Computer Science (CS)

An introduction to science and engineering as a profession. Examines the problem-solving process used in engineering and science. Emphasizes the interdisciplinary and international nature of problem-solving and the need to evaluate solutions in terms of a variety of constraints: computational, financial, and social.

Introduces the use of high-level programming language as a problem-solving tool in engineering including basic data structures and algorithms, structured programming techniques, and software documentation. Designed for students who have had little or no prior experience with computer programming. Students should only take one of these courses (CS 104, CS 105, CS 115). A C or better grade in CS104 also serves as a prerequisite for CS201, for students who wish to continue their study of computer science.

Introduces the use of high-level programming language as a problem-solving tool, including basic data structures and algorithms, structured programming techniques, and software documentation. Designed for students who have had little or no prior experience with computer programming. Students should only take one of these courses (CS 104, CS 105, CS 115). A C or better grade in CS 105 also serves as a prerequisite for CS201, for students who wish to continue their study of computer science.

An introduction to the following "big ideas" of computer science: (1) computing is a creative activity; (2) abstraction reduces information and detail to facilitate focus on relevant concepts; (3) data and information facilitate the creation of knowledge; (4) algorithms are used to develop and express solutions to computational problems; (5) programming enables problem solving, human expression, and creation of knowledge; (6) the internet pervades modern computing; and (7) computing has global impacts. A C or better grade in CS110 also serves as a prerequisite for CS201, for students who wish to continue their study of computer science.

Introduces the use of a high-level object-oriented programming language as a problem-solving tool, including basic data structures and algorithms, object-oriented programming techniques, and software documentation. Designed for students who have had little or no prior experience with computer programming. For students in CS and CS-related degree programs. Students should only take one of these courses (CS 104, CS 105, CS 115).

Introduces more advanced elements of object-oriented programming, including dynamic data structures, recursion, searching and sorting, and advanced object-oriented programming techniques. For students in CS and CS-related degree programs.

This course provides an accessible, interdisciplinary introduction to artificial intelligence for students from all majors. Emphasis is placed on practical exploration rather than technical theory. Students engage with contemporary AI tools for communication, creativity, planning, and basic data-driven tasks, while developing an understanding of the capabilities and limitations of modern AI systems. The course introduces foundational concepts in AI, ethical and responsible use of AI, and the issues of privacy and security. Through weekly hands-on activities and guided discussions, students learn effective strategies for human-AI collaboration and examine how AI is influencing a wide range of academic and professional domains.

Problem-solving and design using an object-oriented programming language. Introduces a variety of problem-solving techniques, algorithms, and data structures in object-oriented programming.

Introduction to the use of formal mathematical structures to represent problems and computational processes. Topics covered include Boolean algebra, first-order logic, recursive structures, graphs, and abstract language models. Credit will not be granted for both CS 330 and MATH 230.

Implementation and application of the essential data structures used in computer science. Analysis of basic sorting and searching algorithms and their relationship to these data structures. Particular emphasis is given to the use of object-oriented design and data abstraction in the creation and application of data structures.

This course balances the imperative, object-oriented bent of the introductory programming sequence by examining fundamental programming patterns through a functional lens. Students will use Haskell to explore techniques that transcend language boundaries: composing effects with monads, managing concurrency, building domain-specific languages, and using property-based testing for correctness. Programming assignments drawn from various domains will help students recognize these patterns in modern frameworks and languages, developing the judgment to select appropriate abstractions, data structures, and techniques for the problems at hand.

Introduction to the internal architecture of computer systems, including micro-, mini-, and mainframe computer architectures. Focuses on the relationship among a computer's hardware, its native instruction set, and the implementation of high-level languages on that machine. Uses a set of assembly language programming exercises to explore and analyze a microcomputer architecture. Credit will not be granted for both CS 350 and ECE 242.

Examines the components of sophisticated multilayer software systems, including device drivers, systems software, applications interfaces, and user interfaces. Explores the design and development of interrupt-driven and event-driven software.

**Instructor permission required.**

First course in a two-course sequence that is designed to prepare students for graduate study in computer science. Explores the implementation and application of fundamental data structures and algorithms with an emphasis on object-oriented programming in Java. Examines the relationship between these elements and the mathematical structures that form the foundation of computer science. This course does not apply toward M. S./Ph. D. credit in Computer Science.

Second course in a two-course sequence that is designed to prepare students for graduate study in computer science. Explores the development of the multiple layers of software that form a sophisticated software system, from device drivers to application interfaces to user interfaces. Examines how computer architecture influences software development. Emphasizes the design and implementation of interrupt-driven/event-driven software.

This course is a six-credit hour integration of CS 401 and CS 402. The course is an introduction to data structures but is designed to expand programming skills/concepts using software development methodology techniques. The course also provides an introduction to computer architecture and systems programming including assembly language programming, event handling and multi-threading.

This course will provide students with an understanding of some basic discrete mathematics and techniques for designing computer algorithms and measuring and analyzing their behavior. We emphasize the necessary mathematical ideas such as how to prove a mathematical statement, how to analyze the worst case and the average case complexity of an algorithm. We also introduce students to some new techniques used in designing algorithms, such as the approximation algorithms and randomized algorithms.

Overview of display devices and applications. Vector graphics in two and three dimensions. Image generation, representation, and manipulation. Homogeneous coordinates. Modeling and hidden line elimination. Introduction to raster graphics. Perspective and parallel projections.

This course will provide an introduction to the field of human computer interaction. The course begins with a foundational description of the design process, the human person, and the computer. Then, students deepen and solidify their knowledge through a course-long design project. Topics in the design process include needfinding, empathy maps, experience prototypes, brainstorming, sketching, user testing, and low-, medium- and high-fidelity prototypes. Topics in the design of computers include the command line and graphical user interface, user interface design, mobile computing, ubiquitous computing, direct manipulation interfaces, and emerging interaction paradigms such as AR/VR and AI. Embedded within the course are arguments for and practice of design ethics, design justice, and accessibility.

This course will provide an introductory look at concepts and techniques in the field of data mining. After covering the introduction and terminologies to Data Mining, the techniques used to explore the large quantities of data for the discovery of meaningful rules and knowledge such as market basket analysis, nearest neighbor, decision trees, and clustering are covered. The students learn the material by implementing different techniques throughout the semester.

Overview of database architectures, including the Relational, Hierarchical, Network, and Object Models. Database interfaces, including the SQL query language. Database design using the Entity-Relationship Model. Issues such as security, integrity, and query optimization.

Overview of fundamental issues of information retrieval with theoretical foundations. The information-retrieval techniques and theory, covering both effectiveness and run-time performance of information-retrieval systems are covered. The focus is on algorithms and heuristics used to find documents relevant to the user request and to find them fast. The course covers the architecture and components of the search engine such as parser, stemmer, index builder, and query processor. The students learn the material by building a prototype of such a search engine. Requires strong programming knowledge.

Introduction to the design, behavior, and analysis of computer algorithms. Searching, sorting, and combinatorial algorithms are emphasized. Worst case, amortized, and expected bounds on time and space usage.

Study of commonly used computer programming languages with an emphasis on precision of definition and facility in use. Scanning, parsing, and introduction to compiler design. Use of compiler generating tools.

This course covers the design and implementation of a compiler for modern languages by implementing the following: abstract syntax trees; intermediate representations; static analysis; fix-point operations; symbol tables and type checking; and first-order and high-order function implementation. Students will incrementally create a series of compilers.

Introduction to methodologies for object-oriented design and programming. Examines the object model and how it is realized in various object-oriented languages. Focuses on methods for developing and implementing object-oriented systems.

Introduction to operating system concepts-including system organization for uniprocessors and multiprocessors, scheduling algorithms, process management, deadlocks, paging and segmentation, files and protection, and process coordination and communication.

This course covers general introductory concepts in the design and implementation of parallel and distributed systems covering all the major branches such as cloud computing, grid computing, cluster computing, supercomputing, and many-core computing.

Introduction to data communication concepts and facilities with an emphasis on protocols and interface specifications. Focuses on the lower four layers of the ISO-OSI reference model.

This class provides an opportunity for students to obtain a fundamental understanding of the nature and operation of the full range of wireless networks (personal, local area, wide area, and satellite) and their performance characteristics, future potential, and challenges through class lectures, assigned readings, homework, projects, and various hands-on experiences.

An introduction to the fundamentals of computer and information security. This course focuses on algorithms and techniques used to defend against malicious software. Topics include an introduction to encryption systems, operating system security, database security, network security, system threats, and risk avoidance procedures.

Introduction to the functional elements and structures of digital computers. Detailed study of specific machines at the register transfer level illustrates arithmetic, memory, I/O and instruction processing.

Introduction to computational methods for intelligent control of autonomous agents, and the use of programming paradigms that support development of flexible and reactive systems. These include heuristic search, knowledge representation, constraint satisfaction, probabilistic reasoning, decision-theoretic control, and sensor interpretation. Particular focus will be places on real-world application of the material.

Theory and programming paradigms that enable systems to understand human language texts and extract useful information and knowledge. For example, extraction of structured event representations from news stories or discovering new research hypotheses by analyzing thousands of medical research articles. the course covers a variety of text analysis and text mining methods, with an emphasis on building working systems. Connections to information retrieval, data mining, and speech recognition will be discussed.

This course explores the theory, processes, and technologies of knowledge management (KM) in organizations. Topics include the nature of knowledge, knowledge infrastructure and mechanisms, systems for capture, sharing, discovery and application of knowledge, and emerging trends such as KM in the cloud, crowdsourcing, AI, and crises. Students will analyze KM solutions, evaluate system and process design, and understand how KM supports organizational performance.

An introduction to machine learning concepts and algorithms, including classification, clustering, and regression. Topics include k-means clustering, nearest neighbors classification, decision trees, naive Bayes, logistic regression, support vector machines, and neural networks. Special focus will be on practical aspects of machine learning, including data preparation, experimental design, and modern tools for building machine learning systems. Basic probability theory knowledge is required.

Discussion of the impact of computer technology on present and future society. Historical development of the computer. Social issues raised by cybernetics.

Study of the principles and practices of software engineering. Topics include software quality concepts, process models, software requirements analysis, design methodologies, software testing and software maintenance. Hands-on experience building a software system using the waterfall life cycle model. Students work in teams to develop all life cycle deliverables: requirements document, specification and design documents, system code, test plan, and user manuals.

**Instructor permission required.**

Prepares undergraduate computer science majors for conducting research.

This course will treat a specific topic, varying from semester to semester, in which there is particular student or staff interest.

Special projects.

Covers advanced topics in computer graphics. The exact course contents may change based on recent advances in the area and the instructor teaching it. Possible topics include: Geometric modeling, Subdivision surfaces, Procedural modeling, Warping and morphing, Model reconstruction, Image based rendering, Lighting and appearance, Texturing, Natural phenomena, Nonphotorealistic rendering Particle systems, Character animation, Physically based modeling and animation.

Introduction to fundamental topics in computer vision and the application of deep neural networks to this area. Intended to give the student a good basis for work in this important field. Topics include: Geometric image formation, Feature extraction, Object recognition, Object detection, Semantic segmentation, Probabilistic modeling, Camera calibration, Epipolar geometry, Model reconstruction, Optical flow, Motion estimation.

This course introduces the principles and techniques for designing and developing immersive interactive systems that blend digital content with the physical world or create fully virtual environments. The course covers core concepts in 3D graphics, spatial computing, tracking, and user interaction, along with the hardware and software platforms used in AR/VR systems. Students gain hands-on experience building applications that integrate real-time rendering, sensing, and interaction. Topics include scene representation, camera and sensor integration, user interfaces for immersive environments, and performance considerations for real-time systems. The course also addresses usability, human perception, and design challenges in creating effective and engaging AR/VR experiences.

This course introduces the design and implementation of AI systems that can autonomously plan, reason, and act to complete tasks. The course focuses on building agents using modern AI tools, including large language models, tool integration, and retrieval-augmented generation (RAG). Students gain hands-on experience developing workflows that connect AI systems with external resources such as APIs, databases, and software tools. Topics include agent design patterns, task decomposition, memory and context management, and evaluation of agent performance. The course emphasizes reliability, safety, and practical deployment considerations. Through projects, students build and evaluate agent-based systems for real-world applications such as automation, information retrieval, and decision support.

This course studies foundation models and generative AI systems, with an emphasis on their role as general-purpose components in modern computing systems. Rather than focusing on traditional natural language processing tasks, the course examines how large-scale pretrained models are integrated into real- world applications and workflows. Topics include transformer-based architectures (overview), pretraining and scaling principles, and practical methods for adapting and deploying foundation models. The course emphasizes system-level design, including prompting as an interface, retrieval-augmented generation (RAG), embeddings as infrastructure for search and memory, and tool use within AI-driven pipelines. Students also explore evaluation, reliability, and safety challenges, including hallucination, grounding, and bias. Through hands-on projects and analysis of real-world systems, students learn to design, implement, and evaluate generative AI applications, focusing on performance, scalability, and responsible deployment.

This course introduces the principles and protocols for transmitting audio, video, and interactive media over networks, with an emphasis on performance, quality, and scalability. The course covers core topics in multimedia networking, including compression, streaming architectures, transport protocols, congestion control, and quality of service (QoS) and quality of experience (QoE). An emphasis is placed on interactive and real-time communication systems, including video conferencing and conversational AI where large language models (LLMs) participate as media endpoints, introducing new constraints on end-to-end latency. In addition, the course explores how data-driven and AI-enabled techniques can enhance multimedia delivery. Topics include adaptive bitrate selection, content-aware encoding, and QoE prediction using machine learning models. The course combines networking fundamentals with modern techniques for improving efficiency and user experience in multimedia applications.

This course introduces the basic concepts of data integration, data warehousing, and provenance. We will learn how to resolve structural heterogeneity through schema matching and mapping. The course introduces techniques for querying several heterogeneous datasources at once (data integration) and translating data between databases with different data representations (data exchange). Furthermore, we will cover the data-warehouse paradigm including the Extract-Transform-Load (ETL) process, the data cube model and its relational representations (such as snowflake and star schema), and efficient processing of analytical queries. This will be contrasted with Big Data analytics approaches that (besides other differences) significantly reduce the upfront cost of analytics. When feeding data through complex processing pipelines such as data exchange transformations or ETL workflows, it is easy to lose track of the origin of data. Therefore, in the last part of the course we cover techniques for representing and keeping track of the origin and creation process of data (its provenance). The course emphasizespractical skills through a series of homework assignments that help students develop a strong background in data integration systems and techniques. At the same time, it also addresses the underlying formalisms. For example, we will discuss the logic based languages used for schema mapping and the dimensional data model as well as their practical application (e.g., developing an ETL workflow with rapid miner and creating a mapping between two example schemata). The literature reviews will familiarize students with data integration and provenance research.

This course introduces principles and techniques for developing software systems with strong guarantees of correctness, reliability, and security. The course focuses on methods for designing, implementing, and verifying software that operates safely in critical and adversarial environments. Topics include formal specification, verification and validation techniques, secure coding practices, threat modeling, and system-level assurance. Students gain experience applying tools and methodologies to identify vulnerabilities, reason about system behavior, and ensure compliance with safety and security requirements. The course also addresses trade-offs between assurance, performance, and development complexity in real-world systems.

Continued exploration of data mining algorithms. More sophisticated algorithms such as support vector machines will be studied in detail. Students will continuously study new contributions to the field. A large project will be required that encourages students to push the limits of existing data mining techniques.

This course explores the emerging interface between cryptography and AI. Its central focus is on cryptographic approaches to AI, asking how the foundations of modern cryptography can help us understand, formulate, and achieve privacy, trust, and safety goals for AI systems as they become increasingly embedded in everyday life and public discourse. Topics include cryptographic techniques for privacy-preserving and secure AI, as well as methods for analyzing and exposing vulnerabilities in AI systems. Topics include but are not limited to privacy-preserving training and inference using multi-party computation and homomorphic encryption, watermarking methods for protecting models and AI-generated content, model verification via proof systems, and the broader use of a cryptographic mindset to assess security challenges in the AI era. We will explore these topics through critical reading of recent research papers, along with discussion of open problems and future directions.

Comprehensive coverage of the problems involved in database system implementation and an in-depth examination of contemporary structures and techniques used in modern database management systems. Teaches advanced skills appropriate for DBMS architects and developers , database specialist, and the designers and developers of client/server and distributed systems. Focus is on transaction management, database structures and distributed processing.

This course introduces the principles and technologies underlying decentralized digital systems and cryptocurrencies. The course covers the design of blockchain architectures, including consensus mechanisms, distributed ledgers, and smart contracts. Students develop an understanding of how cryptographic techniques enable security, transparency, and trust in decentralized environments. Topics include blockchain protocols, token economics, transaction validation, and scalability challenges. The course also explores practical applications and limitations of blockchain systems, along with considerations for security, privacy, and real-world deployment.

From traditional PCs, mobile devices to IoT devices, software is literally ubiquitous in our everyday life. That is why the study of software security has become essential for defeating malware, preserving user privacy and protecting (intellectual and financial) properties. In this graduate-level research-oriented course, we will explore a variety of topics, including both classic and cutting-edge topics, in the area of software security. The goal of this course is to introduce basic concepts as well as state-of-the-art techniques in this area, and let students gain hands-on experience by applying some of these techniques to solve real-world security problems.

New technologies have increasingly enabled corporations and governments to collect, analyze and share huge amount of data related to individuals. Today, the challenge is enabling the legitimate use of the collected data without violating privacy and security. In this course, we are going to analyze the fundamental models of ensuring data privacy and security, and explore potential theoretical models, algorithms, and technologies that can enhance data privacy and security in different contexts, such as market basket, search engine, location-based services, social network, cloud computing, cryptocurrencies, and smart grid. We will also design and implement different techniques (e.g., cryptographic protocols, secure computation, and data sanitization) as well as examine their performance in terms of three critical properties (privacy/security, utility and efficiency).

The course covers the advanced topics in Information Retrieval. The topics such as Summarization, cross-lingual, Meta-Search, Question Answering, Parallel and distributed IR systems are discussed. The students get involved in research ideas, and get involved in individual and group projects.

Computability topics such as Turing machines, nondeterministic machines, undecidability, and reducibility. Computational complexity topics such as time complexity, NP-completeness and intractability, time and space hierarchy theorems. Introduces the complexity classes P, NP, NL, L, PSPACE, NC, RNC, BPP and their complete problems.

Topics selected from mathematical systems and automata theory, decision problems, realization and minimization, algebraic decomposition theory and machines in a category.

This course provides an introduction to the theory of formal languages and machines. Topics to be covered include: strings, alphabets, and languages; grammars, the Chomsky Hierarchy of languages and corresponding machines (regular sets and finite automata, context free languages of various types, Turing machines and recursive functions, undecidable problems), and computational complexity, polynomial-time reductions, NP-completeness.

This course covers fundamental algorithms and data structures for convex hulls, Voronoi diagrams, Delauney triangulation, Euclidean spanning trees, point location, and range searching. Also included are lower bounds and discrepancy theory. Optimization in geometry will be covered. This includes fixed dimensional linear programming and shortest paths. Graphic data structures such as BSP trees will be covered.

Many useful features of modern programming languages, such as anonymous functions (lambdas), generics, and ownership, have arisen out of research into the fundamentals of programming languages. This course will introduce students to those fundamentals: students will learn the tools and techniques used by programming languages researchers to model programs, type systems, and the design choices made in the creation of programming languages. There will be a particular focus on type systems, which provide many programming languages with a certain guarantee of runtime safety: usually that type errors will not occur at runtime, but more advanced type systems can guarantee information security, data-race-freedom and many other desirable properties. Students will see how type systems are designed, and how these safety properties of typed programs are proven. The course will also explore some applications of type systems in modern programming language research and practice.

Design of efficient algorithms for a variety of problems, with mathematical proof of correctness and analysis of time and space requirements. Topics include lower bounds for sorting and medians, amortized analysis of advanced data structures, graph algorithms (strongly connected components, shortest paths, minimum spanning trees, maximum flows and bipartite matching) and NP-Completeness.

Formal specification of how programs execute operational semantics , how mathematical functions programs compute denotational semantics , and how to use logic to characterize properties and invariants of the program execution (axiomatic semantics).

Machine learning (ML), or artificial intelligence (AI) more broadly, has achieved significant breakthroughs in academia and industry, profoundly impacting everyday life. At the same time, ML/AI systems can introduce serious security and privacy risks and are vulnerable to adversarial attacks. In addition, these systems may exhibit bias and lack interpretability. In this course, we will study vulnerabilities of ML/AI systems and methods to mitigate them, as well as how adversaries can leverage ML/AI for security and privacy attacks and how to defend against them. We will also discuss bias in ML systems and approaches for improving interpretability.

Linear programs and their properties. Efficient algorithms for linear programming. Network flows, minimum cost flows, maximum matching, weighted matching, matroids.Prerequisite:CS 430 and a linear algebra course.

This course focuses on computational issues in the theory of games, economics, and network design. Interest in the algorithmic aspects of games is motivated by the computational issues of fundamental aspects of games and economic theory, e.g. Nash equilibrium and market equilibrium. Computing and approximating Nash equilibrium will be studied. Of considerable interest to the computer science community are problems that arise from the Internet and computer networks and are similar to issues that arise in traditional transport networks, e.g. Wardrop equilibrium.

This course introduces the fundamental optimization techniques that underpin modern artificial intelligence and machine learning methods. The course covers continuous and discrete optimization approaches used in model training and decision-making, including gradient-based methods, convex optimization, and constrained optimization. Students develop an understanding of how optimization drives learning in neural networks and other models, and how problem structure influences algorithm design and performance. The course emphasizes practical implementation and analysis of optimization algorithms, along with their role in improving efficiency, scalability, and solution quality in AI systems.

Advanced topics in compiler construction, including incremental and interactive compiling, error correction, code optimization, models of code generators, etc. The objective of the course is to provide an in-depth coverage of compiler optimization techniques, including both classical optimization and areas of current interest in compiler research.

This course focuses on the engineering and analysis of network protocols and architecture in terms of the Internet. Topics include content distribution, peer-to-peer networking, congestion control, unicast and multicast routing, router design, mobility, multimedia networking quality of service, security and policy-based networking.

Many important services—including Internet services many of us use—operate over networks that support reconfigurable, fine-grained processing of traffic, and are said to be "software-defined". Software-Defined Networks (SDNs) enable the deployment of larger and richer network services, but they also introduce new technical challenges. This course provides an overview of SDN concepts and techniques. It will teach practical skills for SDN engineering, and will prepare students for careers in designing and operating different kinds of networks.

Qualitative and quantitative analysis of networks. A combination of analytical and experimental analysis techniques will be used to study topics such as protocol delay, end-to-end network response time, intranet models, Internet traffic models, web services availability, and network management.

This course introduces the principles and practices of efficient machine learning systems, with emphasis on reducing the cost of training and inference across software and hardware. Topics include model compression and adaptation methods such as quantization, pruning, distillation, and low-rank adaptation; ML systems techniques such as LLM KV caching and speculative decoding, and efficient GNN systems; and accelerator architectures including GPUs, TPUs, NPUs, and specialized AI hardware. The course combines lectures, research paper discussions, student presentations, and a semester project. Students will learn to analyze performance bottlenecks, evaluate trade-offs among accuracy, latency, memory, and energy, and apply reproducible methods to the design of efficient ML workloads and systems.

This course covers general issues of parallel and distributed processing from a user's point of view which includes system architectures, programming, performance evaluation, applications, and the influence of communication and parallelism on algorithm design.

This course introduces cellular/PCS systems, short-range mobile wireless systems, fixed wireless systems, satellites, and ad hoc wireless systems. It explains in detail the underlying technology as well as regulations, politics, and business of these wireless communications systems. It looks beyond the hype, examining just what is and is not possible with present-day and future wireless systems. As an advanced graduate course, it will combine extensive reading and in-class discussion of the research literature with in-depth independent research projects of students' own choosing.

This course examines the architectures, protocols, technologies, and systems underlying broadband (high-speed) multimedia networks. It focuses on the principles that enable efficient transmission of audio, video, and interactive media across heterogeneous network environments. The course covers representative broadband access technologies, including cable modems, Digital Subscriber Lines (DSL), wireless broadband systems, and modern cellular networks (e.g., 4G/5G). For local area networks (LANs), topics include Gigabit Ethernet, virtual LANs (VLANs), and wireless technologies such as WiFi and Bluetooth. Wide area networking (WAN) topics include optical networks (e.g., DWDM and optical switching), high-speed routing, IP-based architectures, and MPLS. Additional topics include quality of service (QoS) and quality of experience (QoE) for multimedia traffic, as well as modern networking paradigms such as content delivery networks (CDNs), cloud and edge networking, and the convergence of technologies across access, edge, and core networks. The course provides a system-level understanding of how contemporary networks support high-performance multimedia applications.

Cryptography provides foundations for ensuring the confidentiality, authenticity, integrity and privacy of the increasing sensitive information in digital world. It is a theoretical field that relies on a diverse and wide variety of mathematics. The topics in this course include encryption, message authentication codes, digital signatures, public key crypto-systems, key exchange, identification protocols, zero-knowledge proof systems, etc. The goal of this course is to help students develop a solid understanding of the fundamentals of security and become familiar with the theories of cryptography as well as the role of cryptography in the recent and emerging applications.

Distributed Systems introduces the design and implementation of large-scale distributed computing systems, focusing on the fundamental principles that enable multiple independent machines to operate as a single coherent system. The course covers core concepts such as interprocess communication, distributed processing, data sharing and replication, and system coordination. Students study key challenges in distributed systems, including consistency, synchronization, fault tolerance, scalability, and performance. The course also examines system architectures and real-world implementations, highlighting trade-offs in reliability, efficiency, and resource management. Through lectures, research readings, and projects, students gain practical experience in designing, analyzing, and evaluating distributed systems for modern computing environments.

This course covers in detail the design and implementation of processes, interprocess communication, semaphores, monitors, message passing, scheduling algorithm, input/output, device drivers, memory management, file system design, security and protection mechanisms. The hardware-software interface and the user process-system call-kernel interface are examined in detail. Students modify and extend a multiuser operating system.

This course introduces the principles and techniques for developing high-performance implementations of deep learning models using modern GPU architectures. The course focuses on parallel programming concepts, memory hierarchy, and optimization strategies that enable efficient training and inference. Students gain hands-on experience implementing and optimizing computational kernels, understanding performance bottlenecks, and leveraging GPU frameworks and libraries for deep learning workloads. Topics include parallel execution models, memory management, kernel optimization, and efficient implementation of common deep learning operations such as matrix multiplication and convolution. The course also covers performance profiling, scalability, and trade-offs between computational efficiency, memory usage, and numerical accuracy in real-world deep learning systems.

This course is a tour through various topics and technologies related to cloud computing. Students will explore solutions and learn design principles for building large network-based systems to support both compute-intensive and data-intensive applications across geographically distributed infrastructure. Topics include resource management, programming models, application models, system characterizations, and implementations. Discussions will often be grounded in the context of deployed cloud computing systems such as Amazon EC2 and S3, Microsoft Azure, Google AppEngine, Eucalyptus, Nimbus, OpenStack, Google's MapReduce, Yahoo's Hadoop, Microsoft's Dryad, Sphere/Sector, and many other systems. The course involves lectures, outside invited speakers, discussions of research papers, programming assignments, and a major project (including both a written report and an oral presentation).

This course is a tour through various research topics in distributed data-intensive computing, covering topics in cluster computing, grid computing, supercomputing, and cloud computing. The course will explore solutions and learn design principles for building large network-based computational systems to support data-intensive computing. This course is geared for junior/senior-level undergraduates and graduate students in computer science.

Analytic and simulation techniques for the performance analysis of computer architecture, operating systems and communication networks. Rigorous development of queuing models. Study of simulation languages and models.

This course examines machine learning methods designed for data that are distributed across devices, networks, and organizations rather than centralized infrastructure. The course explores the computing continuum spanning cloud, fog, and edge systems, with an emphasis on the unique challenges of training AI models under system and statistical heterogeneity. Core topics include edge and fog computing, deep learning foundations, federated and split learning, gossip-based learning, and methods for aggregating knowledge across decentralized environments. Students engage with contemporary research literature and practical tools to better understand how learning systems operate in real-world, resource-constrained settings. Through lectures, assignments, and student-led discussions of recent papers, the course develops both theoretical insight and applied skills relevant to modern edge AI and decentralized learning applications.

In this course, we will examine the security and privacy issues in the vast implementations of Cyber-physical systems (CPS). According to the definition, CPS refers to a system that has both physical and software components, and they are all controlled or monitored by computer-based algorithms. CPS, or IoT systems touch many aspects of life, including transportation, health care, safety, environment, energy, and more. We will examine how existing security mechanisms can be applied to the CPS system, why such protections are not enough, and study the trend of security system design in the area. In addition, we will examine and discuss CPS/IoT technology and market specific topics, relevant case studies of system security vulnerabilities and attacks, and mitigation controls. We will have several case studies in current CPS/IoT systems and students will assess the health, safety, privacy, and economic impacts of IoT security events. Also, students will need to review research papers related to course topics and present a final project report at the end of the course.

This course will teach various modern topics in network and computer security. It will provide a thorough grounding in cyber-security for students who are interested in conducting research on security and networking and for students who are more broadly interested in real-world security issues and techniques. Students will undertake a semester-long research project with the goal of technical publications. Lecture topics will include, but not limited to: (1) Unwanted traffic, such as denial of service (DoS), and spam; (2) Malware, such as botnet, worm, and virus; (3) Network configuration and defense, such as firewall, access control, and intrusion detection systems; (4) Cyber physical system security, such as critical infrastructure protection (e.g., smart grid); and (5) Hot topics, such as software-defined networking (SDN), network verification, data center and enterprise network security, web security and more.

In a security conscious society, biometrics-based authentication and identification have become a central focus for many important applications as biometrics can provide accurate and reliable identification. Biometrics research and technology continue to mature rapidly, driven by pressing industrial and government needs and supported by industrial and government funding. This course offers an introduction to major biometric techniques, the underlying pattern recognition and computer vision basis for these biometrics, scientific testing and evaluation methodologies of biometrics systems, a deeper study of facial recognition, and an examination of the current privacy and social/ethical issues surrounding the technology. The course includes readings from the literature, short writing assignments, and practical experience with current biometric technology. Prerequisite working knowledge of Matlab or C/C++ is necessary.

This course examines the ethical, social, and legal implications of data-driven systems and algorithmic decision-making. The course explores issues such as bias, fairness, accountability, transparency, and privacy in the design and deployment of computational systems. Students develop the ability to critically evaluate how data and algorithms impact individuals and society, and to identify potential risks and unintended consequences. Topics include sources of bias in data and models, fairness metrics, responsible data collection and use, and the role of governance and regulation. The course emphasizes practical approaches to designing ethical systems, including methods for auditing and mitigating bias, ensuring transparency, and balancing competing objectives such as accuracy, efficiency, and fairness.

This course is a graduate-level and application-oriented course in security/privacy and machine learning. In this course, you will learn the fundamental techniques to enable privacy-preserving machine learning based on recent advancements in cryptography, with an in-depth focus on fully homomorphic encryption (FHE). This course is application-oriented with some necessary theoretical concepts. Specifically, this course will cover the basics of machine learning, the foundations of FHE, different FHE schemes, and the programming techniques of using FHE for machine learning.

This is an advanced systems course which introduces the internals of modern virtualization software and hardware, from full system emulators, binary translators, and high-level language virtual machines to hypervisors, lightweight virtualization mechanisms such as containers, and hardware virtualization extensions. Students will learn the key abstractions and mechanisms that underly resource virtualization by building significant components of real-world systems.

This course introduces methods for learning sequential decision-making policies using function approximation and deep neural networks. Building on the foundations of reinforcement learning, the course focuses on scalable algorithms that operate in high-dimensional state and action spaces. Topics include value-based methods such as Deep Q-Networks, policy gradient methods, and actor–critic architectures, along with techniques for stabilizing and improving learning such as experience replay, target networks, and exploration strategies.

Machine Learning for Time Series Analysis and Forecasting introduces the principles and practice of modeling temporal data using statistical and modern machine learning techniques. The course focuses on identifying structure in time series, including trends, seasonality, and autocorrelation, and leveraging these patterns for accurate and robust forecasting. Students gain experience working with real-world datasets, developing end-to-end workflows for preprocessing, feature engineering, model training, and evaluation through applied projects. Core topics include stationarity, time series decomposition, residual diagnostics, and uncertainty quantification through prediction intervals. The course covers classical approaches (e.g., ARIMA and exponential smoothing) alongside machine learning methods (e.g. tree-based models, recurrent neural networks, and transformer-based architectures). By the end of the course, students will be able to design, implement, and critically evaluate forecasting systems for real-world applications across diverse domains.

Advanced Computer Architecture examines the design and performance of modern computer systems, focusing on techniques for improving computational efficiency and scalability. The course covers key architectural concepts such as instruction-level parallelism, pipelining, and dynamic execution, along with memory hierarchy design, interconnection networks, and multicore and multiprocessor systems. Students analyze trade-offs in performance, power, and complexity, and study architectural mechanisms that enable high-performance computing. Topics include cache design, memory systems, parallel architectures, and storage organization, as well as evaluation methodologies and benchmarking. The course also explores emerging architectural trends and experimental systems, preparing students to design and reason about next-generation computing platforms.

Current problems in computer architecture.

Deep neural networks form an important sub-field of machine learning that is responsible for much of the progress in in cognitive computing in recent years in areas of computer vision, audio processing, and natural language processing. Deep networks can be trained with a single end-to-end model and bypass the need for traditional task-specific feature engineering. In this way deep learning simplifies learning tasks and allows using developed models to new tasks. Deep networks are suitable for parallel processing implementations and can easily leverage intensive computational resources. The course will focus on mathematical concepts, numerical algorithms, principles, GPU frameworks, and applications of deep learning. Topics include deep feedforward networks, convolutional networks, sequence modeling, transformers, and deep generative models with applications to data analysis, computer vision, and natural language processing. Several programming assignments and a project will practice the application of deep learning techniques to actual problems. The course requires sufficient math and programming background but does not require prior knowledge in machine learning.

This course provides a graduate level introduction to reinforcement learning, a framework for learning sequential decision making policies through interaction with an environment. Topics include multi armed bandits, Markov Decision Processes, and core solution methods such as dynamic programming, Monte Carlo learning, and temporal difference learning. Students develop a unified understanding of prediction, control, and learning from experience, as well as on policy and off policy learning and model based reinforcement learning. In addition to theoretical foundations, students gain experience implementing and evaluating reinforcement learning algorithms through assignments and projects.

This course will explore the latest algorithms for analyzing online social networks, considering both their structure and content. Fundamentals of social graph theory will be covered including distance, search, influence, community discovery, diffusion, and graph dynamics. Fundamentals of text analysis will also be covered with an emphasis on the type of text used in online social networks and common applications. Topics include sentiment classification, information extraction, clustering, and topic modeling. Emphasis will be placed on the application of this technology to areas such as public health, crisis response, politics, and marketing.

This course covers advanced topics in machine learning. The exact course contents may change based on recent advances in the area and the instructor teaching it. Possible topics include active learning, reinforcement learning, online learning, non-parametric learning, inductive learning, statistical relational learning, dimensionality reduction, ensemble methods, transfer learning, outlier detection, specific application areas of machine learning, and other relevant and/or emerging topics.

Covers advanced topics in artificial intelligence. Topics include search and optimization, simulated annealing, evolutionary algorithms, gradient optimization, constraint optimization, A* search, alpha-beta search, Monte Carlo tree search, probabilistic reasoning, Bayesian networks, hidden Markov models, Kalman filters, decision-making under uncertainty, influence diagrams, Markov decision processes, bandit problems, supervised learning, classification, deep learning, reinforcement learning, knowledge representation, propositional and first-order logic, ontological engineering, AI ethics and safety, privacy, bias and fairness in machine learning, and explainable AI.

This course introduces the algorithmic foundations of autonomous robotics, focusing on how robots perceive their environment, estimate their state, plan actions, and execute control strategies under uncertainty. Emphasis is placed on software and computational methods rather than hardware systems. The course covers robot kinematics, sensing and perception, and probabilistic state estimation, including Bayesian filtering techniques. Core problems such as localization and mapping are studied in depth, followed by planning and navigation algorithms that enable robots to achieve goals in complex environments. The course concludes with control strategies that integrate perception, estimation, and decision-making into coherent autonomous behavior. Through algorithmic analysis and simulation-based implementation, students develop a complete understanding of the perception-estimation-planning-control pipeline underlying modern robotic systems.

This course will cover probabilistic graphical models -- powerful and interpretable models for reasoning under uncertainty. The generic families of models such as directed, undirected, and factor graphs as well as specific representations such as hidden Markov models and conditional random fields will be discussed. The discussions will include both the theoretical aspects of representation, learning, and inference, and their applications in many interesting fields such as computer vision, natural language processing, computational biology, and medical diagnosis.

Introduce fundamental problems in machine learning. Provide understanding of techniques, mathematical concepts, and algorithms used in machine learning. Provide understanding of the limitations of various machine learning algorithms and the way to evaluate performance of learning algorithms. Topics include introduction, regression, kernel methods, generative learning, discriminative learning, neural networks, support vector machines, graphical models, unsupervised learning, and dimensionality reduction.

An introduction to the problems of computing with human languages. Parsing. Semantic representations. Text generation. Lexicography. Discourse. Sublanguage studies. Applications to CAI, database interfaces and information retrieval.

This course covers the state-of-the-art in architectural design of complex software systems. The course considers commonly-used software system architectures, techniques for designing and implementing these architectures, models and notations for characterizing and reasoning about architectures, and case studies of actual software system architectures.

Concepts of software product and process quality. Role of TQM in software project management. Use of metrics, feasibility studies, cost and effort estimates. Discussion of project planning and scheduling. The project team and leadership issues. The Capability Maturity Model: basic tenets and application of process evaluation.

Software development process improvement is a major objective of this course. This is achieved through a series of individual programming and process projects. Students learn to plan their projects, apply measurements, estimate size, schedule tasks, and classify defects in order to improve the quality of both their development process and their software products.

Concepts and techniques for testing and analysis of software. Software testing at the unit, subsystem, and system levels. Specification-based testing. Code-based testing. Model-based testing. Methods for test generation and validation. Static and dynamic analysis. Formal methods and verification. Reliability analysis.

Investigation and discussion by faculty and students concentrated on some topic of current interest. May be taken more than once. Prerequisite: Instructor permission required.

Instructor permission required.

Instructor permission required.

This course will treat a specific topic, varying from semester to semester, in which there is a particular student or staff interest. May be taken more than once.

May be taken more than once. (Credit: Variable) Instructor permission required.

Covers advanced topics in computer vision to enhance knowledge of students interested in this highly important area. The topics in this course may change between semesters depending on the instructor teaching the course and the current state of the art in this area. Possible topics include: Image based modeling and rendering, Multiple view geometry, Auto-calibration, Object recognition, Motion analysis, Tracking, Perceptual user interfaces, Face and gesture recognition, Active vision.

Theoretical analysis of various types of algorithms. Topics vary, and may include approximation, quantum, on-line, distributed, randomized, and parallel algorithms. Requires CS 430. Instructor permission required.

Introduction to advanced networking research. A particular focus area will be considered, keeping current with advances in computer networking. Quantitative methods will be emphasized.

CS 585 Covers various topics in linguistics as they may be applied to various computational problems in AI, NLP, or IR. The topics in this course may change between semesters depending on the instructor teaching the course and the current state of the art in this area. Possible topics include: Systemic Functional Linguistics, Clausal structure, Group structure, Complex structure, Cognitive Linguistics, Process semantics.

Course content is variable and reflects the current trends in software engineering. Instructor permission required.

Instructor permission required.

Doctoral seminar.

Overview of database architectures, including the Relational, Hierarchical, Network, and Object Models. Database interfaces, including relational algebra and the SQL query language. (Coursera)

Fundamentals of relational database design. Entity-Relationship diagrams, UML, and design principles. Normal forms and functional dependencies.

Accessing SQL from traditional programming languages. Functions, procedures, and triggers. Transactions and concurrency models. Database storage and indexing.