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Current Courses

Fall 2024 

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15639
Independent Study in Computer Science Pedagogy
varies
This class is for master's students contributing to the development and delivery of a class, e.g., in a co-instructor role or as a preparation for teaching professionally. Students will be supervised by a faculty member and will participate in graduate teaching support activities sponsored by the Eberly Center for Teaching Excellence and Educational Innovation. You must contact your academic advisor to be enrolled in the class.

Instructor(s)

David O'Hallaron
Ruben Martins
Dave Eckhardt

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15640
Distributed Systems
12
The goals of this course are twofold: First, for students to gain an understanding of the principles and techniques behind the design of distributed systems, such as locking, concurrency, scheduling, and communication across the network. Second, for students to gain practical experience designing, implementing, and debugging real distributed systems. The major themes this course will teach include scarcity, scheduling, concurrency and concurrent programming, naming, abstraction and modularity, imperfect communication and other types of failure, protection from accidental and malicious harm, optimism, and the use of instrumentation and monitoring and debugging tools in problem solving. As the creation and management of software systems is a fundamental goal of any undergraduate systems course, students will design, implement, and debug large programming projects. As a consequence, competency in both the C and Java programming languages is required.

Instructor(s)

Wenting Zheng
Heather Miller

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15641
Networking and the Internet
12
The emphasis in this course will be on the basic performance and engineering trade-offs in the design and implementation of computer networks. To make the issues more concrete, the class includes several multi-week projects requiring significant design and implementation.

Instructor(s)

Peter Steenkiste

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15645
Database Systems
12
This course is on the design and implementation of database management systems. Topics include data models (relational, document, key/value), storage models (n-ary, decomposition), query languages (SQL, stored procedures), storage architectures (heaps, log-structured), indexing (order preserving trees, hash tables), transaction processing (ACID, concurrency control), recovery (logging, checkpoints), query processing (joins, sorting, aggregation, optimization), and parallel architectures (multi-core, distributed). Case studies on open-source and commercial database systems will be used to illustrate these techniques and trade-offs. The course is appropriate for students with strong systems programming skills.

Instructor(s)

Andrew Pavlo

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15650
Algorithms & Advanced Data Structures
12
The objective of this course is to study general computational problems, with a focus on the principles used to design those algorithms. Efficient data structures will be discussed to support these algorithmic concepts. Topics include: Run time analysis, divide-and-conquer algorithms, dynamic programming algorithms, network flow algorithms, linear and integer programming, large-scale search algorithms and heuristics, efficient data storage and query, and NP-completeness. Although this course will have several programming assignments, it is primarily not a programming course. Instead, it will focus on the design and analysis of algorithms for general classes of problems. This course is not open to CS graduate students who should consider taking 15-651 instead.

Instructor(s)

Yun Yu

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15651
Algorithm Design and Analysis
12
This course is intended for SCS graduate students. All other graduate students should register for 15-650. This course is about the design and analysis of algorithms. We study specific algorithms for a variety of problems, as well as general design and analysis techniques. Specific topics include searching, sorting, algorithms for graph problems, efficient data structures, lower bounds and NP-completeness. A variety of other topics may be covered at the discretion of the instructor. These include parallel algorithms, randomized algorithms, geometric algorithms, low level techniques for efficient programming, cryptography, and cryptographic protocols.

Instructor(s)

Daniel Anderson
Jason Li

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15652
Foundations of Programming Languages
12
This course discusses in depth many of the concepts underlying the design, definition, implementation, and use of modern programming languages. Formal approaches to defining the syntax and semantics are used to describe the fundamental concepts underlying programming languages. A variety of programming paradigms are covered such as imperative, functional, logic, and concurrent programming. In addition to the formal studies, experience with programming in the languages is used to illustrate how different design goals can lead to radically different languages and models of computation.

Instructor(s)

Robert Harper

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15657
Constructive Logic
9
This multidisciplinary junior-level course is designed to provide a thorough introduction to modern constructive logic, its roots in philosophy, its numerous applications in computer science, and its mathematical properties. Some of the topics to be covered are intuitionistic logic, inductive definitions, functional programming, type theory, realizability, connections between classical and constructive logic, decidable classes. This course counts as a Fundamentals course in the Computer Science major.

Instructor(s)

Karl Crary

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15659
Probability & Computing: Randomized Algs and Markov Chains
12
Probability theory has become indispensable in computer science. In areas such as artificial intelligence and computer science theory, probabilistic methods and ideas based on randomization are central. In other areas such as networks and systems, probability is becoming an increasingly useful framework for handling uncertainty and modeling the patterns of data that occur in complex systems. This course is a follow-up course to 15-259, Probability and Computing. It will cover Chapters 18-27 of the same textbook, "Introduction to Probability for Computing", by Prof. Harchol-Balter. Topics include concentration inequalities, various randomized algorithms including number theoretic routines, Markov chains and their many applications, and queueing theory. The course will assume familiarity with multivariate calculus and linear algebra.

Instructor(s)

Weina Wang
Richard Peng

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15662
Computer Graphics
12
This course provides a comprehensive introduction to computer graphics modeling, animation, and rendering. Topics covered include basic image processing, geometric transformations, geometric modeling of curves and surfaces, animation, 3-D viewing, visibility algorithms, shading, and ray tracing.

Instructor(s)

Minchen Li
Oscar Dadfar

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15663
Computational Photography
12
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.).

Instructor(s)

Ioannis Gkioulekas

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15666
Computer Game Programming
12
The goal of this course is to acquaint students with the code required to turn ideas into games. This includes both runtime systems -- e.g., AI, sound, physics, rendering, and networking -- and the asset pipelines and creative tools that make it possible to author content that uses these systems. In the first part of the course, students will implement small games that focus on specific runtime systems, along with appropriate asset editors or exporters. In the second part, students will work in groups to build a larger, polished, open-ended game project. Students who have completed the course will have the skills required to extend -- or build from scratch -- a modern computer game. Students wishing to take this class should be familiar with the C++ language and have a basic understanding of the OpenGL API. If you meet these requirements but have not taken Computer Graphics (the formal prerequisite), please contact the instructor.

Instructor(s)

James McCann

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15672
Real-Time Graphics
12
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; debugging, profiling, and accelerating graphics algorithms.

Instructor(s)

James McCann

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15673
Visual Computing Systems
12
Visual computing tasks such as computational imaging, image/video understanding, and real-time graphics are key responsibilities of modern computer systems ranging from sensor-rich smart phones to large datacenters. These workloads demand exceptional system efficiency and this course examines the key ideas, techniques, and challenges associated with the design of parallel, heterogeneous systems that accelerate visual computing applications. This course is intended for graduate and advanced undergraduate-level students interested in architecting efficient graphics, image processing, and computer vision platforms.

Instructor(s)

Oscar Dadfar

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15689
Independent Study in the Computer Sciences
varies
This course is for Computer Science master's students carrying out research supervised by a faculty member. Students will be automatically wait-listed pending program approval of an independent-study prospectus (contact your academic advisor for details).

Instructor(s)

David O'Hallaron
Dave Eckhardt
Ruben Martins

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15697
Graduate Reading and Research
varies
Research course for students pursuing a thesis in the 5th Year Master of Science Program. Working 1 on 1 with faculty and their graduate students.

Instructor(s)

Ruben Martins
Tracy Farbacher
Dave Eckhardt
David O'Hallaron

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15698
MSCS Research Thesis
varies
This course is for students in the "MSCS" course-based Computer Science master's program who are participating in the thesis option. Students will be automatically wait-listed pending program approval of a thesis proposal (contact your academic advisor for details).

Instructor(s)

Ruben Martins
Dave Eckhardt
David O'Hallaron

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15712
Advanced Operating Systems and Distributed Systems
12

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.

Instructor(s)

David Andersen

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Doctoral Breadth: Software Systems - (*)

15721
Advanced Database Systems
12
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.

Instructor(s)

Jignesh Patel

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15735
Foundations of Blockchains
12
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.

Instructor(s)

Elaine Shi

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15745
Optimizing Compilers for Modern Architectures
12

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.

Instructor(s)

Todd Mowry

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Doctoral Breadth: Computer Systems - (*)

15746
Storage Systems
12
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.

Instructor(s)

Gregory Ganger
George Amvrosiadis

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15749
Post-von Neumann Computer Architecture
12
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".

Instructor(s)

Nathan Beckmann

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15750
Algorithms in the Real World
12

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.

Instructor(s)

Daniel Sleator
Rashmi Korlakai Vinayak

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Doctoral Breadth: Algorithms and Complexity - (*)

15756
Randomized Algorithms
12

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

Instructor(s)

William Kuszmaul

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Doctoral Breadth: Algorithms and Complexity - (*)

15772
Real-Time Computer Graphics
12
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.

Instructor(s)

James McCann

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15789
Theoretical and Empirical Foundations of Modern Machine Learning
12
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.

Instructor(s)

Aditi Raghunathan

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15795
Topics in Algorithmic Problem Solving
12
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.

Instructor(s)

Richard Peng

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15801
CS PhD Alternative Elective
varies
This number is used internally by the department to transfer approved courses for elective credit.

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15802
CSD PhD Elective
varies
This course number is used for internal course transfer for approved electives outside CSD

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15814
Types and Programming Languages
12

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.

Instructor(s)

Jan Hoffmann

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Doctoral Breadth: Programming Languages - (*)

15816
Advanced Topics in Logic: Automated Reasoning and Satisfiability
12

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.

Instructor(s)

Marijn Heule

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Doctoral Breadth: Programming Languages - (*)

15821
Mobile and Pervasive Computing
12

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.

Instructor(s)

Mahadev Satyanarayanan
Asim Smailagic

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Doctoral Breadth: Software Systems - (-)

15826
Multimedia and Data Mining
12

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.

Instructor(s)

Christos Faloutsos

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Doctoral Breadth: Software Systems - (-)

15856
Introduction to Cryptography
12

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

Instructor(s)

Aayush Jain

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Doctoral Breadth: Algorithms and Complexity - (*)

15860
Monte Carlo Methods and Applications
12
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.

Instructor(s)

Keenan Crane
Gautam Iyer

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15862
Computational Photography
12
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.).

Instructor(s)

Ioannis Gkioulekas

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15888
Computational Game Solving
12
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.

Instructor(s)

Tuomas Sandholm

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15889
Special Topics in AI: Adv. Topics in Reinforcement Learning and Decision Making
12
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.

Instructor(s)

Aviral Kumar

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15890
Computer Science Pedagogy
12
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.

Instructor(s)

Michael Hilton
Franceska Xhakaj

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15898
Quantum Computing Systems
12
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.

Instructor(s)

Umut Acar

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15990
Computer Science Colloquium
No course description provided.

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15996
Introductory Course for CS Doctoral Students (IC)
6
This orientation course is required for incoming Computer Science Department doctoral students.

Instructor(s)

Karl Crary

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15997
Graduate Reading and Research
varies
This course number is used for doctoral students to register the appropriate units for research associated with work toward on or on their dissertation.

Instructor(s)

Karl Crary

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15998
Practicum in Computer Science
varies
Individualized Experience - Internships

Instructor(s)

Karl Crary

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