Logic has been called 'the calculus of computer science'. The argument is that logic plays a fundamental role in computer science, similar to that played by calculus in the physical sciences and traditional engineering disciplines. Indeed, logic plays an important role in areas of Computer Science as disparate as artificial intelligence (automated reasoning), architecture (logic gates), software engineering (specification and verification), programming languages (semantics, logic programming), databases (relational algebra and SQL), algorithms (complexity and expressiveness), and theory of computation (general notions of computability).

The design, definition and abstract implementation of programming languages including methods for precisely specifying syntax and semantics.

The main focus of the course will be on smooth optimization techniques, with applications in machine learning and artificial intelligence. The course will introduce the basics of algorithms on continuous optimization, starting from the classical gradient descent algorithm in convex optimization, towards more sophisticated approaches in non-convex scenarios. The course will explore the fundamental theory, algorithms, complexity and approximations in nonlinear optimization.

Since the advent of Sanger Sequencing in 1977, computer scientists have devised algorithms and software tools to interpret and analyze DNA sequences. The field of bioinformatics focuses on computational approaches to solving biological questions. This course serves both as an introduction to widely used algorithms in bioinformatics and a software design course. The class involves a semester-long software design and implementation project, emphasizing design patterns and high-performance computing. No prior knowledge of biology is assumed nor required.

The course introduces students to modern cryptographic techniques, focusing mainly on mathematical tools. The course covers topics such as Diffie-Hellman key exchange, the ElGamal public key crypto system, integer factorization and RSA, and elliptic curves and lattices in cryptography.

Concrete mathematics is a blend of continuous and discrete mathematics. Major topics include sums, recurrences, integer functions, elementary number theory, binomial coefficients, generating functions, discrete probability and asymptotic methods.

2D graphics techniques including fast line and curve drawing and polygon filling. 3D graphics problems including representation of solids, shading, and hidden surface elimination. Fractals, graphics standards.

This course will be ideal for someone wanting to build a strong foundation in the theory and practice of algorithms for processing Big-Data. We will discuss advanced data structures and algorithms going beyond deterministic setting and emphasize the role of randomness in getting significant, often exponential, improvements in computations and memory.

Finite automata, regular expressions, regular languages, pushdown automata, context-free languages, Turing machines, recursive languages, computability, and solvability. It is strongly recommended that students complete three semesters of Mathematics before enrolling in this course.

Topics in the design of programming language translators, including parsing, run-time storage management, error recovery, code generation and optimization.

Introduction to the design, construction, and analysis of concurrent programs with an emphasis on operating systems, including filing systems, schedulers, and memory allocators. Specific attention is devoted to process synchronization and communication within concurrent programs.

COMP 422 is an undergraduate version of this course. COMP 422 students will have four programming assignments. COMP 534 students will have five. As part of their assignments, both COMP 422 and COMP 534 students will analyze the scalability and parallel efficiency of parallel programs they write. COMP 534 students will additionally use tools to qualify the root causes of scaling losses in their programs and document their findings.

This elective course covers a wide variety of topics in computer security, including hands-on experience w/breaking software & engineering software to be harder to break.

Network architectures, algorithms, and protocols. Local- and Wide-area networking. Intra- and inter-domain routing. Transmission reliability. Flow and congestion control. TCP/IP. Multicast. Quality of Service. Network Security - Networked applications.

This course covers database management system architecture, query processing and optimization, transaction processing, concurrent control and recover, storage, indexing structures and related topics. Students will build a database system from the ground up. Students who have not had an introductory database course should enroll for 4 credits: all others should enroll for 3 credits.

What is “cloud computing?” How do we build cloud-scale systems and components that are secure against malicious attacks, and scale to millions of users? Many of today’s services run inside the cloud – a set of geographically distributed data centers running heterogeneous software stacks.

Quantum computing is an emerging field with the potential to revolutionize various industries, including cryptography, scientific computation, optimization, and machine learning. Quantum Computing Algorithms is a course designed to introduce students to the foundations and practical algorithms of quantum computing from a systems perspective to equip them for the evolving technological landscape. The course will first refresh students on required mathematical concepts in linear algebra, probabilities, and statistics. Students will also learn about fundamental quantum principles, including superposition, entanglement, reversibility, interference, and circuits. The course will then delve into advanced quantum algorithms, especially variational and parameterized codes, including search, optimization, machine learning, and quantum simulation. Students will gain hands-on experience with Python-based quantum programming languages, Cirq and Tensorflow Quantum, to program current quantum computers.

This course is an introduction to the Internet of Things (IoT). It will present the tools and techniques needed to develop complex IoT applications that encompass interaction with the physical world and data analysis across the IoT computing infrastructure. The topics range from programming microcontrollers (e.g., Arduino) and single-board computers (e.g., Raspberry Pi) to IoT automation and the efficient analysis of real-time IoT data.

In this project-based course, students create multi-user Web applications involving all aspects of application development from front-end and back-end programming to interfacing client-server communications technologies. Class time includes discussions of topics in Web development, structural frameworks, test driven development, and time for students to develop their Web applications.

This is a foundational course in artificial intelligence, the discipline of designing intelligent agents. The course will cover the design and analysis of agents that do the right thing in the face of limited information and computational resources. The course revolves around two main questions: how agents decide what to do, and how they learn from experience. Tools from computer science, probability theory, and game theory will be used. Interesting examples of intelligent agents will be covered, including poker playing programs, bots for various games (e.g. WoW), DS1 -- the spacecraft that performed an autonomous flyby of Comet Borrely in 2001, Stanley -- the Stanford robot car that won the Darpa Grand Challenge, Google Maps and how it calculates driving directions, face and handwriting recognizers, Fedex package delivery planners, airline fare prediction sites, and fraud detectors in financial transactions.

This course introduces students to reinforcement learning (RL), a general and impactful machine learning paradigm for solving sequential decision-making problems and designing autonomous agents. The course will cover both classical and recent algorithms for reinforcement learning (including deep RL) and imitation learning (including inverse RL). Through the assignments and final project, students will get hands-on experience in applying reinforcement learning algorithms to solve problems inspired by real-world applications. The course will conclude with an overview of open problems and ongoing research in reinforcement learning.

An introduction to the basic concepts, algorithms and applications in computer vision. Topics include: image processing pipelines, low-level vision/image processing methods such as filtering and edge detection, mid-level vision topics such as segmentation and feature detection, and high-level vision tasks such as object recognition and face recognition with neural networks. The course will involve programming and implementing basic computer vision algorithms in Python.

Robots have fascinated people for generations. Today, robots are built for applications as diverse as exploring remote planets, de-mining war zones, cleaning toxic waste, assembling cars, inspecting pipes in industrial plants and mowing lawns. Robots are also interacting with humans in a variety of ways: robots are museum guides, robots assist surgeon sin life threatening operations, and robotic cars can drive us around. The field of robotics studies not only the design of new mechanisms but also the development of artificial intelligence frameworks to make these mechanism useful in the physical world, integrating computer science, engineering, mathematics and more recently biology and sociology, in a unique way. This class will present fundamental algorithmic advances that enable today’s robots to move in real environments and plan their actions. It will also explore fundamentals of the field of Artificial Intelligence through the prism of robotics. The class involves a significant programming project.

This course will overview both traditional and more recent graph-based machine learning algorithms. Graphs show up in machine learning in many forms. Oftentimes, the input data can be naturally represented as a graph, such as for relational learning tasks applied to social networks and graph kernels applied to chemical data. Other times, graphs are just a framework to express some intrinsic structure in the data, such as for graphical models and non-linear embedding. In both cases, recent advances in representation learning (or graph embedding) and deep learning have generated a renewed interest in machine learning on graphs. At the end of the course, students are expected to be able to: (1) identify the appropriate graph-based machine learning algorithm for a given problem; (2) extend existing algorithms to solve new related problems; and (3) recognize some of the key research challenges in the field. The course will be a mixture of lectures, a research paper presentation, homework assignments (including programming), and a hands-on class project.

Robots, as seen in many different forms, can help people in various application scenarios to improve the efficiency, safety, and quality of task executions. In particular, those robots which can physically interact with the world possess essential skills, called manipulation, to engage themselves more comprehensively in the real-world tasks. This course will introduce both fundamental and the state-of-the-art knowledge that enable robots to manipulate in our world. Topics including robot motions, kinematics, dynamics, simulation and ROS, hand and grasping, prehensile and nonprehensile manipulation, robot control, and interactive estimation, will be covered. Significant programming projects are involved to facilitate converting the textbook knowledge to solid hands-on experiences.

This course focuses on the principles and practices of test-driven software development, which have been popularized under the banner of 'Extreme Programming.' To provide students with practical experience, the course engages students in the development of open source production programs written in JAVA or C#. The DRJAVA programming courses was developed by students in this course. Some of the major topics covered in course lectures include design patterns for controlling concurrency and refactoring transformations to improve legacy code.

COMP 410 is a pure discovery-based learning course designed to give students real-life, hands-on training in a wide variety of software engineering issues that arise in creating large-scale, state-of-the-art software systems. The class forms a small software development 'company' that works to deliver a product to a customer. The topics encountered include and are not limited to, dealing with new technologies (e.g. C#, .NET, distributed computing), advanced object-oriented programming and design, interacting with customers, problem specification and tasking, individual and group communications, human resource management, group leadership, testing, integration and documentation. Traditional development cycle methodologies will be compared to recent, 'agile' techniques.

This course focuses on modern principles for the construction of distributed programs, with an emphasis on design patterns, modern programming tools, and distributed object systems. The material will be applied in a substantial software design/construction project.

Since the advent of Sanger Sequencing in 1977, computer scientists have devised algorithms and software tools to interpret and analyze DNA sequences. The field of bioinformatics focuses on computational approaches to solving biological questions. This course serves both as an introduction to widely used algorithms in bioinformatics and a software design course. The class involves a semester-long software design and implementation project, emphasizing design patterns and high-performance computing. No prior knowledge of biology is assumed nor required.

This project-based class involves teams of 2-4 CS and Visual Arts students designing and building computer games suitable for Xbox Live Arcade using C# and XNA.

Robots are now a wide spectrum of various embodiments, e.g., humanoids, dogs, industrial arms, drones, and self-driving cars, and they are rapidly merging themselves into our society. While we are not fully ready to welcome them, yet, we are more than certain that we need to work with them in ways that we did not know before. On the one hand, we are still challenged by many technical problems to enable robots to be more dexterous and robust. On another hand, in a time where resources are connected and networked, we need to design novel systems to manage different robots, share the robots, or even make them autonomously and adaptively build teams. In this senior design course, we will develop team-based projects and will look into a robotized future with our imaginations: we will either solve problems that are technically going to enhance the robot skills, or address problems that will make future robots more useful and more organized in the real world. Background in robotics is not required. In our creative projects, your efforts in developing core robot algorithms, databases, machine learning systems, web applications, mobile apps, LLM models, or anything else that can be useful, will together make this robotized world a better place.