C. A. SHUE, HEAD
G. T. Heineman, ASSOCIATE HEAD
PROFESSORS: E. Agu, M. Claypool, N. Heffernan, D. Korkin, C. Ruiz, E. A. Rundensteiner, G. N. Sarkozy, C. A. Shue, C. E. Wills
ASSOCIATE PROFESSORS: J. E. Beck, J. Dai, T. Guo, L. T. Harrison, G. T. Heineman, X. Kong, K. Lee, Y. Li, X. Liu, G. M. Smith, E. Solovey, X. Sun, R. J. Walls, J. R. Whitehill
ASSISTANT PROFESSORS: R. Bohrer, F. Murai, D. Reichman, C. D. Roberts, R. Shraga, A. Yousefi, H. Zhan
PROFESSOR OF TEACHING: R. Neamtu
ASSOCIATE TEACHING PROFESSORS: S. Arslan Ay, W. Wong
ASSISTANT TEACHING PROFESSORS: M. Ahrens, M. Engling, J. Mortensen, Y. Sun, S. Taneja
SENIOR INSTRUCTOR: J. M. Cuneo
INSTRUCTOR: T. Andrews
PROFESSORS EMERITUS: D. C. Brown, D. J. Dougherty, D. Finkel, M. Hofri, R. E. Kinicki, K. A. Lemone, S. M. Selkow
ASSOCIATED FACULTY: S. Barton (HUA), B. Calli (RBE), L. Fichera (RBE), T. Ghoshal (DS), X. Huang (ECE), B. Islam (ECE), A. Lammert (MA), Z. Li (RBE), O. Mangoubi (MA), W. Martin (MA), B. Michalson (RBE), K. Mus (ECE), C. Ngan (DS), R. Paffenroth (MA), K. Pahlavan (ECE), C. Pinciroli (RBE), N. Sanket (RBE), P. Schaumont (ECE), A. Wyglinski (ECE), J. Xiao (RBE), H. Zhang (BME), Z. Zhang (ECE)
The mission of the Computer Science Department at WPI is to provide outstanding education to its undergraduate and graduate students in accordance with the principles of the WPI mission, to advance scholarship in key domains of the computing sciences, and to engage in activities that improve the welfare of society and enhance the reputation of WPI. The Department aims to maintain an environment that promotes innovative thinking, values mutual respect and diversity, encourages and supports scholarship, instills ethical behavior, and engenders life-long learning.
Program Educational Objectives
In support of its goals and mission, the WPI Computer Science undergraduate program’s educational objectives are to graduate students who will
- achieve professional success due to their mastery of Computer Science theory and practice;
- become leaders in business, academia, and society due to a broad preparation in mathematics, science & engineering, communication, teamwork, and social issues;
- pursue lifelong learning and continuing professional development;
- use their understanding of the impact of technology on society for the benefit of humankind.
Based on the educational objectives, the specific educational outcomes for the WPI Computer Science undergraduate program are that by the time of graduation CS majors will have achieved
- an understanding of programming language concepts;
- knowledge of computer organization;
- an ability to analyze computational systems;
- knowledge of computer operating systems;
- an understanding of the foundations of computer science;
- an understanding of software engineering principles and the ability to apply them to software design;
- an understanding of human-computer interaction;
- completion of a large software project;
- knowledge of advanced computer science topics;
- an understanding of mathematics appropriate for computer science;
- knowledge of probability and statistics;
- an understanding of scientific principles;
- an ability to design experiments and interpret experimental data;
- an ability to undertake independent learning;
- an ability to locate and use technical information from multiple sources;
- an understanding of professional ethics;
- an understanding of the links between technology and society;
- an ability to participate effectively in a class or project team;
- an ability to communicate effectively in speech;
- an ability to communicate effectively in writing.
Computer Science Major,Bachelor of Science
This course will use interactive visualization to model and analyze biological information, structures, and processes. Topics will include the fundamental principles, concepts, and techniques of visualization (both scientific and information visualization) and how visualization can be used to study bioinformatics data at the genomic, cellular, molecular, organism, and population levels. Students will be expected to write small- to moderately-sized programs to experiment with different visual mappings and data types. This course will be offered in 2022-23, and in alternating years thereafter.
CS 2102 or CS 2103, CS 2223, and one or more biology courses.
This course will investigate computational techniques for discovering patterns in and across complex biological and biomedical sources including genomic and proteomic databases, clinical databases, digital libraries of scientific articles, and ontologies. Techniques covered will be drawn from several areas including sequence mining, statistical natural language processing and text mining, and data mining. This course will be offered in 2021-22, and in alternating years thereafter.
CS 2102 or CS 2103, CS 2223, MA 2610 or MA 2611, and one or more biology courses.
This course introduces students to the fundamental principles of programming in imperative and scripting languages. Topics include control structures, iterators, functional decomposition, and basic data structures (such as records). Students will be expected to implement, test, and debug programs. Through the use of compelling applications and lab exercises, students will learn how to interface with external data systems and control devices.
none. All Computer Science students and other students wishing to prepare for 3000-level courses in Computer Science should take CS 1101/1102 instead of CS 1004. This course provides sufficient background for CS 2301 Systems Programming for Non-Majors.
This course introduces principles of computation and programming with an emphasis on program design. Topics include the design, implementation, and testing of programs that use a variety of data structures (such as structures, lists, and trees), functions, conditionals, recursion, and higher-order functions. Students will be expected to design simple data models, and implement and debug programs in a functional programming language.
none. Either CS 1101 or CS 1102 provides sufficient background for further courses in the CS department. Undergraduate credit may not be earned for both this course and CS 1102.
In the first half of the term, this course covers the same functional programming material as CS 1101 at roughly twice the pace. The second half of the term is a preview of selected advanced Computer Science topics, such as the design and implementation of application-specific languages, macros, programming with the HTTP protocol, and continuation-passing style. Students will be expected to complete an open-ended individual programming project.
Substantial prior programming experience (including functions, recursion, and lists, as would be covered in high-school Advanced Placement Computer Science A courses, but not necessarily AP CS Principles courses). Either CS 1101 or CS 1102 provides sufficient background for further courses in the CS department. Undergraduate credit may not be earned for both this course and CS 1101.
This course introduces students to the structure and behavior of modern digital computers and the way they execute programs. Machine organization topics include the von Neumann model of execution, functional organization of computer hardware, the memory hierarchy, caching performance, and pipelining. Assembly language topics include representations of numbers in computers, basic instruction sets, addressing modes, stacks and procedures, low-level I/O, and the functions of compilers, assemblers, linkers, and loaders. The course also presents how code and data structures of higher-level languages are mapped into the assembly language and machine representations of a modern processor. Programming projects will be carried out in the C language and the assembly language of a modern processor.
CS 2301 or CS 2303, or a significant knowledge of C/C++.
This course serves as an introduction to some of the more important concepts, techniques, and structures of discrete mathematics providing a bridge between computer science and mathematics. Topics include sets, functions and relations, propositional and predicate calculus, mathematical induction, properties of integers, counting techniques, and graph theory. Students will be expected to develop simple proofs for problems drawn primarily from computer science and applied mathematics.
This course introduces students to an object-oriented model of programming. Building from the design methodology covered in CS 1101/CS 1102, this course shows how programs can be decomposed into classes and objects. By emphasizing design, this course shows how to implement small defect-free programs and evaluate design decisions to select an optimal design under specific assumptions. Topics include inheritance, exceptions, interface, design by contract, basic design patterns, and reuse. Students will be expected to design, implement, and debug object-oriented programs composed of multiple classes and over a variety of data structures.
CS 1101 or CS 1102.
This course covers the data structures and general program-design material from CS2102, but assumes that students have significant prior experience in object-oriented programming. The course covers object-oriented design principles and data structures more deeply and at a faster pace than in CS 2102. Students will be expected to design, implement, test, debug, and critique programs both for correctness and adherence to good object-oriented design principles. The course is designed to strengthen both the design skills and algorithmic thinking of students who already have a foundation in object-oriented programming. Advanced Placement Computer Science A courses should provide sufficient background; students from AP CS Principles courses or gentler introductions to Java Programming are advised to take CS2102 instead. Students may receive credit for only one of the following three courses: CS 2102, CS 2103, CS 210X.
CS 1101 or CS 1102 and significant prior experience writing object-oriented programs from scratch.
This course introduces students to an object-oriented model of programming, with an emphasis on the programming approaches useful in creating software applications. Students will be expected to design, implement, and debug object-oriented programs. Topics include inheritance, user interfaces, and database access. This course is for non-CS majors with prior programming experience and an interest in building software applications.
Some programming experience such as found in CS 1101, CS 1102, or CS 1004.
Building on a fundamental knowledge of data structures, data abstraction techniques, and mathematical tools, a number of examples of algorithm design and analysis — worst case and average case — will be developed. Topics include greedy algorithms, divide-and-conquer, dynamic programming, heuristics, and probabilistic algorithms. Problems will be drawn from areas such as sorting, graph theory, and string processing. The influence of the computational model on algorithm design will be discussed. Students will be expected to perform analysis on a variety of algorithms.
CS 2102 or CS 2103, and CS 2022.
This course introduces the C programming language and system programming concepts to non-CS majors who need to program computers in their own fields. The course assumes that students have had previous programming experience. It quickly introduces the major concepts of the C language and covers manual memory management, pointers and basic data structures, the machine stack, and input/output mechanisms. Students will be expected to design, implement, and debug programs in C. All Computer Science students and other students wishing to prepare for upper-level courses in Computer Science should take CS 2303 instead of CS 2301. Students who have credit for CS 2303 may not receive subsequent credit for CS 2301.
CS 1101, CS 1102, or CS 1004 or previous experience programming a computer.
This course introduces students to a model of programming where the programming language exposes details of how the hardware stores and executes software. Building from the design concepts covered in CS 2102, this course covers manual memory management, pointers, the machine stack, and input/ output mechanisms. The course will involve large-scale programming exercises and will be designed to help students confront issues of safe programming with system-level constructs. The course will cover several tools that assist programmers in these tasks. Students will be expected to design, implement, and debug programs in C++ and C. The course presents the material from CS 2301 at a fast pace and also includes C++ and other advanced topics.
CS 2102, CS 2103, or CS 2119 and/or substantial object-oriented programming experience.
This course provides the student with an understanding of the basic components of a general-purpose operating system. Topics include processes, process management, synchronization, input/output devices and their programming, interrupts, memory management, resource allocation, and an introduction to file systems. Students will be expected to design and implement a large piece of system software in the C programming language. Undergraduate credit may not be earned both for this course and for CS 502.
CS 2303 or CS 2301, and CS 2011.
This course develops in the student an understanding of the nature and importance of problems concerning the efficiency and effectiveness of human interaction with computer-based systems. Topics include the design and evaluation of interactive computer systems, basic psychological considerations of interaction, interactive language design, interactive hardware design, and special input/output techniques. Students will be expected to complete several projects. A project might be a software evaluation, interface development, or an experiment.
CS 2102, CS 2103, or CS 2119.
This course makes the student aware of the social, moral, ethical, and philosophical impact of computers and computer-based systems on society, both now and in the future. Topics include major computerbased applications and their impact, human machine relationships, and the major problems of controlling the use of computers. This course is recommended for juniors and seniors.
a general knowledge of computers and computer systems.
This course introduces the theoretical foundations of computer science. These form the basis for a more complete understanding of, and proficiency in computer science. Topics include computational models, formal languages, and an introduction to computability and complexity theory, including NP-completeness. Students will be expected to complete a variety of exercises and proofs. Undergraduate credit may not be earned for both this course and for CS 5003. Students who have credit for CS 4123 may not receive credit for CS 3133.
Discrete Mathematics (CS 2022 or equivalent), and Algorithms (CS 2223 or equivalent).
This course introduces the student to the design, use, and application of database management systems. Topics include the relational data model, relational query languages, design theory, and conceptual data design and modeling for relational database design. Techniques that provide for data independence and minimal redundancy will be discussed. Students will be expected to design and implement database system applications. Undergraduate credit may not be earned both for this course and for CS 4431 or CS 542.
CS 2022 and either CS 2102, CS 2103, or CS 2119.
This course provides a broad view of computer networks. The course exposes students to all seven layers of OSI Reference Model while providing an introduction into newer topics such as wireless networking and Internet traffic concerns. The objective is to focus on an understanding of fundamental concepts of modern computer network architecture from a design and performance perspective. Topics covered include physical layer considerations, network protocols, wide area networks, local area networks, wireless networks, switches and routing, congestion, Internet traffic, and network security. Students will be expected to do extensive systems/network programming and will be expected to make use of simulation and measurement tools to gain an appreciation of current network design and performance issues. This course is also highly recommended for RBE and IMGD majors.
CS 2301 or CS 2303, or a significant knowledge of C/C++.
This course introduces the fundamental principles of software engineering. Modern software development techniques and life cycles are emphasized. Topics include requirements analysis and specification, analysis and design, architecture, implementation, testing and quality, configuration management, and project management. Students will be expected to complete a project that employs techniques from the topics studied. This course should be taken before any course requiring a large programming project. Undergraduate credit may not be earned both for this course and for CS 509.
CS 2102, CS 2103, or CS 2119.
This course provides an introduction to modern computational methods for linear and nonlinear equations and systems and their applications. Topics covered include solution of nonlinear scalar equations, direct and iterative algorithms for the solution of systems of linear equations, solution of nonlinear systems, and the eigenvalue problem for matrices. Error analysis will be emphasized throughout.
MA 2071. An ability to write computer programs in a scientific language is assumed.
This course provides an introduction to modern computational methods for differential and integral calculus and differential equations. Topics covered include interpolation and polynomial approximation, approximation theory, numerical differentiation and integration, and numerical solutions of ordinary differential equations. Error analysis will be emphasized throughout.
MA 2051. An ability to write computer programs in a scientific language is assumed. Undergraduate credit may not be earned for both this course and for MA 3255/CS 4031.
Instances of this course will explore advanced and emerging topics that are not covered by the current regular CS offerings. Content and format will vary to suit the interests and needs of the faculty and students. This course may be repeated for credit as topics change.
Algorithms and programming techniques from artificial intelligence (AI) are key contributors to the experience of modern computer games and interactive media, either by directly controlling a non-player character (NPC) or through more subtle manipulation of the environment. This course will focus on the practical AI programming techniques currently used in computer games for NPC navigation and decision-making, along with the design issues that arise when AI is applied in computer games, such as believability and real-time performance. The course will also briefly discuss future directions in applying AI to games and media. Students will be expected to complete significant software development projects using the studied techniques. This course will be offered in 2021-22, and in alternating years thereafter.
Object-oriented design concepts (CS 2102 or CS 2103), algorithms (CS 2223), and knowledge of technical game development (IMGD 3000 or IMGD 4000).
This course develops the skill of analyzing the behavior of algorithms. Topics include the analysis — with respect to average and worst case behavior — and correctness of algorithms for internal sorting, pattern matching on strings, graph algorithms, and methods such as recursion elimination, dynamic programming, and program profiling. Students will be expected to write and analyze programs. Undergraduate credit may not be earned both for this course and for CS 5084. This course will be offered in 2022-23, and in alternating years thereafter.
Algorithms (CS 2223 or equivalent), and some knowledge of probability.
Building on the preliminaries established in CS 3133, this course explores fundamental questions of computability and complexity. Emphasis is on both mathematical foundations and applications to computing practice. Topics include the Church-Turing thesis, the halting problem, NP-completeness, time and space complexity classes, and related material as determined by the instructor. Students will be expected to read and write mathematical proofs.
This Software Engineering course will focus on the process of Object-Oriented Analysis and Design. Students will be expected to complete a large number of exercises in Domain Modeling, Use Case Analysis, and Object-Oriented Design. In addition, the course will investigate Design Patterns, which are elements of reusable object-oriented software designs. This course will survey a set of design patterns and consider how these patterns are described and used to solve design problems. This course will be offered in 2022-23, and in alternating years thereafter.
CS 2303 and CS 3733.
This course explores the computational aspects of network information systems as embodied by the World Wide Web (WWW). Topics include languages for document design, programming languages for executable content, scripting languages, design of WWW based human/computer interfaces, client/server network architecture models, high level network protocols (e.g., http), WWW network resource discovery and network security issues. Students in this course will be expected to complete a substantial software project (e.g., Java based user interface, HTML/CGI based information system, WWW search mechanisms).
CS 2102, CS 2103, or CS 2119; and CS 3013.
This course trains students to create accelerated simulations using Graphics Processing Unit (GPU) programming techniques, and to render the output of these simulations in aesthetically interesting ways. The aesthetic focus of the course is grounded by examining the histories of experimental animation, video synthesis, and the use of simulation in the digital arts. Students will evaluate the effectiveness of GPU-accelerated techniques for a variety of simulations and will create their own aesthetic explorations of appropriate simulations throughout the course.
This course studies the problem of making computers act in ways which we call “intelligent”. Topics include major theories, tools and applications of artificial intelligence; aspects of knowledge representation; searching and planning; and natural language understanding. Students will be expected to complete projects which express problems that require search in state spaces and to propose appropriate methods for solving the problems. Undergraduate credit may not be earned both for this course and for CS 534.
CS 2102 or CS 2103; CS 2223; and CS 3133.
In this course, students will explore both theoretical and practical aspects of machine learning, including algorithms for regression, classification, dimensionality reduction, clustering, and density estimation. Specific topics may include neural networks and deep learning, Bayesian networks and probabilistic graphical models, principal component analysis, k-means clustering, decision trees and random forests, support vector machines, and kernel methods.
Multivariate Calculus (MA 1024 or MA 1034), Linear Algebra (such as MA 2071), Probability (MA 2621 or MA 2631), and Algorithms (CS 2223). Students may not earn credit for both CS 453X and CS 4342. Undergraduate credit may not be earned both for this course and for CS 539.
This course provides an introduction to the pitfalls and practices of building secure software applications. Topics will include threat modeling, secure software development, defensive programming, web security, and the interaction between security and usability. The course focuses on the application level with minor attention to operating-system level security; network-level security is not covered. Assignments involve designing and implementing secure software, evaluating designs and systems for security-related flaws, and presentations on security issues or tools. All students will be required to sign a pledge of responsible conduct at the start of the course.
CS3013 and CS3733. The course assumes nontrivial experience with C and Unix, familiarity with operating systems, filesystems, and databases, and experience with technologies for building web applications (from CS4241 or personal experience).
This course introduces students to modern network security concepts, tools, and techniques. The course covers security threats, attacks, and mitigations at the operating-system and network levels (as opposed to the software level). Topics include authentication, authorization, confidentiality, integrity, anonymity, privacy, intrusion detection and response, and cryptographic applications. Students will become familiar with modern security protocols and tools. Assignments will involve using security-testing software to uncover vulnerabilities, network packet analyzers, and existing security applications to create secure network implementations. The course requires enough programming and systems background to understand attacks and use systems tools but does not involve significant programming projects. Assignments and projects will use a Linux base for implementation.
Knowledge of operating systems (CS 3013 or equivalent) and computer networks (CS 3516 or equivalent). Familiarity with Linux or Unix is essential.
This course concentrates on the study of the internals of database management systems. Topics include principles and theories of physical storage management, advanced query languages, query processing and optimization, index structures for relational databases, transaction processing, concurrency control, distributed databases, and database recovery, security, client server and transaction processing systems. Students may be expected to design and implement software components that make up modern database systems. Undergraduate credit may not be earned both for this course and CS 542. This course will be offered in 2021-22, and in alternating years thereafter.
CS 3431 and CS 3733.
This course introduces the emerging techniques and infrastructures for big data management and analytics including parallel and distributed database systems, map-reduce, Spark, and NoSQL infrastructures, data stream processing systems, scalable analytics and mining, and cloud-based computing. Query processing and optimization, access methods, and storage layouts developed on these infrastructures will be covered. Students are expected to engage in hands-on projects using one or more of these technologies.
Knowledge in database systems at the level of CS4432, and programming experience are assumed.
This course provides an introduction to Knowledge Discovery in Databases (KDD) and Data Mining. KDD deals with data integration techniques and with the discovery, interpretation, and visualization of patterns in large collections of data. Topics covered in this course include data warehousing and mediation techniques; data mining methods such as rule-based learning, decision trees, association rules, and sequence mining; and data visualization. The work discussed originates in the fields of artificial intelligence, machine learning, statistical data analysis, data visualization, databases, and information retrieval. Several scientific and industrial applications of KDD will be studied. This course will be offered in 2021-22, and in alternating years thereafter.
MA 2611, CS 2223, and CS 3431 or CS 3733.
This course extends the study of the design and implementation of operating systems begun in CS 3013 to distributed and advanced computer systems. Topics include principles and theories of resource allocation, file systems, protection schemes, and performance evaluation as they relate to distributed and advanced computer systems. Students may be expected to design and implement programs that emphasize the concepts of file systems and distributed computing systems using current tools and languages. Undergraduate credit may not be earned both for this course and for CS 502. This course will be offered in 2021-22, and in alternating years thereafter.
CS 3013, CS 3516, and system programming experience.
This course explores the architectural design of modern computer systems in terms of instruction sets and the organization of processors, controllers, memories, devices, and communication links. Topics include an overview of computer architectures and system components, theoretical foundations, instruction-level and thread-level pipelining, multifunction pipelines, multi-core systems, caching and memory hierarchies, and multi-core and parallel computer organization. Students may be expected to design and implement programs that simulate significant components of modern computer architectures. This course will be offered in 2022-23, and in alternating years thereafter.
CS 2011 or ECE 2049, and CS 3013.
This course provides an in-depth look into computer networks. While repeating some of the areas from CS 3516, the goal is to go deeper into computer networks topics. This in-depth treatment in topics such as routing, congestion control, wireless layer protocols, and physical signaling considerations will require the use of basic queuing theory and probability to provide a more formal treatment of computer networks performance. Other topics covered include LAN and WLAN technologies, mobile wireless networks, sensor networks, optical networks, network security, intrusion detection, and network management. Students will be expected to do more sophisticated network programming than seen in CS 3516 and will conduct laboratory activities involving measuring the performance of modern networking applications running on both wired networks and infrastructure wireless networks. Undergraduate credit may not be earned both for this course and for CS 513. This course will be offered in 2021-22, and in alternating years thereafter.
CS 3013, CS 3516, and knowledge of probability. The course assumes a familiarity with operating systems including Unix or Linux and significant experience with C/C++.
The goal of this course is to acquaint students with fundamental concepts and state-of-the-art computer science literature in mobile and ubiquitous computing. Topics to be covered include mobile systems issues, human activity and emotion sensing, location sensing, mobile human-computer interaction, mobile social networking, mobile health, power saving techniques, energy and mobile performance measurement studies, and mobile security. The course will introduce the programming of mobile devices such as smartphones running the Android operating system. Students may not earn credit for both CS 403X and CS 4518.
Proficiency in programming in Java, including classes, inheritance, exceptions, interfaces, and polymorphism (CS 2102 or equivalent).
This course studies the compiling process for high-level languages. Topics include lexical analysis, syntax analysis, semantic analysis, symbol tables, intermediate languages, optimization, code generation, and run-time systems. Students will be expected to use compiler tools to implement the front end — and to write a program to implement the back end — of a compiler for a recursive programming language. Undergraduate credit may not be earned for both this course and for CS 544. This course will be offered in 2022-23, and in alternating years thereafter.
CS 2102 or CS 2103, and CS 3133.
This course covers the design and implementation of programming languages. Topics include data structures for representing programming languages, implementing control structures (such as functions, recursion, and exceptions), garbage collection, and type systems. Students will be expected to implement several small languages using a functional programming language. Undergraduate credit may not be earned for both this course and CS 536. This course will be offered in 2021-22, and in alternating years thereafter.
CS 2303, CS 3133, and experience programming in a functional language (as provided by CS 1101 or CS 1102).
This course studies the use of the computer to model and graphically render two- and three-dimensional structures. Topics include graphics devices and languages, 2- and 3-D object representations, and various aspects of rendering realistic images. Students will be expected to implement programs which span all stages of the 3-D graphics pipeline, including clipping, projection, arbitrary viewing, hidden surface removal, and shading. Undergraduate credit may not be earned both for this course and for CS 543.
CS 2223, CS 2303, and MA 2071.
This course provides an in-depth examination of the algorithms, data structures, and techniques used in modeling and rendering dynamic scenes. Topics include animation hardware and software; parametric blending techniques; modeling physical and articulated objects; forward and inverse kinematics; key-frame, procedural, and behavioral animation; and free-form deformation. Students will be expected to develop programs to implement low-level animation algorithms as well as use commercial animation tools to design and produce small- to moderately-sized animations. This course will be offered in 2022-23, and in alternating years thereafter.
This course provides an introduction to modern cryptography and communication security. It focuses on how cryptographic algorithms and protocols work and how to use them. The course covers the concepts of block ciphers and message authentication codes, public key encryption, digital signatures, and key establishment, as well as common examples and uses of such schemes, including the AES, RSA-OAEP, and the Digital Signature Algorithm. Basic cryptanalytic techniques and examples of practical security solutions are explored to understand how to design and evaluate modern security solutions. The course is suited for students interested in cryptography or other security related fields such as trusted computing, network and OS security, or general IT security.
Experience in expressing algorithms in a modern programming language (e.g., ECE 2049 or CS 2301). ECE 2049 (Embedded Computing in Engineering Design) or CS 2301 (Systems Programming for Non-Majors) or equivalent.
Discrete mathematics (CS 2022/MA 2201 or equivalent)
This course trains students in data visualization, the graphical communication of data and information for presentation, confirmation, and exploration. Students learn the stages of the visualization pipeline, including data characterization, mapping data attributes to graphical attributes, user task abstraction, visual display techniques, tools, paradigms, and perceptual issues. Students evaluate the effectiveness of visualizations for specific data, task, and user types. Students implement visualization algorithms and undertake projects involving the use of commercial and public-domain visualization tools.
CS 2102 or CS 2103, and CS 2223.