Current Courses

This is a graduate course in operating systems. The course will consist of a reading/lecture/discussion component and a project component. The class will cover approximately 40 research papers. Some of these papers will introduce students to the basic principles on which modern operating systems and distributed systems are based. Others will cover more recent work to explore the state of the art and observe the evolution of these systems over time.

This course is a comprehensive study of the internals of modern database management systems. It will cover the core concepts and fundamentals of the components that are used in large-scale analytical systems (OLAP). The class will stress both efficiency and correctness of the implementation of these ideas.

In this course, students will learn the mathematical foundations of blockchains, including how to construct distributed consensus protocols and prove them secure, cryptography for blockchains, and mechanism design for blockchains. Students may also be expected to implement some consensus or cryptographic algorithms.

Theoretical and practical aspects of building optimizing compilers that e¿ectively exploit modern architectures. The course will begin with the fundamentals of compiler optimization, and will build upon these fundamentals to address issues in state-of-the-art commercial and research machines.

Storage systems are among the most fascinating and the most important parts of computer systems. They often dominate the performance of a system, and failures of other components are frequently addressed by restarting from the data stored on them. Indeed, storage systems hold the crown jewels of most organizations: their information (from source code to Microsoft's software to the sales databases of every e-commerce site to the logs and indices driving the Big Data and ML revolution). There continues to be great demand for bright people and better solutions in this critical field of computer systems.

Computing has been dominated by von Neumann CPU architectures for seventy years. The von Neumann architecture is familiar and flexible, but it is also extremely inefficient, wasting upwards of 99% of energy. As computing is now energy-limited across all scales, from IoT to data center, von Neumann's inefficiency can no longer be tolerated. Recently, industry has adopted heterogeneous "accelerator" hardware to boost performance and efficiency. However, accelerators have limited programmability, sacrificing the main benefit of CPU architectures and putting future innovation at risk. This class will survey non-von Neumann general-purpose architectures, recent work on specialized hardware accelerators, and cutting-edge research on "programmable accelerators".

The course covers a broad set of topics in algorithms design and analysis. The goal is to cover tools and algorithms that give students the ability to (a) recognize which tool or method to apply to problems, (b) to become reasonably proficient at using these tools, and (c) to be able to reason about the correctness and performance of the resulting algorithms. The course webpage for this semester will list the tentative list of topics to be covered; these will include basic graph algorithms, randomized algorithms, hashing and streaming, flows and linear programming, convex optimization, and linear algebraic algorithms. Please refer to https://www.cs.cmu.edu/~csd-grad/courseschedules22.html for the most recent schedule updates.

A graduate-level course on how to use randomization to design algorithms and data structures with strong provable guarantees.

Real-time computer graphics is about building systems that leverage modern CPUs and GPUs to produce detailed, interactive, immersive, and high-frame-rate imagery. Students will build a state-of-the-art renderer using C++ and the Vulkan API. Topics explored will include efficient data handling strategies; culling and scene traversal; multi-threaded rendering; post-processing, depth of field, screen-space reflections; volumetric rendering; sample distribution, spatial and temporal sharing, and anti-aliasing; stereo view synthesis; physical simulation and collision detection; dynamic lights and shadows; global illumination, accelerated raytracing; dynamic resolution, "AI" upsampling; compute shaders; parallax occlusion mapping; tessellation, displacement; skinning, transform feedback; and debugging, profiling, and accelerating graphics algorithms.

In this advanced machine learning seminar class, we tackle the typical struggle in using the modern machinery including large language models and other foundation models: what works and why? How do we make things more reliable and robust? We build a conceptual understanding of deep learning and foundation models through several different angles: standard in-distribution generalization, out-of-distribution generalization, self-supervised learning, data curation, scaling laws, alignment etc. We will read papers that contain a mix of theoretical and empirical insights with a focus on making connections to classic ideas, identifying recurring themes, and discussing avenues for future developments. The class aims to equip students with the ability to critically reason about and build a more principled understanding of current advances which will hopefully spark their own research.

This course aims to give implementation motivated perspectives on some algorithmic ideas that fall outside of the scopes of typical algorithms courses. It is intended for graduate students, as well as undergraduate students who have high grades in 15-451, and 15-259 or 21-325. The first half of the course will discuss floating point precision, numerical approximation schemes, heuristic search, usage of optimization packages, and vectorization. The second half will provide high-level surveys of 2-D range update & query data structures, proactive propagation, and iterative methods. Evaluations will consist of about 30 auto-graded coding tasks, plus either participation in the ICPC NAEast Programming Contest, or presentations of problem-solving reports from various OI team selections.

This number is used internally by the department to transfer approved courses for elective credit.

This course number is used for internal course transfer for approved electives outside CSD

The course studies the theory of type systems, with a focus on applications of type systems to practical programming languages. The emphasis is on the mathematical foundations underlying type systems and operational semantics. The course includes a broad survey of the components that make up existing type systems, and also teaches the methodology behind the design of new type systems.

Automated reasoning has become a powerful technology with applications ranging from verification of hardware and software to solving long-standing open problems in mathematics. This course covers several state-of-the-art automated reasoning techniques and provides hands-on experience with research questions in this area.

This is a course exploring research issues in mobile computing and its close relative, pervasive computing (aka "Internet of Things (IoT)"). Many traditional areas of computer science and computer engineering are impacted by the constraints and demands of mobile and pervasive computing. The course will offer significant hands-on experience: students will work in small groups under the guidance of a mentor on a project. Each student will present a research paper from the literature in a conference-style 30-minute talk. In teams of two, students will present a short (30 minutes) overview of the commercial landscape for one of the topics covered in class. There will a brief quiz at the start of each class, based on the readings for that class. Prerequisites Knowledge of operating systems, distributed systems, and computer architecture. If in doubt, check with one of the instructors before registering.

The course covers advanced algorithms for learning, analysis, data management and visualization of large datasets. Topics include indexing for text and DNA databases, searching medical and multimedia databases by content, fundamental signal processing methods, compression, fractals in databases, data mining, privacy and security issues, rule discovery, data visualization, graph mining, stream mining.

This course is aimed as an introduction to modern cryptography. This course will be a mix of applied and theoretical cryptography.

The Monte Carlo method uses random sampling to solve computational problems that would otherwise be intractable, and enables computers to model complex systems in nature that are otherwise too difficult to simulate. This course provides a first introduction to Monte Carlo methods from complementary theoretical and applied points of view, and will include implementation of practical algorithms. Topics include random number generation, sampling, Markov chains, Monte Carlo integration, stochastic processes, and applications in computational science. Students need a basic background in probability, multivariable calculus, and some coding experience in any language.

Computational photography is the convergence of computer graphics, computer vision and imaging. Its role is to overcome the limitations of the traditional camera, by combining imaging and computation to enable new and enhanced ways of capturing, representing, and interacting with the physical world. This advanced undergraduate course provides a comprehensive overview of the state of the art in computational photography. At the start of the course, we will study modern image processing pipelines, including those encountered on mobile phone and DSLR cameras, and advanced image and video editing algorithms. Then we will proceed to learn about the physical and computational aspects of tasks such as 3D scanning, coded photography, lightfield imaging, time-of-flight imaging, VR/AR displays, and computational light transport. Near the end of the course, we will discuss active research topics, such as creating cameras that capture video at the speed of light, cameras that look around walls, or cameras that can see through tissue. The course has a strong hands-on component, in the form of seven homework assignments and a final project. In the homework assignments, students will have the opportunity to implement many of the techniques covered in the class, by both acquiring their own images of indoor and outdoor scenes and developing the computational tools needed to extract information from them. For their final projects, students will have the choice to use modern sensors provided by the instructors (lightfield cameras, time-of-flight cameras, depth sensors, structured light systems, etc.).

The course will focus on multi-step imperfect-information games because most real-world strategic settings are such games. Such games beget additional issues beyond perfect-information games like chess and Go, such as signaling, deception, and understanding deception by others. There has been tremendous progress in the AI community on solving such games since around 2003. This course covers the fundamentals and the state of the art of solving such games.

This seminar class will cover some advanced topics in RL including foundations, algorithms, applications (in foundation model fine-tuning, robotics, scientific applications, etc). This course is expected to involve paper reading each week, including a discussion of these papers in the class, followed by a class project.

A broad introduction to Computer Science Pedagogy. Although the course focus is on CS pedagogy and developing curricular content, students will learn some fundamentals of modern mobile application programming.

Quantum computing has been developing into an important field cross-cutting several disciplines, including computer science, physics, and mathematics. Over the past several decades, there has been much work on the theory front, including advances in quantum algorithms. In recent years, this work is starting to become practically relevant.

No course description provided.

This orientation course is required for incoming Computer Science Department doctoral students.

This course number is used for doctoral students to register the appropriate units for research associated with work toward on or on their dissertation.

Individualized Experience - Internships