Concentrations Mappings CS New Program (128 Credits)

(Summer Training Option)

In case the concentration had a re-requisite course, it will be mapped as follows:

In summary, the mapping will be as follows:

• Four concentration courses will be mapped to four major elective courses.
• Concentration pre-requisite course, if any, will be mapped to the technical elective course.

The committee applied the proposed mapping to all concentrations.

1. Artificial Intelligence and Machine Learning

This interdisciplinary concentration provides the students with the required knowledge to develop intelligent techniques and systems. Students are exposed to topics such as machine learning, deep learning, computer vision, and natural language processing. Furthermore, it also covers classification, regression, clustering, dimensionality reduction, perception, motion and manipulation, reinforcement learning, and various types of neural networks. It promotes interdisciplinary education where computer science intersects with mathematics and engineering. The applications of this concentration are wide-ranging and include automatic image and video processing, healthcare, financial data and trading, speech recognition, facial identification, and seismic survey processing.

Host: ICS

2. Cybersecurity and Blockchain

This interdisciplinary program covers topics related to secure and trusted computing, including data and information assurance, identification of cyber assets and related security risks and threats, measurement of system resilience against cyber-attacks, and security policy compliance and governance. Students learn the fundamental pillars of computer security and data privacy and how they affect complex engineering systems (e.g. manufacturing plants). Topics include cryptology, access control models and mechanisms, intrusion detection systems, and integrity verification mechanisms. Students also learn the fundamentals of Blockchain technology, including record and hash replication, and types of blockchains (public, private, and hybrid), as well the applications in cryptocurrency and various other scientific, engineering, and business use cases.

Host: ICS

3. Cloud Computing

This interdisciplinary program focuses on the development of Internet-scale applications that can serve millions of users at the same time. The program includes topics that span the disciplines of computer engineering, computer science, and software engineering. These topics include Cloud architectures and enabling technologies, Cloud services and deployment models, software-defined infrastructures, principles of distributed systems, distributed programming models, Web applications, and Cloud-native applications. The program also covers the modern software engineering practices for Cloud applications development and deployment, software architectures for Cloud applications, and design patterns and tools for performance, dependability, and security. This program is distinguished by its hands-on approach to teaching. Students will come out of the program with the motivation, tools, and confidence they need to successfully apply Cloud computing to create business value.

Host: COE

4. Computer Networks

Computer networks are the backbone that interconnects different networks and provides a path for exchange of data around the world. This multidisciplinary program is designed to prepare students to enter the field of computer networks and equip them with knowledge and skills to design, manage and secure computer networks. The program also enables students to utilize tools and technologies in computer networks. 

The program covers subjects related to wired and wireless networks, network design and management, network security, and internet cloud engineering. Topics include computer network OSI layers, radio frequency propagation models, multiple access techniques, quality of service, 5/6 G networks, interVLAN routing protocols, interior and Exterior for routing for IPv4 and IPv6, multicasting, software-defined network, Internet and web protocols and technologies (HTTP), basics of web development: frontend, backend, and full-stack (HTML, CSS, JavaScript, Node.js), utility computing: Cloud and Edge computing, Cloud Service-oriented architecture and microservice, XaaS pyramid, serverless computing, cloud resource management, virtualization and containerization, cloud data storage, BigTable, Dynamo, and Cassandra, Network Management Standards, Models, and protocols, applications, tools, and systems, remote monitoring and management (RMM), security of LANs, wireless LANs, and cellular networks, authentication, authorization, accountability, and access controls of computer networks, firewalls, Intrusion Detection and Prevention Systems, Sandboxing, proxies, study of diverse attack types: DDoS, spoofing, flooding, hijacking, poisoning, DNS, replay attacks and their countermeasures. Hands-on experiences in network design, management and security.

Host: COE

5. Data Science and Analytics

This interdisciplinary program focuses on the analysis and handling of data from multiple sources and for various applications in order to draw inferences from it, combining topics from mathematics, statistics, and computer science. These topics include probability theory, inference, least-square estimation, maximum likelihood estimation, finding local and global optimal solutions (gradient descent, genetic algorithms, etc.), and generalized additive models. It also covers machine learning topics such as classification, conditional probability estimation, clustering, and dimensionality reduction (e.g. discriminant factor and principal component analyses), and decision support systems. The program also covers big data analysis, including big data collection, preparation, preprocessing, warehousing, interactive visualization, analysis, scrubbing, mining, management, modeling, and tools such as Hadoop, Map-Reduce, Apache Spark, etc.

Host: MATH

6. Decision Analytics

The interdisciplinary field of Decision Analytics (DA) seeks to understand and improve the judgment and decision making of individuals, groups, and organizations. Decision Analytics is grounded in theories and methods drawn from mathematics, probability and statistics, operations research, optimization, and artificial intelligence-based tools such as machine learning. The knowledge of this multidisciplinary area can be applied almost everywhere including government, manufacturing, design, health care, transportation, city planning, and business. The Systems Engineering department proposes a concentration in DA with the aim to equip students with the knowledge and skills for scientific decision making. The concentration consists of four courses taught by the systems Engineering Department, Mathematics and Information and Computer Sciences. The courses are Decision Making, Intelligent Decision Support Systems, Applied Game Theory and Cases in Decision Analytics.

Host: ISE

7. Internet of Things

This interdisciplinary program covers smart applications built using smart systems capable of sensing, actuation, computing, and communication. In this concentration, students learn how to use smart systems to develop fascinating applications such as those used in smart homes, smart cities, intelligent transportation systems, and more. Topics covered include IoT applications, embedded systems and sensing, IoT communication protocols, Industrial Internet of Things (IIoT), cloud and edge computing, big data analytics, and IoT security. Students are introduced to embedded systems programming and interfacing. Students also learn how to connect smart things to each other, as well as to the cloud. Through learning big data analytics, students can use advanced analytics and machine learning to process sensor data and build innovative applications. Students are also exposed to how IIoT is used in industrial applications using state-of-the-art use cases.

Host: COE

8. Quantum Information & Computing

This interdisciplinary program covers an emerging discipline in computing that utilizes quantum theory and how to apply it in the fields of computing and communication. The program covers the concepts of qubits, superposition, entanglement, quantum gates, and quantum algorithms in order to understand the difference between classical and quantum computing. Other topics include quantum electrodynamics, including cavity and circuit qubits, quantum superconductivity, non-linear harmonic oscillators, etc. Students are introduced to quantum computing concepts such as quantum hardware, processors, circuits, instruction sets, quantum programming languages, quantum error correction, algorithms, and quantum cryptography. Students learn how to design, simulate, and test the core parts of a superconducting Qubit.

Host: PHYS