Course Schedule

Current semester Fall 2025, and future semesters are now available to view.

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Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1170-001
DS-GA.1170-001
(8918) Fundamental Algorithms Yevgeniy Dodis T 4:55-6:55PM Meyr 121

Course Description:

Reviews a number of important algorithms, with emphasis on correctness and efficiency. The topics covered include solution of recurrence equations, sorting algorithms, selection, binary search trees and balanced-tree strategies, tree traversal, partitioning, graphs, spanning trees, shortest paths, connectivity, depth-first and breadth-first search, dynamic programming, and divide-and-conquer techniques.

Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
CSCI-GA.1170-002
DS-GA.1170-002
(8919) Fundamental Algorithms Recitation Daniel Collins M 5:55-6:45PM 5WP 101 Additional information: Recitation for Section 001

Course Description:

Reviews a number of important algorithms, with emphasis on correctness and efficiency. The topics covered include solution of recurrence equations, sorting algorithms, selection, binary search trees and balanced-tree strategies, tree traversal, partitioning, graphs, spanning trees, shortest paths, connectivity, depth-first and breadth-first search, dynamic programming, and divide-and-conquer techniques.

Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
CSCI-GA.1170-003
(8920) Fundamental Algorithms Ernest Davis T 4:55-6:55PM GCASL 269

Course Description:

Reviews a number of important algorithms, with emphasis on correctness and efficiency. The topics covered include solution of recurrence equations, sorting algorithms, selection, binary search trees and balanced-tree strategies, tree traversal, partitioning, graphs, spanning trees, shortest paths, connectivity, depth-first and breadth-first search, dynamic programming, and divide-and-conquer techniques.

Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
CSCI-GA.1170-006
(8921) Fundamental Algorithms Recitation Daniel Collins F 4:55-5:45PM 60 Fifth Ave 150 Additional information: Recitation for Section 001

Course Description:

Reviews a number of important algorithms, with emphasis on correctness and efficiency. The topics covered include solution of recurrence equations, sorting algorithms, selection, binary search trees and balanced-tree strategies, tree traversal, partitioning, graphs, spanning trees, shortest paths, connectivity, depth-first and breadth-first search, dynamic programming, and divide-and-conquer techniques.

Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
CSCI-GA.1170-007
(8922) Fundamental Algorithms Recitation Era Sarda R 5:55-6:45PM Silv 509 Additional information: Recitation for Section 003

Course Description:

Reviews a number of important algorithms, with emphasis on correctness and efficiency. The topics covered include solution of recurrence equations, sorting algorithms, selection, binary search trees and balanced-tree strategies, tree traversal, partitioning, graphs, spanning trees, shortest paths, connectivity, depth-first and breadth-first search, dynamic programming, and divide-and-conquer techniques.

Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java; and familiarity with recursive programming methods and with data structures (arrays, pointers, stacks, queues, linked lists, binary trees).
CSCI-GA.1180-001
BMIN-GA.1180-001
(8923) Mathematical Techniques For CS Applications Parijat Dube W 7:10-9:10PM Online Online

Course Description:

An introduction to theory, computational techniques, and applications of linear algebra, probability and statistics. These three areas of continuous mathematics are critical in many parts of computer science, including machine learning, scientific computing, computer vision, computational biology, natural language processing, and computer graphics. The course teaches a specialized language for mathematical computation, such as Matlab, and discusses how the language can be used for computation and for graphical output. No prior knowledge of linear algebra, probability, or statistics is assumed.
CSCI-GA.2110-001
(8924) Programming Languages Joseph Tassarotti R 4:55-6:55PM 19UP 102

Course Description:

Discusses the design, use, and implementation of imperative, object-oriented, and functional programming languages. The topics covered include scoping, type systems, control structures, functions, modules, object orientation, exception handling, and concurrency. A variety of languages are studied, including C++, Java, Ada, Lisp, and ML, and concepts are reinforced by programming exercises.

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2110-002
(8925) Programming Languages Recitation Elaine Li M 4:55-5:45PM 60 Fifth Ave 150 Additional information: Recitation for Section 001

Course Description:

Discusses the design, use, and implementation of imperative, object-oriented, and functional programming languages. The topics covered include scoping, type systems, control structures, functions, modules, object orientation, exception handling, and concurrency. A variety of languages are studied, including C++, Java, Ada, Lisp, and ML, and concepts are reinforced by programming exercises.

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2110-003
(8926) Programming Languages Cory Plock M 4:55-6:55PM CIWW 109

Course Description:

Discusses the design, use, and implementation of imperative, object-oriented, and functional programming languages. The topics covered include scoping, type systems, control structures, functions, modules, object orientation, exception handling, and concurrency. A variety of languages are studied, including C++, Java, Ada, Lisp, and ML, and concepts are reinforced by programming exercises.

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2110-005
(8927) Programming Languages Recitation Anushka Gangal F 5:55-6:45PM 60 Fifth Ave 150` Additional information: Recitation for Section 003

Course Description:

Discusses the design, use, and implementation of imperative, object-oriented, and functional programming languages. The topics covered include scoping, type systems, control structures, functions, modules, object orientation, exception handling, and concurrency. A variety of languages are studied, including C++, Java, Ada, Lisp, and ML, and concepts are reinforced by programming exercises.

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2110-006
(8928) Programming Languages Recitation Elaine Li F 11:15-12:05PM Silv 206 Additional information: Recitation for Section 001

Course Description:

Discusses the design, use, and implementation of imperative, object-oriented, and functional programming languages. The topics covered include scoping, type systems, control structures, functions, modules, object orientation, exception handling, and concurrency. A variety of languages are studied, including C++, Java, Ada, Lisp, and ML, and concepts are reinforced by programming exercises.

Prerequisites: Students taking this class should already have substantial programming experience.
CSCI-GA.2112-001
MATH-GA 2043-001
(15847) Scientific Computing Jonathan Goodman TR 4:55-6:10PM CIWW 1302

Course Description:
Methods for numerical applications in the physical and biological sciences, engineering, and finance. Basic principles and algorithms; specific problems from various application areas; use of standard software packages.

Prerequisites: Multivariate calculus and linear algebra. Some programming experience recommended.
CSCI-GA.2250-001
(8929) Operating Systems Hubertus Franke M 7:10-9:10PM CIWW 109

Course Description:

The topics covered include the high-level design of key operating systems concepts such OS structures; process scheduling and synchronization; deadlocks and their prevention; memory management, including (demand) paging; and I/O and file systems. The concepts will be shown in the context of Unix/Linux. The learning is facilitated with labs that implement some of these key design principles and algorithms in C/C++.

Prerequisites: None
CSCI-GA.2250-002
(8930) Operating Systems Yang Tang R 4:55-6:55PM Kimmel 808

Course Description:

The topics covered include the high-level design of key operating systems concepts such OS structures; process scheduling and synchronization; deadlocks and their prevention; memory management, including (demand) paging; and I/O and file systems. The concepts will be shown in the context of Unix/Linux. The learning is facilitated with labs that implement some of these key design principles and algorithms in C/C++.

Prerequisites: None
CSCI-GA.2262-001
(8931) Data Communications and Networks Jean-Claude Franchitti R 7:10-9:10PM Meyr 122

Course Description:

This course teaches the design and implementation techniques essential for engineering robust networks. Topics include networking principles, Transmission Control Protocol/Internet Protocol, naming and addressing (Domain Name System), data encoding/decoding techniques, link layer protocols, routing protocols, transport layer services, congestion control, quality of service, network services, programmable routers and overlay networks.

Prerequisites: Students must have a working knowledge of fundamental data structures and associated algorithms. For some of the practical aspects of the course, a working knowledge of an object-oriented programming language (e.g., C++, C#, or preferably Java) is expected. An undergraduate course in data communication and networks is helpful but not required.
CSCI-GA.2270-001
(8932) Computer Graphics Kenneth Perlin W 7:10-9:10PM 12WV L120

Course Description:

Problems and objectives of computer graphics. Vector, curve, and character generation. Interactive display devices. Construction of hierarchical image list. Graphic data structures and graphics languages. Hidden-line problems; windowing, shading, and perspective projection. Curved surface generation display.

Prerequisites: CSCI-GA 1170 and CSCI-UA 140 (or an equivalent undergraduate course in linear algebra).
CSCI-GA.2271-001
(8933) Computer Vision Saining Xie R 7:10-9:10PM Silv 408

Course Description:

Basic techniques of computer vision and image processing. General algorithms for image understanding problems. Study of binary image processing, edge detection, feature extraction, motion estimation, color processing, stereo vision, and elementary object recognition. Mathematical, signal processing, and image processing tools. Relation of computer vision algorithms to the human visual system.

Prerequisites: CSCI-GA 1170
CSCI-GA.2420-001
MATH-GA 2010-001
(15848) Numerical Methods I Benjamin Peherstorfer MW 4:55-6:10PM CIWW 1302

Course Description:
Numerical linear algebra. Approximation theory. Quadrature rules and numerical integration. Nonlinear equations and optimization. Ordinary differential equations. Elliptic equations. Iterative methods for large, sparse systems. Parabolic and hyperbolic equations.

Prerequisites: Corequisite: linear algebra.
CSCI-GA.2433-001
DS-GA.2433-001
(8934) Database Systems Jean-Claude Franchitti R 4:55-6:55PM Meyr 122

Course Description:
Database system architecture. Modeling an application and logical database design. The relational model and relational data definition and data manipulation languages. Design of relational databases and normalization theory. Physical database design. Concurrency and recovery. Query processing and optimization.
CSCI-GA.2434-001
(8935) Advanced Database Systems Dennis Shasha R 4:55-6:55PM 60 Fifth Ave 150

Course Description:
This is a capstone course emphasizing large-scale database systems. This course studies the internals of database systems as an introduction to research and as a basis for rational performance tuning. Topics include concurrency control, fault tolerance, operating system interactions, query processing, and principles of tuning. Database capstone projects involve topics such as design, concurrency control, interactions, and tuning. These projects include some or all of the following elements: formation of a small team, project proposal, literature review, interim report, project presentation, and final report.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250.
CSCI-GA.2436-001
BMIN-GA.2436-001
(8986) Realtime and Big Data Analytics Yang Tang R 7:10-9:10PM Kimmel 808

Course Description:

This course introduces architectures and technologies at the foundation of the Big Data movement. These technologies facilitate scalable management and processing of vast quantities of data collected through realtime and near realtime sensing. We explore tools enabling the acquisition of data in the social domain and the fusion of those data when in flight and at rest using Hadoop and Hadoop-related tools.

The material covered in this course aligns with the prevailing state of the art in Big Data technologies, which continues to be a rapidly evolving landscape as new technologies emerge and existing ones evolve and mature.

Students are required to complete weekly reading and/or programming assignments and demonstrate mastery of course topics by designing, developing, and demonstrating an analytics project of their choosing. Class time will be set aside for project proposal and final demo.

This is an introductory Hadoop course intended for students who have no experience with the Apache Hadoop ecosystem of tools and frameworks.
CSCI-GA.2437-001
(8987) Big Data Application Development Yang Tang T 7:10-9:10PM GCASL 279

Course Description:

This is an introductory course on Spark programming, Spark architecture, Spark SQL, Spark Streaming, and integration of Spark with the Hadoop ecosystem for developing Big Data analytics applications. The course project can be completed with Scala or Python, and Spark. This course covers technologies that integrate well with Spark in the creation of Big Data analytics applications.

Students are required to complete weekly reading and programming assignments and demonstrate mastery of course topics by developing a final project using Spark.

Prerequisites: Students are required to successfully complete CSCI-GA 2436 Realtime and Big Data Analytics prior to taking this course. Students should have experience with Hadoop MapReduce and HDFS.
CSCI-GA.2560-001
(8936) Artificial Intelligence Paul Bethe M 4:55-6:55PM CIWW 101

Course Description:

There are many cognitive tasks that people do easily and almost unconsciously but that have proven extremely difficult to program on a computer. Artificial intelligence is the problem of developing computer systems that can carry out these tasks. This course covers problem solving and state space search; automated reasoning; probabilistic reasoning; planning; and knowledge representation.
CSCI-GA.2565-001
(8937) Machine Learning Matus Telgarsky T 7:10-9:10PM 60 Fifth Ave 150

Course Description:

This course covers a wide variety of topics in machine learning, pattern recognition, statistical modeling, and neural computation. The course covers the mathematical methods and theoretical aspects but primarily focuses on algorithmic and practical issues.

Prerequisites: Undergraduate course in linear algebra and strong programming skills for implementation of algorithms studied in class. Strong knowledge of probability. Recommended: knowledge of vector calculus, elementary statistics.
CSCI-GA.2566-001
(8938) Foundations Of Machine Learning* Mehryar Mohri T 4:55-6:55PM 60 Fifth Ave 110

Course Description:

This course introduces the fundamental concepts and methods of machine learning, including the description and analysis of several modern algorithms, their theoretical basis, and the illustration of their applications. Many of the algorithms described have been successfully used in text and speech processing, bioinformatics, and other areas in real-world products and services. The main topics covered are probability and general bounds; PAC model; VC dimension; perceptron, Winnow; support vector machines (SVMs); kernel methods; decision trees; boosting; regression problems and algorithms; ranking problems and algorithms; halving algorithm, weighted majority algorithm, mistake bounds; learning automata, Angluin-type algorithms; and reinforcement learning, Markov decision processes (MDPs).

Prerequisites: CSCI-GA 1180.
CSCI-GA.2572-001
DS-GA.1008-001
(8998) Deep Learning Yann LeCun
Alfredo Canziani W 4:55-6:55PM 12WV G08

Course Description:

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional net and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

Prerequisites: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.
CSCI-GA.2572-002
DS-GA.1008-002
(8999) Deep Learning Lab STAFF M 3:45-4:35PM 5WP 101 Additional information: Lab for Section 001

Course Description:

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional net and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition.

Prerequisites: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.
CSCI-GA.2630-003
(8975) Foundations of Networks and Mobile Systems* Lakshminarayanan Subramanian W 10:15-12:15PM Silv 414

Course Description:

Only open to MS students in the Computing, Entrepreneurship, and Innovation program

The class will begin with introductory concepts of network protocols across different layers of the network stack including routing, transport, naming, addressing and connect them to the core building blocks of the Internet. The second part of the class will focus on networking concepts in the evolution of Webbased systems, providing an introduction to datacenter networks, clouds, middle boxes (proxies, firewalls) and next generation networks. The third part of the class will focus on the fundamental concepts in wireless networks, cellular networks and mobile devices with a specific focus on mobile programming and applications, 802.11 evolution, mobile services and the evolution of cellular networks.
CSCI-GA.2630-004
(8976) Foundations of Networks and Mobile Systems Lab* Lakshminarayanan Subramanian W 12:30-1:20PM Silv 414 Additional information: Lab for Section 003

Course Description:

Only open to MS students in the Computing, Entrepreneurship, and Innovation program

The class will begin with introductory concepts of network protocols across different layers of the network stack including routing, transport, naming, addressing and connect them to the core building blocks of the Internet. The second part of the class will focus on networking concepts in the evolution of Webbased systems, providing an introduction to datacenter networks, clouds, middle boxes (proxies, firewalls) and next generation networks. The third part of the class will focus on the fundamental concepts in wireless networks, cellular networks and mobile devices with a specific focus on mobile programming and applications, 802.11 evolution, mobile services and the evolution of cellular networks.
CSCI-GA.2810-001
(8977) Design and Innovation* Gary Zamchick F 10:15-12:15PM GCASL 369

Course Description:

Only open to MS students in the Computing, Entrepreneurship, and Innovation program.

This is an intensive idea-incubation, computing-centric design class where students will be exposed to a spectrum of tech challenges, latest and future technology trends (using case studies) and will need to iteratively propose and refine bold computing centric ideas for real problems. Students will also initiate the process of translating their ideas to initial prototypes. Right from the beginning, students in the class will form small teams where each team will work on a single project idea that solves an important real world problem. Students will be provided exposure to basic tools and platforms that can be leveraged by individual teams in their project design and implementation. In addition to idea incubation, the class will provide a beginner's background to tech startups including: product development cycle, how to pitch your idea, product market fit, fundraising and venture capital, customer discovery. Teams are expected to constantly interact with other teams to discuss and exchange ideas.

Prerequisites: None
CSCI-GA.2810-002
(8978) Design and Innovation Lab* Gary Zamchick F 12:30-1:20PM GCASL 369 Additional information: Lab for Section 001

Course Description:

Only open to MS students in the Computing, Entrepreneurship, and Innovation program.

This is an intensive idea-incubation, computing-centric design class where students will be exposed to a spectrum of tech challenges, latest and future technology trends (using case studies) and will need to iteratively propose and refine bold computing centric ideas for real problems. Students will also initiate the process of translating their ideas to initial prototypes. Right from the beginning, students in the class will form small teams where each team will work on a single project idea that solves an important real world problem. Students will be provided exposure to basic tools and platforms that can be leveraged by individual teams in their project design and implementation. In addition to idea incubation, the class will provide a beginner's background to tech startups including: product development cycle, how to pitch your idea, product market fit, fundraising and venture capital, customer discovery. Teams are expected to constantly interact with other teams to discuss and exchange ideas.

Prerequisites: None
CSCI-GA.2820-001
(8979) DevOps and Agile Methodologies John Rofrano T 4:55-6:55PM Online Online

Course Description:

This course uses a project based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.
CSCI-GA.2820-002
(8980) DevOps and Agile Methodologies Lab John Rofrano T 7:10-8:00PM Online Online Additional information: Lab for Section 001

Course Description:

This course uses a project based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.
CSCI-GA.2820-003
(8981) DevOps and Agile Methodologies John Rofrano W 4:55-6:55PM Online Online

Course Description:

This course uses a project based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.
CSCI-GA.2820-004
(8982) DevOps and Agile Methodologies Lab John Rofrano W 7:10-8:00PM Online Online Additional information: Lab for Section 003

Course Description:

This course uses a project based learning approach towards the study of DevOps as a cultural change in Information Technology organizations, and the supporting development tools and automation technologies required to implement it successfully. Students study the principles of DevOps, and as part of an agile development team, each student is involved in planning, designing, building, testing, and deploying one or more cloud native microservices into a Platform as a Service cloud by utilizing a DevOps Pipeline that they will create.
CSCI-GA.2840-001
(8983) Entrepreneurship Capstone Evan Korth M 10:15-12:15PM 194M 306A

Course Description:

Only open to MS students in the Computing, Entrepreneurship, and Innovation program.

The Entrepreneurship Capstone class is a 15week bootcamp class on entrepreneurship. This class aims to translate experiences of students on idea incubation, prototype development and Lean Launchpad towards entrepreneurship. Students will learn how to pitch their ideas to VCs, focus on business development and translate their prototypes to an evolving MVP.
CSCI-GA.2840-002
(8984) Entrepreneurship Capstone Lab Charlie O'Donnell M 12:30-1:20PM 194M 305 Additional information: Lab for Section 001

Course Description:

Only open to MS students in the Computing, Entrepreneurship, and Innovation program.

The Entrepreneurship Capstone class is a 15week bootcamp class on entrepreneurship. This class aims to translate experiences of students on idea incubation, prototype development and Lean Launchpad towards entrepreneurship. Students will learn how to pitch their ideas to VCs, focus on business development and translate their prototypes to an evolving MVP.
CSCI-GA.2965-001
(8939) Heuristic Problem Solving Dennis Shasha R 7:10-9:10PM 60 Fifth Ave C10

Course Description:
This course revolves around several problems new to computer science (derived from games or puzzles in columns for Dr. Dobb’s Journal, Scientific American, and elsewhere). The idea is to train students to face a new problem, read relevant literature, and come up with a solution. The solution entails winning a contest against other solutions. The winner receives candy. The best solutions become part of an evolving “Omniheurist” Web site that is expected to get many visitors over the years. The course is for highly motivated, mathematically adept students. It is open to supported Ph.D. students and well-qualified master’s students. Class size has been around 10 in the past, and instructor and students have all gotten to know one another very well. Algorithmic and programming knowledge is the main prerequisite. It also helps to be familiar with a rapid prototyping language such as Matlab, Mathematica, K, or Python, or to be completely fluent in some other language.

Prerequisites: CSCI-GA 1170 and an ability to prototype algorithms rapidly.
CSCI-GA.3033-025
(8959) Special Topics: Graphics Processing Units (GPUs): Architecture & Programming Mohamed Zahran W 4:55-6:55PM 5WP 101

Course Description:

This course serves as a capstone for the MSCS program.

In this course, we will cover architectural aspects and capabilities of modern GPUs (graphics processing unit) and will learn how to program GPUs to solve different type of problems. Many computations can be performed much faster on the GPU than on a traditional processor. This is why GPUs are present now in almost all computers; and the majority of Top 500 supercomputers in the world are built around GPUs. GPUs are no longer restricted to graphics applications but are now used for a diverse set of applications and domains. This introduces the concept of general-purpose GPUs or GPGPUs, which is the main subject of this course.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250. Basic understudying of operating systems and computer architecture, ability to program in at least one of c/c++, go, or Python languages. Basic understanding of cloud computing systems.
CSCI-GA.3033-034
(8960) Special Topics: Multicore Processors: Architecture & Programming Mohamed Zahran T 4:55-6:55PM Silv 414

Course Description:

This course serves as a capstone for the MSCS program.

The tremendous advances in process technology have created a revolution both in hardware and in software. On the hardware side, we moved from single core processors to multicore/manycore processors. Multicore chips are now everywhere. You can find them in smartphones, playstations, notebooks, all the way up to supercomputers. To benefit from these chips, software must be parallelized, which starts another revolution in software. The purpose of this course is to introduce you to both the hardware advances and parallel programming techniques targeting multicore and manycore processors. You will learn how to make the best use of the underlying hardware to build applications that are faster, more power efficient, and more reliable.
CSCI-GA.3033-061
(8961) Special Topics: Predictive Analytics Anasse Bari W 4:55-6:55PM GCASL 288

Course Description:

Predictive analytics is the art and science of extracting useful information from historical data and present data for the purpose of predicting future trends. In this course, students will have an introduction to the phases of the analytics lifecycle and a basic understanding of a variety of tools and machine learning techniques to analyze data to discover forward insights. Several techniques will be introduced; including data pre-processing techniques, data clustering algorithms, data classification algorithms, association rules data mining algorithms, recommender systems, etc.

Applications from bio-informatics, social networks analytics, and text mining will be covered. Highlights from industrial use cases will be covered to demonstrate how Predictive Analytics relates to improving business performance and impacting better decisions. This is an introductory course that will provide students with basic skills of the new generation of data scientists that will allow them to structure, analyze and derive useful insights from data that could help make better decisions.
CSCI-GA.3033-077
(17590) Special Topics: Big Data and ML Systems Jinyang Li W 10:15-12:15PM 60 Fifth Ave C15

Course Description:

This class will discuss recent research on machine learning systems, esp. those targeted at accelerating deep learning workloads. We will take a deep dive exploring how these systems work so that ML models can be written in a high-level language and executed as low-level kernels on parallel hardware accelerators. Topics covered in this course include: basics of neural networks, how they are programmed and executed by today's deep learning frameworks, automatic differentiation, deep learning accelerators, distributed training techniques, computation graph optimizations, automated kernel generation.

The class will require reading research papers, presenting papers, working on programming labs, and completing a significant final project.

This is a capstone course for the MSCS program.

Prerequisites: The class does require an undergraduate-level knowledge in operating systems, programming languages, algorithms, linear algebra, probability and statistics. If you are new to these concepts, then this class may be advanced for you.
CSCI-GA.3033-078
(20497) Special Topics: Cryptocurrencies and Decentralized Ledgers Joseph Bonneau M 7:10-9:10PM 60 Fifth Ave C15

Course Description:

Bitcoin, Ethereum, and other systems using decentralized ledgers (blockchains) have quickly grown to valuations of hundreds of billions of dollars. Their future potential has captured the imagination of researchers working on applications as diverse as banking and finance, voting, law, corporate governance, gambling and online gaming. This course will cover the technical concepts underlying these systems: append-only ledgers, decentralized consensus, smart contracts and zero-knowledge proof systems. Students will gain working knowledge of both Bitcoin and Ethereum through practical assignments. The course will also survey the wide variety of potential future applications.

Prerequisites: At least one year of experience with a high-level language such as Pascal, C, C++, or Java. An undergraduate course in cryptography or successful completion of CSCI-GA.3205 or CSCI-GA.3210 strongly recommended.
CSCI-GA.3033-085
(8962) Special Topics: Cloud and Machine Learning I-Hsin Chung
Eun Kyung Lee R 4:55-6:55PM 60 Fifth Ave 110

Course Description:

This is a capstone course for the MSCS program. This course exposes students to the practical aspects of the state of art in cloud computing with an emphasis on employing cloud for machine learning problems. The course material introduces students to various cloud technologies such as virtualization and container. Students will learn how to build machine learning systems using cloud computing, the application performance characteristics, and develop hands-on experience with programming machine learning applications on the cloud platforms.

Prerequisites: CSCI-GA 1170, CSCI-GA 2110, and CSCI-GA 2250. Python programming. Students are expected to know basic Linux commands and configurations. Software communication and development tools such as Slack and Github will be used.
CSCI-GA.3033-086
(8970) Special Topics: Quantum Computation Oded Regev M 2:45-4:45PM CIWW 317

Course Description:

This course will provide an introduction to quantum computation and information. We will cover topics such as the quantum circuit model of computation, the Fourier transform, quantum algorithms like Deutsch-Jozsa, Simon, and Grover, Shor's factoring algorithm and discrete log. Topics in quantum information will also be discussed, such as quantum entanglement, teleportation, superdense coding, and quantum nonlocal games. We will also mention quantum cryptography and quantum key distribution. The course will be of interest to students working in computer science, mathematics, or physics.

Prerequisites include a strong undergraduate background in linear algebra (e.g., NYU’s MATH-UA.0140 Linear Algebra), and some familiarity with discrete probability (e.g., NYU’s MATH-UA 235 Probability and Statistics). Familiarity with theory of computation (e.g., NYU’s CSCI-UA.0310 Basic Algorithms) is a plus. No background in physics is required. Evaluation will be based on class participation, homework, and exams.

Prerequisites: Prerequisites include a strong undergraduate background in linear algebra (e.g., NYU’s MATH-UA.0140 Linear Algebra), and some familiarity with discrete probability (e.g., NYU’s MATH-UA 235 Probability and Statistics). Familiarity with theory of computation (e.g., NYU’s CSCI-UA.0310 Basic Algorithms) is a plus. No background in physics is required. Evaluation will be based on class participation, homework, and exams.
CSCI-GA.3033-087
DS-GA.3001-011
(20474) Special Topics: Bayesian Machine Learning Andrew Gordon Wilson W 10:15-12:15AM GCASL 275

Course Description:

This course aims to provide students with a strong grasp of the fundamental principles underlying Bayesian model construction and inference. We will go into particular depth on Gaussian process and probabilistic deep learning models. The course will comprise three units, alongside a major research project component. 1. Model Construction and Inference: Parametric models, support, inductive biases, gradient descent, sum and product rules, graphical models, exact inference, approximate inference (Laplace approximation, variational methods, MCMC), model selection and hypothesis testing, Occam's razor, non-parametric models. 2. Gaussian Processes: From finite basis expansions to infinite bases, kernels, function-space modelling, marginal likelihood, non-Gaussian likelihoods, Bayesian optimization. 3. Bayesian Deep Learning: Fully connected, convolutional, and recurrent networks, generative models, normalizing flows, loss surfaces, deep kernel learning, subspace inference.

Prerequisites: A strong foundation in basic linear algebra, probability, statistics, multivariable calculus, and programming. A careful reading of the first three chapters of Christopher Bishop's Pattern Recognition and Machine Learning (2006) before class starts. Prior courses on machine learning are strongly recommended.
CSCI-GA.3033-091
(8963) Special Topics: Introduction to Deep Learning and LLM based Generative AI Systems Parijat Dube
Chen Wang F 7:10-9:10PM GCASL 279

Course Description:

The course will cover several topics related to Deep Learning (DL) systems and their performance. Both algorithmic and system related building blocks of DL systems will be covered including DL training algorithms, network architectures, and best practices for performance optimization. We will study hyperparameter choices, scalable distributed DL training, Kubernetes based DL system stack on cloud, tools and benchmarks for performance evaluation of DL systems, transfer learning etc. Emphasis will be on getting working knowledge of tools and techniques for performance evaluation of DL systems. The students will gain practical experience working on different stages of DL life cycle, including model development, testing, and deployment. The assignments will be mostly hands-on involving standard DL frameworks (Tensorflow, Pytorch) and open source technologies.

Prerequisites: An introductory machine learning course. Knowledge of python is preferred but not necessary.
CSCI-GA.3033-100 Special Topics: Public Interest Technology Sunoo Park W 12:30-2:30PM 60 Fifth Ave C15

Course Description:

Technologists are in a unique position to utilize their knowledge and skills in the public interest. This course is an introduction to the theory and practice of public interest technology, with substantial projects that will give students the opportunity to gain experience working with external stakeholders on real public-interest issues. For example, students will have the opportunity to work on a mission-driven advocacy project on a current tech policy issue in the US, e.g., related to surveillance, privacy, or AI. Collaborating organizations that students may get to work with include the ACLU, the Policing Project at NYU Law School, the Knight First Amendment Institute at Columbia University, the Surveillance Technology Oversight Project (STOP), and more.

The aim of this course is to elaborate strategies of engagement with broader multidisciplinary social issues related to technology and the public interest. Thus the course consists of two parts: (1) a regular class meeting covering theories, practical approaches, and case studies of public interest technology, and (2) a project-based practicum in which students will work in groups with partners (individuals or organizations) to address a specific public interest issue. This course has no exam; grading will be based on project work and class participation. A majority of students' time will be spent collaboratively in pairs/groups for the project practicum, with weekly instructor-team meetings. In-person class attendance is required, due to the project-based and collaborative nature of the course.

Applications will be evaluated on a rolling basis starting March 24, 2025. Fall 2025 Application form link: https://forms.gle/Sd9BJLLj8WLzEAzp
CSCI-GA.3033-110
(8964) Special Topics: Technologies for Finance CANCELLED -

Course Description:

This is a new masters seminar that will explore topics at the intersection of finance and computing technologies. The course will include a mix of lectures from the instructor, lectures from invited speakers, and assigned readings.

Tentative list of topics:
Network technologies: microwave links, FPGAs, layer 1 switches, multicast, programmable switches, Cloud technologies: Challenges of operating exchange infrastructure in the cloud, Synchronized clocks: The role of synchronized time in stock exchanges: PTP, WhiteRabbit, PPS, Software solutions, etc.; High-frequency trading: History, trading algorithms, limit order books, market making, speed races, batch auctions, AI meets finance: BloombergGPT and related technologies, Secure payments, Programming language techniques: OCaml, Confidential computing for fintech, Central bank digital currencies, Distributed systems: Consensus, designing central exchange servers, etc., Dark pools
CSCI-GA.3033-111
(8965) Special Topics: Protein Design CANCELLED -

Course Description:

This new course will explore recent exciting developments in the application of machine learning and computational methods to the design of new-to-nature proteins. These advances promise a revolution in drug design, scientific tooling, materials and more. The course will consist of a mix of lectures (given by the instructor and external speakers) and seminar-style reading groups of recent papers. Assessment will be through class participation (discussions & paper presentations).

Prerequisites: Graduate-level machine learning class. Some knowledge of molecular biology may be useful, although not essential.
CSCI-GA.3033-117
(8968) Special Topics: Randomized Algorithms Anupam Gupta R 2:45-4:45PM CIWW 312 Additional information: MS students and undergraduate students require instructor permission

Course Description:

Since the early days of algorithm design, randomness has proven itself to be a useful resource for developing provably efficient algorithms and protocols. Randomization allows us to design algorithms that are simpler and more efficient. This course will explore techniques for effectively using randomization and for analyzing randomized algorithms, as well as examples from a variety of settings and problem areas. Topics will include graph algorithms (minimum spanning trees, low-stretch spanning trees, matchings, flows, and cuts), hashing and load balancing, optimization, MCMC and sampling/counting, online algorithms, and also derandomization. The course assumes a good understanding of probability theory and algorithm design, and basic linear algebra.
CSCI-GA.3033-121
(8969) Special Topics: Programming Parallel Algorithms Sam Westrick M 10:15-12:15PM 60 Fifth Ave C10

Course Description:

This course considers the challenge of converting high-level algorithmic ideas into efficient parallel code. We will see how this challenge is greatly simplified by modern programming languages and programming techniques, especially functional programming techniques which make it possible to quickly develop efficient, scalable, and correct implementations. Prior experience with functional programming is helpful but not required. Students will learn how to design, analyze, implement, and evaluate the performance of parallel algorithms across a variety of problem domains (e.g., graph analysis, computational geometry, numerical algorithms, image processing, etc.). In the second half of the course, students will complete a parallel programming project of their own design.

Prerequisites: CSCI-GA.2110-001 Programming Languages. Students should be comfortable with recursive programming techniques and familiar with basic data structures and algorithms.
CSCI-GA.3033-125
(20479) Special Topics: Reinforcement Learning with Foundation Models Sherry Yang M 2:45-4:45PM 60 Fifth Ave 110

Course Description:

Reinforcement learning has traditionally focused on task-specific, tabula rasa settings with limited prior knowledge. Recent research in applying RL on top of foundation models pretrained on internet-scale data has led to LLMs capable of solving a variety of reasoning, coding, and math problems. RL algorithms combined with video world models have also led to fruitful research outcomes in embodied learning. In this course, we will study these RL algorithms and their practical implications when applied to align foundation models in task-specific downstream settings.

This course is designed to be a PhD-level research course. Students taking this course should have successfully completed CSCI-GA.3033-090 Deep Decision Making & Reinforcement Learning usually taught in the spring by Professor Lerrel Pinto. Students enrolled in this class should also be interested in pursuing conference-style publications in Reinforcement Learning.

Prerequisites: Linear algebra, differential equations, CSCI-GA.3033-090 Deep Decision Making & Reinforcement Learning.
CSCI-GA.3033-126
(20472) Special Topics: Mathematical Foundations of Deep Learning and Large Language Models Matus Telgarsky W 7:10-9:10PM 60 Fifth Ave 110

Course Description:

A rigorous, mathematical proof-based mathematical introduction to deep learning and large language models, focusing on questions in approximation, optimization, and generalization, with other areas as time permits. Topics include: constructive and non-constructive approximations and separation results for various deep architectures with and without attention; optimization guarantees with first-order methods both with and without feature learning; maximum margin concepts; Rademacher complexity, VC dimension, and covering number bounds for standard architectures; basic statistical properties of autoregressive generation. Evaluation is based on a course project. The course goal is to prepare students to perform their own research in the field; emphasis will be placed throughout on research-level open problems.
CSCI-GA.3033-127
ECE-GY.9483-A
(20629) Special Topics: Efficient AI Computing: Algorithm and Implementation Sai Qian Zhang W 7:10-9:10PM GCASL 275

Course Description:

The course will focus on recent advancements in the design of efficient neural networks, specifically on how to create and optimize AI models for improved performance, and resource efficiency. Students will explore essential techniques such as pruning, quantization, and model distillation across different model architectures like CNNs, RNNs, Transformers, and LLMs. These techniques aim to reduce computational complexity while preserving accuracy. Additionally, the course will cover efficient training and inference methods, including distributed computing, parallelism, and low-precision computation, essential for deploying AI on resource-constrained platforms. Lastly, students will gain a foundational understanding of computer architectures and learn how to deploy AI algorithms on actual edge devices.

Prerequisites: CSCI-GA.2565-001 Machine Learning or equivalent
CSCI-GA.3033-187
DS-GA.3001-012
(21638) Special Topics in Data Science: Bayesian Machine Learning Lab Yucen Li F 2:45-3:35PM CIWW 101

Course Description:

This course aims to provide students with a strong grasp of the fundamental principles underlying Bayesian model construction and inference. We will go into particular depth on Gaussian process and probabilistic deep learning models. The course will be comprised of three units, alongside a major research project component. 1. Model Construction and Inference: Parametric models, support, inductive biases, gradient descent, sum and product rules, graphical models, exact inference, approximate inference (Laplace approximation, variational methods, MCMC), model selection and hypothesis testing, Occam's razor, non-parametric models. 2. Gaussian Processes: From finite basis expansions to infinite bases, kernels, function-space modelling, marginal likelihood, non-Gaussian likelihoods, Bayesian optimization. 3. Bayesian Deep Learning: Fully connected, convolutional, and recurrent networks, generative models, normalizing flows, loss surfaces, deep kernel learning, subspace inference. Prerequisites: A strong foundation in basic linear algebra, probability, statistics, multivariable calculus, and programming. A careful reading of the first three chapters of Christopher Bishop's Pattern Recognition and Machine Learning (2006) before class starts. Prior courses on machine learning are strongly recommended.

Students must enroll into Lecture section 087 with Lab section 187.
CSCI-GA.3033-925
(8972) Special Topics: Graphics Processing Units (GPUs): Architecture & Programming Recitation Zephyr Luo F 11:15-12:05PM Silv 520 Additional information: Recitation for Section 925

Course Description:

This is the recitation for section 925. Refer to the lecture for description.
CSCI-GA.3033-934
(8973) Special Topics: Multicore Processors: Architecture & Programming Recitation Venkat Nikhil Kotagiri F 12:30-1:20PM GCASL 279 Additional information: Recitation for Section 934

Course Description:

This is the recitation for section 034. Refer to the lecture for description.
CSCI-GA.3033-7863
ROB-GY.7863-A
(23168) Special Topics: Space Robotics Benjamin Riviere M 2:00-4:30PM Jacobs 211

Course Description:

This class will study the math and implementation of planning, learning, and control algorithms through applications in spacecraft, rocket landings, and rover autonomy. We will cover classical topics and emerging research areas in linear and nonlinear dynamics, optimal control, numerical optimization, Bayesian estimation, search-based planning, and reinforcement learning. We will study existing applications like precision rocket landings and martian rover path planning, and proposed future missions like distributed apertures of spacecraft swarms and complex locomotion for planetary exploration.

The goals of the course are: (i) to build fundamental understanding of robot planning, learning, and control, (ii) to introduce applications in aerospace robotics and (iii) to prepare students to become researchers and devise novel methods and algorithms. The grading will be coding-assignments and project-based. This class is targeted towards graduate-level students with familiarity in robotics, linear algebra, differential equations, probability, and Python programming. If you are unsure, feel free to contact Dr. Riviere at riviere.b@nyu.edu.
CSCI-GA.3110-001
(8940) Honors Programming Languages Patrick Cousot TR 3:30-4:45PM 60 Fifth Ave C10

Course Description:

The course will introduce a panorama of programming languages concepts underlying the main programming language paradigms (such as imperative, functional, object-oriented, logic, concurrent, and scripting languages) and present in detail the formal methods (code semantics, specification, and verification) used in modern high quality assurance tools for software safety and security. A programming project (design and implementation of an interpreter/compiler for an dynamic object-oriented mini-language) will be programmed in OCaml, a multiparadigm language introduced at the beginning of the course.

Prerequisites: CSCI-GA.2110 Programming Languages, CSCI-GA.3110 Honors Programming Languages
CSCI-GA.3205-001
(8941) Applied Cryptography & Network Security Mazdak Zamani W 4:55-6:55PM GCASL 275

Course Description:
This course first introduces the fundamental mathematical cryptographic algorithms, focusing on those that are used in current systems. To the extent feasible, the mathematical properties of the cryptographic algorithms are justified, using elementary mathematical tools. Second, actual security mechanisms and protocols, mainly those employed for network traffic that rely on the previously introduced cryptographic algorithms, are presented. The topics covered include introduction to basic number-theoretical properties, public/private and symmetric key systems, secure hash functions, digital signature standards, digital certificates, IP security, e-mail security, Web security, and stand-alone computer privacy and security tools.
CSCI-GA.3210-001
MATH-GA 2170-001
(8942) Introduction To Cryptography Marshall Ball R 7:10-9:10PM 194M 306B

Course Description:
The primary focus of this course is on definitions and constructions of various cryptographic objects, such as pseudorandom generators, encryption schemes, digital signature schemes, message authentication codes, block ciphers, and others, time permitting. The class tries to understand what security properties are desirable in such objects, how to properly define these properties, and how to design objects that satisfy them. Once a good definition is established for a particular object, the emphasis will be on constructing examples that provably satisfy the definition. Thus, a main prerequisite of this course is mathematical maturity and a certain comfort level with proofs. Secondary topics, covered only briefly, are current cryptographic practice and the history of cryptography and cryptanalysis.

Prerequisites: Strong mathematical background
CSCI-GA.3520-001
(8943) Honors Analysis of Algorithms Subhash Khot TR 9:30-10:45AM CIWW 101

Course Description:
Design of algorithms and data structures. Review of searching, sorting, and fundamental graph algorithms. In-depth analysis of algorithmic complexity, including advanced topics on recurrence equations and NP-complete problems. Advanced topics on lower bounds, randomized algorithms, amortized algorithms, and data structure design as applied to union-find, pattern matching, polynomial arithmetic, network flow, and matching.

Prerequisites: Permission of the instructor for master’s students.
CSCI-GA.3812-001
(8974) Information Technology Projects * Jean-Claude Franchitti M 6:10-9:10PM 60 Fifth Ave 110

Course Description:

This is a capstone course that connects students directly with real-world information technology problems. The goal of this course is to teach the skills needed for success in real-world information technology via a combination of classroom lectures and practical experience with large projects that have been specified by local “clients.” The typical clients are primarily companies, but can also be government agencies or nonprofit organizations. Each project lasts for the entire semester and is designed to involve the full software project life cycle. Examples of such projects are development of software to solve a business problem, including specifying requirements, writing and testing prototype code, and writing a final report; and evaluation of commercial software to be purchased to address a business problem, including gathering requirements, designing an architecture to connect the new software with existing systems, and assessing the suitability of available software products.

Prerequisites: For MS in IS students: Successful completion of CSCI-GA 1170 Fundamental Algorithms and two of the following three courses: CSCI-GA 2262 Data Communications & Networks; CSCI-GA 2250 Operating Systems; CSCI-GA 2433 Database Systems. For MS in CS students: Successful completion of CSCI-GA 1170 Fundamental Algorithms, CSCI-GA 2110 Programming Languages, and CSCI-GA 2250 Operating Systems. PERMISSION OF THE DEPARTMENT REQUIRED.
CSCI-GA.3813-001
(8944) Adv Lab MS * STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-002
(8945) Adv Lab MS * STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-003
(8946) Adv Lab MS * STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-004
(8947) Adv Lab MS * STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-005
(8948) Adv Lab PhD ** STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-006
(8949) Adv Lab PhD ** STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-007
(8950) Adv Lab PhD ** STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3813-008
(8951) Adv Lab PhD ** STAFF -

Course Description:

Large-scale programming project or research in cooperation with a faculty member.

Prerequisites: Permission of the faculty project supervisor and the Director of Graduate Studies.
CSCI-GA.3840-001
(8953) Master's Thesis Research * STAFF -

Course Description:

Prerequisites: Approval of a faculty adviser and the Director of Graduate Studies for the M.S. programs.
CSCI-GA.3850-001
(8954) PhD Seminar: Cryptography ** Yevgeniy Dodis T 11:00-1:50PM CIWW 317

Course Description:
Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.

Prerequisites: Permission of the instructor.
CSCI-GA.3850-002
(18147) PhD Seminar: Theory Anupam Gupta R 11:00-1:50PM CIWW 317

Course Description:
Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.

Prerequisites: Permission of the instructor.
CSCI-GA.3850-004
(8955) PhD Seminar: Formal Methods Thomas Wies
Joseph Tassarotti W 11:00-12:30PM 60 Fifth Ave 446

Course Description:
Graduate seminars serve as loosely structured forums for exploring research topics from broad areas of computer science. They are designed to foster dialogue by bringing together faculty and students from a given area and to encourage the exchange of ideas. As such, they bridge the gap between more structured course offerings and informal research meetings. Subject matter varies by section.

Prerequisites: Permission of the instructor.
CSCI-GA.3860-001
(8956) PhD Thesis Research ** STAFF -

Course Description:

Prerequisites: Permission of the thesis adviser or director of graduate studies for the Ph.D. program.
CSCI-GA.3870-001
(8957) Internship In Computer Science MS* STAFF -

Course Description:

Participation in a programming project or research project conducted outside of the classroom in a governmental, commercial, or academic setting. Open only to graduate students with permission of the Director of Graduate Studies (DGS). Students must submit a brief written description of their work to the DGS before starting the internship and submit a written summary of their work when it is completed. MS students may take this course a maximum of two times. PhD students who wish to take this course more than four times need to request a special permission and provide adequate academic justification.

Section 1 is for Master's students. Section 2 is for Ph.D. students.

Prerequisites: Permission of Director of Graduate Studies.
CSCI-GA.3870-002
(8958) Internship in Computer Science PhD* STAFF -

Course Description:

Participation in a programming project or research project conducted outside of the classroom in a governmental, commercial, or academic setting. Open only to graduate students with permission of the Director of Graduate Studies (DGS). Students must submit a brief written description of their work to the DGS before starting the internship and submit a written summary of their work when it is completed. MS students may take this course a maximum of two times. PhD students who wish to take this course more than four times need to request a special permission and provide adequate academic justification.

Section 1 is for Master's students. Section 2 is for Ph.D. students.

Prerequisites: Permission of Director of Graduate Studies.

*

Indicates controlled enrollment (permission number required for registration). Contact your program advisor.
**

Indicates controlled enrollment (permission number required for registration). Contact Santiago Pizzini (pizzini@cs.nyu.edu).
***

Indicates controlled enrollment (permission number required for registration). Contact the instructor.

Classroom Locations

Notice: This webpage is subject to change and will be revised and updated as necessary.