Course Schedule

Current semester Fall 2026, and future semesters are now available to view.

View undergraduate course grid View graduate course grid

Number

Name

Professor(s)

Meet Time

Room

CSCI-GA.1170-001
DS-GA.1170-001
(5644) Fundamental Algorithms Chee Yap M 4:55-6:55PM CIWW 109

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.

Required Foundational course for MSCS, MSIS

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
(5645) Fundamental Algorithms Recitation Bingwei Zhang F 11:15-12:05PM 19UP 102 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.

Required Foundational course for MSCS, MSIS

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
BMIN-GA.1170-001
(5646) Fundamental Algorithms Julia Kempe T 10:15-12:15PM Online

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.

Required Foundational course for MSCS, MSIS

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
DS-GA.1170-002
(5647) Fundamental Algorithms Recitation Bingwei Zhang F 2:45-3:35PM Silv 414 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.

Required Foundational course for MSCS, MSIS

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
(5648) Fundamental Algorithms Recitation Manas Ghai R 11:15-12:05PM 60 Fifth Ave 110 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.

Required Foundational course for MSCS, MSIS

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
(5649) Mathematical Techniques For CS Applications Parijat Dube W 7:45-9:45PM 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
(5650) Programming Languages Joseph Tassarotti T 4:55-6:55PM Meyr 121

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
(5651) Programming Languages Recitation Seong-Heon Jung F 12:30-1:20PM Meyr 122 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
(5652) Programming Languages Cory Plock M 4:55-6:55PM Silv 401

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
(5653) Programming Languages Recitation Satyapragnya Kar F 11:15-12:05PM Silv 401 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
(5654) Programming Languages Recitation Sundara Satish F 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.2112-001
MATH-GA 2043-001
(5722) Scientific Computing Sean Reiter TR 4:55-6:10PM 194M 203

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
(5655) Operating Systems Hubertus Franke M 12:30-2:30PM Silv 408

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
(5656) Operating Systems Yang Tang R 4:55-6:55PM Silv 414

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
(5657) Data Communications and Networks Jean-Claude Franchitti R 7:10-9:10PM 60 Fifth Ave 150

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
(5658) Computer Graphics Kenneth Perlin W 7:10-9:10PM 194M 208

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
(5659) Computer Vision Saining Xie R 7:10-9:10PM 12WP G08

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.

Pre-requisites: Students are expected to have a solid mathematics background and strong programming skills. Students are expected to have completed at least one of these undergraduate courses: 1) Deep Learning, 2) Machine Learning, and 3) Undergraduate Computer Vision. Other requirements include: Python programming; Algorithms and data structure (CSCI-UA.102); Deep learning programming with PyTorch or JAX; Foundations of machine learning; Foundations of deep learning; Linear algebra; Probability and statistics (DS-GA.1002, MATH-UA.140, MATH-UA.235);

Prerequisites: CSCI-GA 1170 Fundamental Algorithms
CSCI-GA.2390-001
(21646) Logic In Computer Science CANCELLED -

Course Description:

Introduction to formal logic: mathematical and computational issues. Topics to be covered include: Propositional logic: Foundations. Axiomatization. Completeness of propositional resolution. Davis-Putnam algorithm. SAT-solving technology.

First-order logic: Axiomatization. Tarskian semantics. First-order resolution. Completeness of resolution. Semi-decidability of inference. First-order logic with equality. Sorted first-order logic. Horn-clause inference.

Modal logic. Dynamic logic. Applications of temporal logics to program language semantics and program verification. Possible-world (Kripkean) semantics. Axiomatizations of propositional modal logics and their completeness proofs.

History of modern (1840-present) formal logic and formalized mathematical proof.

Prerequisites:
Fundamental Algorithms or equivalent.
CSCI-GA.2420-001
MATH-GA 2010-001
(5723) Numerical Methods I Florian Schaefer MW 4:55-6:10PM Silv 208

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
(5660) Database Systems Jean-Claude Franchitti R 4:55-6:55PM 60 Fifth Ave 150

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
(5661) Advanced Database Systems Dennis Shasha R 4:55-6:55PM CIWW 101

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
(5719) Realtime and Big Data Analytics Yang Tang R 7:10-9:10PM Silv 414

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
(5720) Big Data Application Development Yang Tang T 7:10-9:10PM Silv 208

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
(5662) Artificial Intelligence Paul Bethe M 4:55-6:55PM 194M 206

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
(5663) Machine Learning Kyunghyun Cho T 4:55-6:55PM CANTR 101

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.1180 Mathematical Techniques for CS Applications.
CSCI-GA.2572-001
DS-GA.1008-001
(5382) Deep Learning Yann LeCun
Alfredo Canziani M 12:30-2:30PM Cant 102

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
(5384) Deep Learning Lab Alfredo Canziani M 2:45-3:35PM Cant 102 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.2590-001
BMIN-GA 2590-001
(22249) Natural Language Processing Haneul Yoo W 4:55-6:55PM Silv 401

Course Description:

How can we teach machines to understand human languages so that they can answer queries, summarize dense information, or hold a conversation with us? The primary goal of this course is to provide students with the principles and state-of-the-art tools needed to solve a variety of NLP problems. We will focus on three paradigms in the field of NLP: supervised learning, pretrain-then-finetune, and the most recent large language models, with a focus on representation learning techniques. Students are expected to read research papers and gain hands-on experience through coding assignments.

Prerequisites:
Probability, statistics, linear algebra (DS-GA.1002, MATH-UA.140, MATH-UA.235); Algorithms and data structure (CSCI-UA.102); Basic knowledge in machine learning (DS-GA.1003, CSCI-UA.0473). We will not spend a significant amount of time on machine learning basics so some prior exposure to the supervised learning framework (e.g., loss functions, SGD) is expected. Good comfort level with Python
CSCI-GA.2630-003
(5708) Foundations of Networks and Mobile Systems* Lakshminarayanan Subramanian T 10:15-12:15PM GCASL 375 Additional information: Update for Fall 2023, MSCS students can request a permission code by emailing graduate@cs.nyu.edu

Course Description:
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
(5709) Foundations of Networks and Mobile Systems Lab* Lakshminarayanan Subramanian T 12:30-1:20PM GCASL 375 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
(5710) Design and Innovation* Gary Zamchick F 10:15-12:15PM Silv 407 Additional information: Open only to students in the MS-CEI program

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
(5711) Design and Innovation Lab* Gary Zamchick F 12:30-1:20PM Silv 407 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
(5712) DevOps and Agile Methodologies John Rofrano T 4:55-6:55PM Online Additional information: Open only to students in the Stern TECH MBA program

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
(5713) DevOps and Agile Methodologies Lab John Rofrano T 7:10-8:00PM Online Additional information: Lab for Section 001. Open only to students in the Stern TECH MBA program.

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
(5714) DevOps and Agile Methodologies John Rofrano W 4:55-6:55PM 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
(5715) DevOps and Agile Methodologies Lab John Rofrano W 7:10-8:00PM 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
(5716) 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
(5717) Entrepreneurship Capstone Lab Evan Korth
Charlie O'Donnell M 12:30-1:20PM 194M 306B 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
(5664) Heuristic Problem Solving Dennis Shasha R 7:10-9:10PM CIWW 312

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
(5684) Special Topics: Graphics Processing Units (GPUs): Architecture & Programming Mohamed Zahran W 4:55-6:55PM Online

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.3033-061
(5685) Special Topics: Predictive Analytics Anasse Bari R 4:55-6:55PM GCASL 261

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.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-078
(5687) Special Topics: Cryptocurrencies and Decentralized Ledgers Joseph Bonneau M 7:10-9:10PM 60 Fifth Ave 110

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
(5688) 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.3033-091
(5690) Special Topics: Introduction to Deep Learning and LLM based Generative AI Systems Parijat Dube
Chen Wang F 7:10-9:10PM Tisch LC11

Course Description:

This course serves as a graduate-level introduction to Deep Learning systems, with an emphasis on LLM-based Generative AI systems. The course will cover several topics related toDeep 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. The course will have an in-depth exploration of Large Language Models (LLMs), covering key areas towards advanced topics including attention mechanisms, transformer models, prompt engineering, LLM applications including RAG, tool assisted agents, model context protocol (MCP), toolless multi-agents, pre-training strategies, Reinforcement Learning with Human Feedback (RLHF), efficient LLM serving techniques, fine-tuning methods, and benchmarking specifically for LLMs. The students will gain practical experience working on different stages of LLM life cycle, including model pretraining, fine tuning, GenAI application development and deployment. The assignments will be mostly hands-on involving standard LLM frameworks (Pytorch, vLLM) and open-source technologies.

Prerequisites: An introductory graduate level machine learning course. Working knowledge of Python, Pytorch and experience using Jupyter Notebook
CSCI-GA.3033-097
(22335) Special Topics: Virtual Reality* Kenneth Perlin T 7:10-9:10PM 60 Fifth Ave 446

Course Description:

Students will learn how to create shared interactive experiences in Virtual Reality (VR), using an open-source WebXR platform. This is a hands-on projects based course. Topics include design and implementation of interactive tools for collaborative modeling and animation, with a focus on graphical user interface (GUI) design for VR.

To get permission to enroll, email Prof. Perlin at kp1@nyu.edu and forward the permission email to graduate@cs.nyu.edu for the permission code.

Prerequisites: CSCI-GA.2270 Computer Graphics or CSCI-UA.0480-041 Computer Graphics. Permission of the instructor required.
CSCI-GA.3033-110
(5691) Special Topics: Technologies for Finance Anirudh Sivaraman M 7:10-9:10PM CIWW 101

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

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-121
(5692) 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
(21958) Special Topics: Reinforcement Learning with Foundation Models Sherry Yang R 4:55-6:55PM Online

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 and CSCI-GA 2565 Machine Learning
CSCI-GA.3033-127
ECE-GY.9483-A
(5694) Special Topics: Efficient AI Computing: Algorithm and Implementation Sai Qian Zhang W 7:10-9:10PM Silv 206

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.3033-134
(5696) Special Topics: The Science of Deep Learning Pratyusha Sharma M 2:45-4:45PM 60 Fifth Ave 110

Course Description:

Large language models generalize in ways we did not design and fail in ways we did not anticipate. This course is about understanding why. This course will take an empirical approach to unpacking this question: building language models from scratch to develop hands-on fluency, then using that fluency to ask scientific questions about generalization. What does compute-optimal training actually mean? What do data quality, repetition, and domain diversity do to what a model learns? When and why does chain-of-thought help? and more. Students will learn to build transformers from scratch, fit scaling laws, build data pipelines, and run fine-tuning experiments, not as engineering exercises, but as instruments for forming and testing hypotheses. By the end, students will be equipped to read the frontier literature critically, design experiments, and pursue research questions in one of the most consequential and but incompletely understood areas of science.

Prerequisites: deep learning, linear algebra, probability, proficiency in Python and PyTorch.
CSCI-GA.3033-135
(5697) Special Topics: Computational Genomics Eran Halperin T 2:45-4:45PM 60 Fifth Ave 125

Course Description:

This course will bridge classical statistical genomics and modern deep learning, taking students from maximum likelihood estimation through large-scale genomic foundational models. By the end of the semester, students will have hands-on experience with state-of-the-art methods applied to real genomic data.

https://eran-halperin.github.io/CSCI-GA3033-135CompGenomics/

Prerequisites: Python programming experience. Previous coursework in probability, statistics, and linear algebra. Familiarity with machine learning fundamentals is helpful but not required.
CSCI-GA.3033-137
(5698) Special Topics: Information Theory and Applications in CS Marshall Ball M 12:30-2:30PM CIWW 317

Course Description:

In the late 1940s, Claude Shannon introduced a mathematical theory of information, quantifying information in terms of uncertainty. Since Shannon's pioneering work on the limits of data compression and reliable/secure communication, intuitions and techniques from information theory have impacted not just our modern communication infrastructure but also a diverse array of other scientific endeavors, including theoretical computer science. In this course, we will begin by covering the foundations of information theory (entropy, mutual information, KL-divergence, etc.) before branching off to explore various applications, primarily in theoretical computer science. Potential application topics include: channel and source coding, error correcting codes, communication complexity, hardness amplification, data structures, Kolmogorov complexity, information-theoretic cryptography, pseudoentropy, the Lovasz Local Lemma, and applications in combinatorics. While there are no specific prerequisites, fluency in basic probability and mathematical maturity are required.

Prerequisites: No official prerequisites. However, fluency in basic probability and mathematical maturity is required.
CSCI-GA.3033-138
(5699) Special Topics: Approximation Algorithms CANCELLED -

Course Description:

The area of approximation algorithms is aimed at giving provable guarantees on the performance of heuristics for hard problems. The course will cover techniques developed to obtain such algorithms, including the use of linear and convex programming, the use of randomness and linear-algebraic methods, and metric embeddings. Along algorithmic approaches, we will discuss techniques to prove inapproximability, i.e., conditional limits to how well problems can be approximated (typically assuming conjectures like P neq NP, or the UGC). Finally, we will also discuss some ideas behind online algorithms, which studies decision-making under uncertainty, and shared several techniques with approximation algorithms. The aim of the course is to expose students to ideas from all of these threads of research.

Prerequisites: Honors Analysis of Algorithms (CSCI-GA.3520-001), Algorithms 2 (CS-GY 6043), Algorithmic Machine Learning and Data Science (CS-GY 6763), or similar graduate-level Algorithms course. Familiarity with probability theory and linear algebra. Mathematical maturity. If you are not sure you meet the prerequisites, feel free to email the instructor.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-139
(5700) Special Topics: Quantum Cryptography Fermi Ma M 2:45-4:45PM Bobst LL150

Course Description:

Quantum computers will profoundly change the landscape of modern cryptography. Most famously, Shor’s algorithm will enable quantum computers to break much of the cryptography that currently secures the internet. But the impact of quantum computers on cryptography goes far beyond breaking existing cryptosystems. Quantum computers will also open the door to new kinds of cryptographic protocols that leverage quantum phenomena. In recent years, there has been an explosion of activity in this area. Not only has there been a flurry of new quantum cryptographic protocols, but surprisingly, studying these protocols has yielded deep insights into computational complexity and even fundamental physics.

The goal of this course will be to survey recent developments at the intersection of quantum computing and cryptography, with an emphasis on basic concepts and techniques. Since this is a rapidly evolving area, this course will also highlight key open questions and encourage students to formulate their own research problems.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-140
(5701) Special Topics: Honors Machine Learning Gautam Kamath T 4:55-6:55PM Silv 401

Course Description:
This course covers fundamental topics in machine learning. Specific topics covered may include linear regression, logistic regression, k-nearest neighbor, support vector machines, kernels, decision trees, aggregation methods, dimensionality reduction, clustering, optimization, regularization, model evaluation, and more.

Prerequisites: Undergraduate courses 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.1180 Mathematical Techniques for CS Applications.
CSCI-GA.3033-141
(5702) Special Topics: Theoretical Foundations in Modern Machine Learning Allen Liu T 4:55-6:55PM Bobst LL138

Course Description:

The overall goal of this class is to explore various ways that theoretical insights play a role in advancing modern machine learning. We will examine a variety of domains such as diffusion models, large language models, and reinforcement learning, with an emphasis on how theory leads to new practical discoveries.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-142
(5703) Special Topics: Quantum Information Tony Metger W 4:55-6:55PM CIWW 317

Course Description:

This course introduces quantum information theory, the study of how quantum systems act as information carriers. This forms the mathematical foundation for many exciting applications in quantum computing and cryptography. We will explore whether we can use quantum systems and entanglement to transmit classical information more efficiently, how to quantify the amount of quantum information in a system, how to extract classical information from quantum systems, and the role symmetry plays in these tasks. The focus will be on understanding these questions rigorously and introducing mathematical tools that are broadly useful in quantum computing research. A strong background in linear algebra and mathematical maturity are required, but no prior knowledge of quantum physics, theoretical computer science, or representation theory is necessary. This course is suitable for theory-minded graduate students in CS, physics, and math.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-148
(21778) Special Topics: Learning to Match Distributions: Optimal Transport, Flow Matching & Applications Romain Lopez M 4:55-6:55PM 60 Fifth Ave C10

Course Description:

This special topics course explores how to match and transform probability distributions for machine learning applications. The course begins with a few lectures on optimal transport and flow matching, and transitions into a reading group covering contemporary methods and applications across scientific domains. A background in probability and statistics is expected; familiarity with convex optimization is helpful but not required.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3033-925
(5706) Special Topics: Graphics Processing Units (GPUs): Architecture & Programming Recitation Lalita Gupta F 11:15-12:05PM Meyr 122 Additional information: Recitation for Section 025

Course Description:

This is the recitation for section 025. Refer to the lecture for description.

Prerequisites: Prerequisites vary according to topic.
CSCI-GA.3110-001
(5666) Honors Programming Languages Patrick Cousot TR 3:30-4:45PM 60 Fifth Ave 261

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
(5667) Applied Cryptography & Network Security CANCELLED - 60 Fifth Ave 110

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
(5668) Introduction To Cryptography Nir Bitansky W 10:15-12:15PM 60 Fifth Ave C10

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
(5665) Honors Analysis of Algorithms Subhash Khot M 5:10-7:40PM Silv 520

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
(5707) Information Technology Projects * Jean-Claude Franchitti M 6:10-9:10PM Silv 509

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
(5669) 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
(5670) 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
(5671) 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
(5672) 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
(5673) 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
(5674) 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
(5675) 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
(5676) 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
(5677) 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
(5678) PhD Seminar: Cryptography ** Yevgeniy Dodis T 11:00-1:50PM CIWW 317 Additional information: This course also meets at CIWW 317. Please check with your instructor to confirm the schedule.

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
(5679) 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
(5680) 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
(5681) 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
(5682) 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
(5683) 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.