the instructor.


CIS605 Strategic Requirements Engineering – 3 credits
The price of not correctly and completely specifying systems requirements is high. Poorly specified requirements lead to low system quality, cost overruns, delayed project, dissatisfied customers, suboptimal ROI, etc. This is an interdisciplinary graduate-level course on requirements engineering and the application of requirements engineering principles and techniques to the development of complex socio-technological systems.  The course puts particular emphasis on the integration of economic, strategic, social, and technological requirements, and the analysis of their impact on the future evolution of the system. The course tightly connects philosophical, methodological, and technical body of knowledge about requirements in order to form a unified approach for systematic control of the requirements evolution and systems’ market positioning.
Prerequisites: CIS502 Software Engineering, ESM501 Systems Architecture or ESM502 Product Design and Development or equivalent


CIS606 Machine Learning – 3 credits
This course significantly extends the topics covered in the pre-requisite course, CIS501. The aim is to provide an in-depth treatment of a variety of important concepts, techniques, and algorithms in machine learning. Topics covered include linear regression, boosting, support vector machines, hidden Markov models, and Bayesian networks. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.
Prerequisites: CIS501 Data Mining: Finding the Data and Models that Create Value


CIS611 Multimodal Data Mining – 3 credits
This course aims to provide a detailed understanding of specific algorithms and pressing issues in data mining. The course will be based on a series of seminars that are organized around specific application domains. In particular, the focus will be on current research topics combined with illuminative case studies. Topics will include, but are not limited to: web mining, text and document clustering, social network mining, security, automated recommender systems and so on. During each lecture a particular topic or research paper will be covered, and students will be expected to read, analyze, and discuss the associated readings.
Prerequisites: CIS501 Data Mining: Finding the Data and Models that Create Value


CIS614 Topics in Computational Social Science – 3 credits
Computational social science is an emerging field that uses techniques from computing and information science to model, understand and predict social phenomena. This understanding can inform how we address challenges in health, sustainability, security, innovation and social adaptation. The importance of this emerging field has been recognized in MIT's interdisciplinary “Connection Science & Engineering” initiative.
This seminar-based course will cover two aspects:

  • The use of computational techniques (simulation) to model social phenomena; and
  • The use of data analytics to learn models of (and to predict) social phenomena using real data.

First, students obtain proficiency in the mathematical modeling of social networks and manipulating their data (around 50% of the course). Students will then read recent papers published in this area and present them in class, with topics rotating in each offering. Students will also be required to complete a major project, which involves substantial use of mathematical modeling combined with computational simulation or data analysis/mining (e.g. from mobile phones or social media), and writing up the results in a short article.
Prerequisites: Both CIS507 Design and Analysis of Algorithms and CIS501 Data Mining: Finding the Data and Models that Create Value, or equivalents with consent of the instructor


CIS617 Advanced Topics in Algorithms – 3 credits
This is a graduate-level course that presents advanced topics in algorithms. Through the course, a variety of recent advanced developments in algorithms will be presented, such as randomized algorithms, approximation algorithms, and online algorithms. In contrast to conventional courses, we emphasize the intuition of concepts, the fundamental insights of algorithms and the elegance of related theories.
Prerequisites: CIS507 Design and Analysis of Algorithms, or equivalent


CIS619 Topics in Algorithmic Game Theory – 3 credits 
This is an advanced course on algorithmic aspects of economics and game theory as they arise in modern information networks. It covers a range of topics at the intersection of classical game theory and algorithm design, such as equilibrium concepts, mechanism design, auctions, non-cooperative and cooperative games, inefficiency of equilibria, with an emphasis on algorithms and computational complexity.
Prerequisites: CIS507 Design and Analysis of Algorithms, or equivalent with instructor permission


CIS620 Algorithms in Bioinformatics – 3 credits 
This course focusses on algorithms to explore the many types of data produced in the Life Sciences, while combining theory and practice. Given the interdisciplinary nature of Bioinformatics, the course highlights the major mechanisms in genetics to an extent that enables formal, algorithmic approaches to process the heterogeneous data from genomics- and proteomics-based technologies: DNA sequence assembly and alignment, functional gene annotation, biological relational databases, metabolic network analysis, comparative genomics, phylogenetics, gene expression analysis and structural bioinformatics. They are coupled with fundamental algorithmic techniques including graph algorithms, dynamic programming, Statistics/Machine Learning, hierarchical clustering, classification and Bayesian methods. We will combine programming (mainly BioPython) and state-of-the-art analysis tools and apply it to Bioenergy, Metagenomics and Biomedicine.
Prerequisites: One of the following: CIS501 Data Mining: Finding the Data and Models that Create Value, CIS 507 Design and Analysis of Algorithms, or WEN520 Microbiology for Environmental and Bioprocess Engineering, or equivalent and consent of the instructor.

Space Concentration

The Master’s Concentration in Space Systems and Technology at Masdar Institute aims to produce post-graduate students with the multi-disciplinary preparation that meets the following goals:

  • Create leaders that are capable of developing and managing cutting-edge computing and information technologies, thus increasing the knowledge force in Abu Dhabi and the surrounding region;
  • Instill in graduates an understanding of the value of technical scholarship, research, and service to society;
  • Produce graduates that are aware of the requirement for and possess the ability to engage in life-long learning; and
  • Collaborate across the Masdar Institute to capitalize on and contribute to the Institute’s interdisciplinary nature to help solve important world problems.

In addition to the CIS Program specific core courses and the University Core Course, space concentration students are supposed to take the following space concentration core courses:

  • SSC501 Spacecraft Systems and Design, SSC502 Spacecraft Systems Lab, SSC503 Spacecraft Systems Lab 2, and SSC504Spacecraft Systems Lab 3

Course

Credits

Year 1: Fall Semester

 

MSc Program Specific Core Course 1

3

MSc Program Specific Core Course 2

3

Space Concentration Core Course (SSC501 Spacecraft Systems and Design)

3

Master's Thesis Work related to space technology

3

Year 1: Spring Semester

 

MSc Program Specific Core Course 3

3

MSc Program Specific Core Course 4

3

SSC502: Space Systems Lab-1

1

Master's Thesis Work related to space technology

3

Year 1: Summer

 

Master's Thesis Work related to space technology

6

Year 2: Fall Semester

 

Technical Elective relevant to space technology

3

MI Core Course: (UCC501: Sustainable Energy)

3

SSC503: Space Systems Lab-2

1

Master's Thesis Work related to space technology

6

Year 2: Spring Semester

 

SSC504: Space Systems Lab-3

1

Master's Thesis Work related to space technology

6

TOTAL CREDITS

48


In addition to the Computing and Information Science Program outcomes, program students in the space concentration are also expected to attain the following concentration specific outcomes:

  • Demonstrate proficiency in the aspects of space systems design and analysis; amd
  • Design and build a small-satellite as a part of a multi-disciplinary team.