Cornell University Registrar

Designed for students who intend to take CS 1110 or CS 1112 and wish to get a head start, CS 1109 focuses on basic programming concepts and problem analysis and decomposition. The programming concepts to be studied include control flow, function, and list. An appropriate high-level programming language is used.

Programming and problem solving using Python. Emphasizes principles of software development, style, and testing. Topics include procedures and functions, iteration, recursion, arrays and vectors, strings, an operational model of procedure and function calls, algorithms, exceptions, object-oriented programming. Weekly labs provide guided practice on the computer, with staff present to help.

Programming and problem solving using Python. Emphasizes the systematic development of algorithms and programs. Topics include iteration, functions, arrays, strings, recursion, object-oriented programming, algorithms, and data handling and visualization. Assignments are designed to build an appreciation for complexity, dimension, randomness, simulation, and the role of approximation in engineering and science. Weekly discussion section provides guided practice on the computer, with staff present to help.

Introduction to the MATLAB programming language. Covers the basic programming constructs of MATLAB, including assignment, conditionals, iteration, functions, arrays, vectorized computation, scientific graphics, and file input/output. Designed for students who need MATLAB for research or other courses. Does not assume any previous programming experience.

Introduction to the Python programming language. Covers the basic programming constructs of Python, including assignment, conditionals, iteration, functions, object-oriented design, arrays, and vectorized computation. Designed for students who need Python for research or other courses. Does not assume any previous programming experience.

Computing requires difficult choices that can have serious implications for real people. This course covers a range of ethical, societal, and policy implications of computing and information. It draws on recent developments in digital technology and their impact on society, situating these in the context of fundamental principles from computing, policy, ethics, and the social sciences. A particular emphasis will be placed on large areas in which advances in computing have consistently raised societal challenges: privacy of individual data; fairness in algorithmic decision-making; dissemination of online content; and accountability in the design of computing systems. As this is an area in which the pace of technological development raises new challenges on a regular basis, the broader goal of the course is to enable students to develop their own analyses of new situations as they emerge at the interface of computing and societal interests.

This course provides an introduction to data science using the statistical programming language R. We focus on building skills in inferential thinking and computational thinking, guided by the practical questions we seek to answer from data sets arising in medicine, economics and other social sciences. The course starts with essential R programming principles, and how to use R for data manipulation, visualization, and sampling. These techniques are then used to summarize and visualize real data sets, draw meaningful conclusions from those data, and assess the uncertainty surrounding those conclusions. Throughout the process, students will learn to develop hypotheses about their data, and use simulations and statistical techniques to test these hypotheses. The course also covers how to use the Tidyverse open-source R packages to clean and organize complex data sets, and create high quality graphics for data visualization.

Over the centuries, artists in a wide variety of media have employed many approaches to the creative process, ranging from the philosophical to the mechanical to the virtual. This course unravels some of the mysteries going on inside software used for art and music. It looks at ways of breaking things apart and sampling and ways of putting things together and resynthesizing, and explores ideas for creation. This course does not teach software packages for creating art and music. The course complements ART 2701 and MUSIC 1421.

Interdisciplinary survey course designed to introduce students in the creative arts, science, and engineering to the concepts of 2D and 3D digital pictorial representation and display. It is a concept course that concentrates on why rather than how. Topics include perspective representations, display technology, how television works, bandwidth concepts, digital photography, computer graphics modeling and rendering, color perception, 3D data acquisition, volumetric imaging, and historical precedents, primarily from the art world.

This course provides a non-programming introduction to the key ideas of Artificial Intelligence (AI), intended for students without significant technical background in computing. Students will leave with an understanding of the core technical ideas that power modern AI, in such areas as machine learning and neural networks, computer vision, natural processing, game AI, and robotics, as well as our evolving appreciation of the societal implications of work in this area.

This course provides an introduction to the science of the mind. Everyone knows what it's like to think and perceive, but this subjective experience provides little insight into how minds emerge from physical entities like brains. To address this issue, cognitive science integrates work from at least five disciplines: Psychology, Neuroscience, Computer Science, Linguistics, and Philosophy. This course introduces students to the insights these disciplines offer into the workings of the mind by exploring visual perception, attention, memory, learning, problem solving, language, and consciousness.

First-year and Nontechnical Team Projects.

An intermediate introduction to the C++ programming language and the C/C++ standard libraries. Topics include basic statements, declarations, and types; stream I/O; user-defined classes and types; derived classes, inheritance, and object-oriented programming; exceptions and templates. Recommended for students who plan to take advanced courses in computer science that require familiarity with C++ or C.

UNIX and UNIX-like systems are increasingly being used on personal computers, mobile phones, web servers, and many other systems. They represent a wonderful family of programming environments useful both to computer scientists and to people in many other fields, such as computational biology and computational linguistics, in which data is naturally represented by strings. This course takes students from shell basics and piping, to regular-expression processing tools, to shell scripting and Python. Other topics to be covered include handling concurrent and remote resources, manipulating streams and files, and managing software installations.

Intermediate programming in a high-level language and introduction to software engineering. Topics include object-oriented programming (objects, classes, subtypes, encapsulation, polymorphism), program correctness (specifications, invariants, testing), algorithm analysis (asymptotic complexity, big O notation), recursion, data structures (lists, trees, stacks, queues, heaps, hash tables, graphs), iteration and searching/sorting, graph algorithms, and concurrent and event-driven programming (graphical user interfaces, synchronization). Java is the principal programming language.

Intermediate software design and introduction to key computer science ideas. Topics are similar to those in CS 2110 but are covered in greater depth, with more challenging assignments. Topics include object-oriented programming, program structure and organization, program reasoning using specifications and invariants, recursion, design patterns, concurrent programming, graphical user interfaces, data structures as in CS 2110, sorting and graph algorithms, asymptotic complexity, and simple algorithm analysis. Java is the principal programming language.

Balancing environmental, economic, and societal needs for a sustainable future encompasses problems of unprecedented size and complexity. Computing and information science can play an important role in addressing critical sustainability challenges faced by present and future generations. The goal of the course is to introduce students to a range of sustainability challenges and to computational methods that can help address such challenges. Sustainability topics include sustainable development, biodiversity and wildlife conservation, poverty mitigation, food security, renewable resources, energy, transportation, and climate change. In the context of these sustainability topics, the course will introduce students to mathematical and computational modeling techniques, algorithms, and statistical methods. The course is at the introductory undergraduate level. Students are expected to have basic knowledge of probability theory and calculus.

Covers the mathematics that underlies most of computer science. Topics include mathematical induction; logical proof; propositional and predicate calculus; sets, functions, and relations; graphs; combinatorics and discrete mathematics; basic probability theory; and finite-state machines. These topics are discussed in the context of applications to many areas of computer science.

Covers the mathematics that underlies most of computer science. Topics include mathematical induction; logical proof; propositional and predicate calculus; sets, functions, and relations; graphs; combinatorics and discrete mathematics; basic probability theory; and finite-state machines. These topics are discussed in the context of applications to many areas of computer science. This course is an honors version of CS 2800. It will cover essentially the same material but go into more depth.

This interdisciplinary course examines network structures and how they matter in everyday life. The course examines how each of the computing, economic, sociological and natural worlds are connected and how the structure of these connections affects each of these worlds. Tools of graph theory and game theory are taught and then used to analyze networks. Topics covered include the web, the small world phenomenon, markets, neural networks, contagion, search and the evolution of networks.

Advanced programming course that emphasizes functional programming techniques and data structures. Programming topics include recursive and higher-order procedures, models of programming language evaluation and compilation, type systems, and polymorphism. Data structures and algorithms covered include graph algorithms, balanced trees, memory heaps, and garbage collection. Also covers techniques for analyzing program performance and correctness.

A project-based course in which programmers and designers collaborate to make a computer game. This course investigates the theory and practice of developing computer games from a blend of technical, aesthetic, and cultural perspectives. Technical aspects of game architecture include software engineering, artificial intelligence, game physics, computer graphics, and networking. Aesthetic and cultural include art and modeling, sound and music, game balance, and player experience.

Introduction to computational mathematics covering topics in (numerical) linear algebra, statistics, and optimization. Topics included are those of particular relevance to upper-division computer science courses in machine learning, numerical analysis, graphics, vision, robotics, and more. An emphasis is placed both on understanding core mathematical concepts and introducing associated computational methodologies.

This course will cover technologies for representing, modeling and displaying data in the context of interactive web pages. Practical skills for building web pages will be mixed with data mining algorithms and visualization design theory. We will use the D3 Javascript library to develop both static and dynamic visualizations, learn more about programming in Javascript, and explore web scalable vector graphics (SVG). Through design critique and formal study, we will identify the techniques visualization developers employ to create the right visualization for a given use case.

Introduction to computer organization, systems programming and the hardware/ software interface. Topics include systems programming, instruction sets, computer arithmetic, data formats, memory hierarchies including caches and virtual memory, operating systems, and parallel programming. Students learn assembly language programming and the C programming language.

An introduction to the design of embedded systems, with an emphasis on understanding the interaction between hardware, software, and the physical world. Topics covered include assembly language programming, interrupts, I/O, concurrency management, scheduling, resource management, and real-time constraints.

Introduction to major topics in artificial intelligence, including heuristic search, game-playing, knowledge representation and reasoning, planning, probabilistic inference, sequential decision-making and reinforcement learning.

The course provides an introduction to machine learning, focusing on supervised learning and its theoretical foundations. Topics include regularized linear models, boosting, kernels, deep networks, generative models, online learning, and ethical questions arising in ML applications.

Earn course credit for working as a consultant or ugrad TA in a computer science course.

An introduction to the theory, design, and implementation of programming languages. Topics include operational semantics, type systems, higher-order functions, scope, lambda calculus, laziness, exceptions, side effects, continuations, objects, and modules. Also discussed are logic programming, concurrency, and distributed programming.

An introduction to the specification and implementation of modern compilers. Topics covered include lexical scanning, parsing, type checking, code generation and translation, an introduction to program analysis and optimization, and compile-time and run-time support for modern programming languages. As part of the course, students will build a working compiler for an object-oriented language.

Students will build a working compiler for an object-oriented language.

Project-based follow-up course to CS 3152. Students work in a multidisciplinary team to develop a game that incorporates innovative game technology. Advanced topics include 3D game development, mobile platforms, multiplayer gaming, and nontraditional input devices.There is a special emphasis on developing games that can be submitted to festivals and competitions.

An introduction to formal verification, focusing on correctness of functional and imperative programs relative to mathematical specifications. Topics include computer-assisted theorem proving, logic, programming language semantics, and verification of algorithms and data structures. Assignments involve extensive use of a proof assistant to develop and check proofs.

Introduction to the fundamentals of numerical analysis: error analysis, approximation, interpolation, and numerical integration. In the second half of the course, we use these to build approximate solvers for ordinary and partial differential equations. Strong emphasis is placed on understanding the advantages, disadvantages, and limits of applicability for all the covered techniques. Computer programming is used to test the theoretical concepts throughout the course. Students will be expected to be comfortable writing proofs and have knowledge of programming. MATH 4250/CS 4210 and MATH 4260/CS 4220 can be taken independently from each other and in either order. Together they provide a comprehensive introduction to numerical analysis.

Introduction to the fundamentals of numerical linear algebra: direct and iterative methods for linear systems, eigenvalue problems, singular value decomposition. In the second half of the course, the above are used to build iterative methods for nonlinear systems and for multivariate optimization. Strong emphasis is placed on understanding the advantages, disadvantages, and limits of applicability for all the covered techniques. Computer programming is required to test the theoretical concepts throughout the course.

How to make sense of the vast amounts of information available online, and how to relate it and to the social context in which it appears? This course introduces basic tools for retrieving and analyzing unstructured textual information from the web and social media. Applications include information retrieval (with human feedback), sentiment analysis, and social analysis of text. The coursework will include programming projects that play on the interaction between knowledge and social factors.

Introduction to modern database and data storage systems. Concepts covered include data models, query languages, database designs, storage structures, access methods, query processing and optimization, transaction management, and recovery in both relational and nonrelation data storage systems.

Students build part of a database system in Java.

Introduction to the design of systems programs, with emphasis on multiprogrammed operating systems. Topics include concurrency, synchronization, deadlocks, memory management, protection, input-output methods, networking, file systems and security. The impact of network and distributed computing environments on operating systems is also discussed.

Studies the practical aspects of operating systems through the design and implementation of an operating system kernel that supports multiprogramming, virtual memory, and various input-output devices. All the programming for the project is in a high-level language.

CS 4414 exposes students to programming applications at the systems level and to the operating systems abstractions that these applications depend on. It then builds on this foundation to look at systems issues that shape the performance and reliability of modern ML and AI applications, such as “chat bots” and question-answering AIs. We do not expect students to understand how these ML and AI tools work, in a mathematical sense. Instead our focus is on how the execute, where components run and how they talk one another, how they interact with big-data storage, and how they leverage accelerators such as GPU.

This course aims to provide a strong foundation for students to understand modern computer system architecture and to apply these insights and principles to future computer designs. The course is structured around the three primary building blocks of general-purpose computing systems: processors, memories, and networks. The first half of the course focuses on the fundamentals of each building block. Topics include processor microcoding and pipelining; cache microarchitecture and optimization; and network topology, routing, and flow control. The second half of the course delves into more advanced techniques and will enable students to understand how these three building blocks can be integrated to build a modern shared-memory multicore system. Topics include superscalar execution, branch prediction, out-of-order execution, register renaming and memory disambiguation; VLIW, vector, and multithreaded processors; memory protection, translation, and virtualization; and memory synchronization, consistency, and coherence. This course includes a significant project decomposed into five lab assignments. Throughout the semester, students will gradually design, implement, test, and evaluate a complete multicore system capable of running real parallel applications at the register-transfer level.

This course introduces the basic architectural and design principles of computer networking including the design of communication protocols, congestion control, routing and switching, Internet, data center networks and wireless networks.

Introduction to the principles of computer graphics in two and three dimensions. Topics include digital images, filtering and antialiasing, 2-D and 3-D affine geometry, ray tracing, perspective and 3-D viewing, the graphics pipeline, curves and surfaces, and human visual perception. This course emphasizes fundamental techniques in graphics, with both written and practical assignments. May be taken with or without concurrent enrollment in CS 4621.

Provides CS 4620 students with hands-on experience in computer graphics programming on modern graphics hardware. This course emphasizes effective use of graphics APIs and the architecture of graphics applications. A final project involves building a substantial interactive graphics system. The course uses Javascript and WebGL for code development.

An in-depth introduction to computer vision. The goal of computer vision is to compute properties of our world-the 3D shape of an environment, the motion of objects, the names of people or things-through analysis of digital images or videos. The course covers a range of topics, including 3D reconstruction, image segmentation, object recognition, and vision algorithms fro the Internet, as well as key algorithmic, optimization, and machine learning techniques, such as graph cuts, non-linear least squares, and deep learning. This course emphasizes hands-on experience with computer vision, and several large programming projects.

Artificial Intelligence project class. Topic choice is student driven and (small) teams are encouraged, but each individual team member must do substantial implementation with significant average weekly time commitment. Possible projects topics include knowledge representation systems, search procedures, game-playing, automated reasoning, machine learning, genetic algorithms, planning, natural language processing, computer vision.

This course constitutes an introduction to natural language processing (NLP), the goal of which is to enable computers to use human languages as input, output, or both. NLP is at the heart of many of today's most exciting technological achievements, including machine translation, question answering and automatic conversational assistants. The course will introduce core problems and methodologies in NLP, including machine learning, problem design, and evaluation methods.This class satisfies the practicum/project requirement for CS majors. As a consequence, expect each of the roughly four connected programming assignments to take tens of hours, although this time is distributed over multiple weeks; to require writing code to massage raw-ish data into different formats and other accessory functions as well as to implement core algorithms; and to necessitate much independent examination of documentation.

Computational models of natural languages. Topics are drawn from: tree syntax and context free grammar, finite state generative morpho-phonology, feature structure grammars, logical semantics, tabular parsing, Hidden Markov models, categorial and minimalist grammars, text corpora, information-theoretic sentence processing, discourse relations, and pronominal coreference.

An in-depth exploration of modern computational linguistic techniques. A continuation of LING 4424 - Computational Linguistics I. Whereas LING 4424 covers foundational techniques in symbolic computational modeling, this course will cover a wider range of applications as well as coverage of neural network methods. We will survey a range of neural network techniques that are widely used in computational linguistics and natural language processing as well as a number of techniques that can be used to probe the linguistic information and language processing strategies encoded in computational models. We will examine ways of mapping this linguistic information both to linguistic theory as well as to measures of human processing (e.g., neuroimaging data and human behavioral responses).

Robotics is interdisciplinary and draws inspiration from many different fields towards solving a variety of tasks in real-world environments using physical systems. This course is a challenging introduction to basic computational concepts used broadly in robotics. By the end of this course, students should have a fundamental understanding of how the different sub-fields of robotics such as kinematics, state-estimation, motion planning, and controls come together to develop intelligent behaviors in physical robotic systems. The mathematical basis of each area will be emphasized, and concepts will be motivated using common robotics applications. Students will be evaluated using a mixture of theoretical and programming exercises throughout the semester.

"Re-Designing Robots" is a studio-based graduate course focused on building and deploying robots in real-world settings such as homes, workplaces, and public spaces. Students will work individually and collaboratively in interdisciplinary teams, incorporating perspectives from engineering, computer science, art, design, and social sciences. The course emphasizes critical examination of the societal roles and ethical implications of robotic technologies, encouraging students to ask not just how robots can function better, but how they can meaningfully and responsibly enhance human activities. Key topics include prototyping interactive robotic systems, video interaction analysis, and hands-on experimentation with physical robot prototypes. Through immersive, hands-on projects, students will develop the expertise to thoughtfully design robots that integrate seamlessly and responsibly into everyday life.

How do we get robots out of the labs and into the real world with all it's complexities? Robots must solve two fundamental problems -- (1) Perception: Sense the world using different modalities and (2) Decision making: Act in the world by reasoning over decisions and their consequences. Machine learning promises to solve both problems in a scalable way using data. However, it has fallen short when it comes to robotics. This course dives deep into robot learning, looks at fundamental algorithms and challenges, and case-studies of real-world applications from self-driving to manipulation.

Creating robots capable of performing complex tasks autonomously requires one to address a variety of different challenges such as sensing, perception, control, planning, mechanical design, and interaction with humans. In recent years many advances have been made toward creating such systems, both in the research community (different robot challenges and competitions) and in industry (industrial, military, and domestic robots). This course gives an overview of the challenges and techniques used for creating autonomous mobile robots. Topics include sensing, localization, mapping, path planning, motion planning, obstacle and collision avoidance, and multi-robot control.

Computational methods for analyzing genetic and genomic data. Topics include sequence alignment, hidden Markov Models for discovering sequence features, motif finding using Gibbs sampling, phylogenetic tree reconstruction, inferring haplotypes, and local and global ancestry inference. Prior knowledge of biology is not necessary to complete this course.Grad students must do a final project that involves original research and that in most circumstances will involve programming and real data.Undergrads will not need to do research but will do a final project that involves a sizeable amount of programming, comprehensive literature review of a topic, or similar.

This class is an introductory course to deep learning. It covers the fundamental principles behind training and inference of deep networks, the specific architecture design choices applicable for different data modalities, discriminative and generative settings, and the ethical and societal implications of such models.

Machine Learning (ML) is a ubiquitous technology. This course, which is a follow up to an introductory course on ML will cover topics that aim to provide a theoretical foundation for designing and analyzing ML algorithms. This course has three basic blocks. First block will provide basic mathematical and statistical toolset required for formalizing ML problems effectively and analyzing them. This block will include topics like generalization, sample complexity of learning algorithm and understanding the inherent challenges in various ML frameworks and models. The second block will provide the foundations in algorithms design and optimization techniques required for building and analyzing various ML algorithms. This block will cover topics like gradient descent, stochastic gradient descent, algorithm design for online learning and computational challenges in ML. ML algorithms are deployed in real world and make decisions that affect real world users. The third block, will cover topics on how to formally reason about and how to design ML methods that address social and user related concerns that ML algorithms need to deal with. This block will cover topics such as fairness, privacy, the right to be forgotten and other such issues and how to build ML algorithms that address or assuage these concerns.

An introduction to the mathematical and algorithms design principles and tradeoffs that underlie large-scale machine learning on big training sets. Topics include: stochastic gradient descent and other scalable optimization methods, mini-batch training, accelerated methods, adaptive learning rates, parallel and distributed training, and quantization and model compression.

Reinforcement Learning is one of the most popular paradigms for modelling interactive learning and sequential decision making in dynamical environments. This course introduces the basics of Reinforcement Learning and the Markov Decision Process. The course will cover algorithms for planning and learning in Markov Decision Processes. We will discuss potential applications of Reinforcement Learning and their implications. We will study and implement classic Reinforcement Learning algorithms.

An introduction to the classical theory of computing: automata theory, formal languages, and effective computability. Topics include finite-state machines, regular languages, regular expressions, grammars, context-free languages, pushdown automata, Turing machines, recursive and recursively enumerable sets, diagonalization, reductions, undecidability, Godel's incompleteness theorem. A complete list of topics can be found on the schedule page (subject to change). As time permits, we will explore some more modern advances such as coalgebraic methods, abstract interpretation, and concurrency.

Quantum computing is an interdisciplinary field that lies at the intersection of computer science, mathematics, and physics. This computational paradigm relies on principles of quantum mechanics, such as superposition and entanglement, to obtain powerful algorithms. This course aims to provide a self-contained, comprehensive introduction to quantum computing, focusing on the design and analysis of quantum algorithms, as well as covering topics in quantum information and quantum cryptography.

Explores the power and limitations of efficient computation. Understanding how the notion of efficient computation changes with respect to resources such as time, space, randomness, advice, and interaction. Concrete computational models that we will study will include Turing machines, Boolean circuits, Decision trees, and Branching Programs. Advanced topics may include error-correcting codes, probabilistic checkable proofs, and circuit lower bounds.

Develops techniques used in the design and analysis of algorithms, with an emphasis on problems arising in computing applications. Example applications are drawn from systems and networks, artificial intelligence, computer vision, data mining, and computational biology. This course covers four major algorithm design techniques (greedy algorithms, divide-and-conquer, dynamic programming, and network flow), undecidability and NP-completeness, and algorithmic techniques for intractable problems (including identification of structured special cases , approximation algorithms, local search heuristics, and online algorithms).

A rigorous introduction to the theoretical foundations of the cryptography that powers much of the modern world. Topics include one-way functions, secret-key encryption, zero-knowledge proofs, signatures, public-key encryption etc. As this is a theoretical class, the emphasis will be on formal definitions and proofs.

Covers the mathematical foundations for access to information. Topics include high dimensional space, random graphs, singular value decomposition, Markov processes, learning theory, and algorithms for massive data.

Networks II builds on its prerequisite course and continues to examine how each of the computing, economic, sociological and natural worlds are connected and how the structure of these connections affects these worlds. In this course, we will construct mathematical models for and analyze networked settings, allowing us to both make predictions about behavior in such systems, as well as reason about how to design such systems to exhibit some desirable behavior. Throughout, we will draw on real-world examples such as social networks, peer-to-peer filesharing, Internet markets, and crowdsourcing, that illustrate these phenomena.

Topics chosen from propositional logic, first-order logic, and higher-order logic, both classical and intuitionistic versions, including completeness, incompleteness, and compactness results. Natural deduction and tableaux style logics and connection to the lambda calculus and programming languages and logics, and program verification. Other topics chosen from equational logic, Herbrand universes and unification, rewrite rules and Knuth-Bendix method, and the congruence-closure algorithm and lambda-calculus reduction strategies. Modal logics, intuitionistic logic, computational logics and programming languages (e.g., LISP, ML, or Nuprl). Students will be expected to be comfortable writing proofs. More experience with proofs may be gained by first taking a 3000-level MATH course.

This independent study course offers CS majors (i.e., undergraduates whose applications to affiliate with the CS major have been approved) an opportunity to reflect on concepts from computer science as they were encountered and applied in a recent internship. Students write a short paper describing their work experience and how it connects to the educational objectives of the computer science major.

Advanced independent work in computer science as part of a student-led team project.

Independent reading and research for undergraduates.

An introduction to the theory, design, and implementation of programming languages. Topics include operational semantics, type systems, higher-order functions, scope, lambda calculus, laziness, exceptions, side effects, continuations, objects, and modules. Also discussed are logic programming, concurrency, and distributed programming.

Fundamental algorithms and data structures used in current applications. Algorithms include graph algorithms, hashing and streaming/sketching techniques. Applications will include selected topics in storage and memory systems, machine learning and security.

This course provides an introduction to novel programming languages for controlling computer networks. It will examine recent proposals based on logic, functional, and streaming languages, as well as tools for establishing the correctness of programs written in such languages using SAT/SMT solvers, model checkers, and proof assistants. Evaluation will be based on class participation and projects.

An introduction to the specification and implementation of modern compilers. Topics covered include lexical scanning, parsing, type checking, code generation and translation, an introduction to program analysis and optimization, and compile-time and run-time support for modern programming languages. As part of the course, students will build a working compiler for an object-oriented language.

Students will build a working compiler for an object-oriented language.

This course in Discrete Optimization is focused on Nondeterministic Polynomial-hard problems but with a very strong focus on the use of Mixed-Integer Linear Programming, general-purpose solvers to attack them.

Introduction to the practical problems of specifying, designing, building, testing, and delivering reliable software systems. Special topics include professionalism, project management, and the legal framework for software development. As a central part of the course, student teams carry out projects for real clients, using concepts of agile software development. Each project includes all aspects of software development from a feasibility study to final delivery.

Students work in a multidisciplinary team to develop a game that incorporates innovative game technology. Advanced topics include 3D game development, mobile platforms, multiplayer gaming, and nontraditional input devices. There is a special emphasis on developing games that can be submitted to festivals and competitions.

Software testing is a widely used approach for detecting flaws in software. Systematic and organized approaches to software testing will be covered, including test adequacy criteria, manual and automatic generation of test inputs, regression testing, debugging, and dynamic analyses for detecting known classes of software errors.

An introduction to formal verification, focusing on correctness of functional and imperative programs relative to mathematical specifications. Topics include computer-assisted theorem proving, logic, programming language semantics, and verification of algorithms and data structures. Assignments involve extensive use of a proof assistant to develop and check proofs.

Models for parallel programming and survey of parallel machines. Existing parallel programming languages, vectorizing compilers, and parallel libraries and toolboxes.

Introduction to the fundamentals of numerical linear algebra: direct and iterative methods for linear systems, eigenvalue problems, singular value decomposition. In the second half of the course, the above are used to build iterative methods for nonlinear systems and for multivariate optimization. Strong emphasis is placed on understanding the advantages, disadvantages, and limits of applicability for all the covered techniques. Computer programming is required to test the theoretical concepts throughout the course.

Massive amounts of data are collected by many companies and organizations and the task of a data scientist is to extract actionable knowledge from the data - for scientific needs, to improve public health, to promote businesses, for social studies and for various other purposes. This course will focus on the practical aspects of the field and will attempt to provide a comprehensive set of tools for extracting knowledge from data.The course will cover the topics needed to solve data-science problems, which include problem formulation (business understanding), data preparation (collection, sampling, integration, cleaning), data modeling (characterization, model selection, and analysis), implementation (large-scale data processing, feedback loops, QA) and communication (data presentation, visualization). Advanced topics such as causal inference and processing streaming data will be presented.Throughout the course, the students will perform a data-science mission with all the required steps, from problem formulation to result presentation.

Crowdsourcing and human computation refer to various ways that computing has brought together human labor to achieve outcomes that were previously beyond our individual capabilities or expectations. Wikipedia's millions of articles, the success of Linux and other open source software projects, citizen science, and the generation of large datasets for machine learning and artificial intelligence using microlabor are all examples of ways in which technology and people together have exceeded the capabilities of people or machines in isolation. This course will survey the state of the art in this area and give practical knowledge of the area, grounded in numerous examples in diverse settings.

Introduction to modern database and data storage systems. Concepts covered include data models, query languages, database design, storage structures, access methods, query processing and optimization, transaction management, and recovery in both relational and nonrelational data storage systems.

Students build part of a real database system in C++ Java.

Trust & Safety is an emerging field that focuses on reducing the harm from interpersonal abuse in digital spaces. The abuse types involved - harassment, misinformation, unwanted sexual content - are often lawful but awful, requiring developers to build their own socio-technical frameworks of what is appropriate behavior in their platform. In this course, we will look at digital abuse through an analysis of historical incidents. We will study how the field developed standards across algorithmic response, product design and manual removals. Students will join teams to describe an emerging online abuse type, develop appropriate moderation pipelines (e.g., using modern machine learning such as LLMs), and detail associated policies, all in an environment mimicking the realities seen in practice. This course might expose students to disturbing material.

This course aims to bridge the gap between academic studies of computer science and production software engineering. The course provides a fast-paced introduction to key tools and techniques that can facilitate the building of prototypes and of actual working systems. It introduces technologies for building Web applications and mobile applications, systems for effective storage of data, and tools that support and ease code writing, such as distributed version-control systems, editors and debuggers.

Algorithms increasingly guide high-stakes decision-making across many domains. This has potential upsides, since algorithms can improve decision-making, but also serious risks, since recent years have showcased the many ways that algorithms can be biased. This course will teach you principles for designing fair algorithms, emphasizing accessibility to a broad audience via practical takeaways which are directly relevant to the real world through case studies and guest speakers. Case studies will be drawn from diverse settings where algorithms are applied, such as large language models, speech recognition systems, healthcare, criminal justice, sustainability, and education. Students will come away with a strong understanding of how algorithm-related choices can have widespread societal impact.

Introduction to the design of systems programs, with emphasis on multiprogrammed operating systems. Topics include concurrency, synchronization, deadlocks, memory management, protection, input-output methods, networking, file systems and security. The impact of network and distributed computing environments on operating systems is also discussed.

Studies the practical aspects of operating systems through the design and implementation of an operating system kernel that supports multiprogramming, virtual memory, and various input-output devices. All the programming for the project is in a high-level language.

Focuses on cloud computing, large-scale Internet applications, and other practical issues in designing and implementing trustworthy, scalable distributed software.

Studies the abstractions and algorithms that constitute the foundations for implementing concurrent and distributed computing, with emphasis on supporting fault-tolerance. Topics vary to reflect advances in the field but typically include global state snapshots, causality and clocks (logical and physical), agreement and consensus, primary-backup and state-machine replication, quorums, and gossip. Students undertake a substantial software project to put these ideas into practice. Many students obtain additional project credit by co-registering in CS 4999 or CS 5999.

CS 5416 is a course focused on the systems aspects of performance for cloud-hosted AI and ML applications such as LLMs, as well as complex systems in which AI or ML is just one element. The T/R lectures are shared with an undergraduate class, CS 4414 but the (required) recitation sections cover content that the CS 4414 students will not see and the last of the multi-stage projects will focus on cloud AI and ML scenarios, while the last of the CS 4414 projects focuses on a single-computer scenario. Thus even though you will overlap with Cornell seniors (and some juniors) in the lecture hall, you will also gain additional perspective and knowledge in recitation and will be doing a more ambitious style of project that comes very close to what companies are undertaking to integrate AI into their cloud solutions. The CS 4414 students will have a separate discussion board than you, and although you do take the same prelim exams, your grading is more focused on projects (25% exams, 75% projects, in contrast to the 50-50 balance for CS 4414). CS 5416 starts by exposing students to programming applications at the systems level and to the operating systems abstractions that these applications depend on. It then builds on this foundation to look at systems issues that shape the performance and reliability of modern ML and AI applications, such as “chat bots” and question-answering AIs. We do not expect students to learn how these ML and AI tools work, in a mathematical sense. Instead our focus is on how they execute, where components run and how they talk one another, how they interact with big-data storage, and how they leverage accelerators such as GPU. CS 4414 students who attend these cloud-computing and ML lectures won’t be using the material in their projects, whereas CS 5416 students will gain hands-on experience working with and optimizing ML and AI applications that have this form. The required recitation is where you will learn about the additional tools and techniques that are needed to perform those tasks

This course discusses advanced topics in computer architecture beyond the material that is covered in undergraduate courses such as ECE 4750/CS 4420. In particular, the course places special focus on multicore and multiprocessor architectures (coherence, consistency, synchronization, interconnects, OS support, etc.), as well as advanced architecture techniques (simultaneous multithreading, speculative loads and stores, neural branch predictors, hardware resource management, memory scheduling, etc.) Students work on parallel programming assignments that emphasize hardware-aware performance optimization.

This course covers the human-centered and technical workings behind interactive devices ranging from cell phones and video game controllers to household appliances and smart cars. This is a hands-on, lab-based course. For the final project, students will build a functional IoT prototype of their own design, using Python, single-board Linux computer, embedded microcontrollers, and/or other electronic components. Topics include electronics prototyping, interface design, sensors and actuators, microcontroller development, physical prototyping, and user testing.

Surveys security for computers and communications networks, including policy issues (e.g., the national debates on cryptography policy) as well as technical approaches for implementing properties that comprise trustworthiness for a computing system: confidentiality, integrity, availability, and assurance. Discusses cryptographic protocols and other mechanisms for implementing authorization and authentication in hardware and in software.

Viewed variously as a niche currency for online criminals and a technological threat to the financial industry, Bitcoin has fueled myth-making and financial speculation, as well as real innovation. In this course, we will study not just Bitcoin, but the rich technological landscape it has inspired and catalyzed, especially smart contracts and Web3. Topics will include: the mechanics of consensus algorithms and their role in blockchains and cryptocurrencies; cryptographic tools employed in cryptocurrencies, including hash functions, digital signatures, and zero-knowledge proofs; the evolution and mechanics of Bitcoin and its ecosystem; smart contracts; and special topics, such as trusted hardware in blockchain-based systems, crime, and NFTs. Grading will be based on homework assignments and a final exam.

This course is about safety, security, privacy, alignment, and adversarial robustness of modern AI and ML technologies. Topics include threats and risks specific to these technologies, understanding vulnerabilities and state-of-the-art defenses, and how to build and use trustworthy AI/ML systems.

This course is a broad overview of modern computer security and digital privacy. It aims to impart technical and social understanding of how and why security and privacy matter, how to think adversarially, and how (and how not) to design secure systems and products. Topics include authentication, Web and mobile security, network, OS, and low-level software security, elements of applied cryptography, privacy protection technologies, censorship resistance, and security and privacy of emerging platforms, illustrated by studying real-world systems and attacks. Key learning objectives include understanding the role of threat modeling in the design and evaluation of modern computing systems, how exploitation of computing systems works, and approaches to finding and remediating vulnerabilities.

This course introduces students to privacy technologies and surveys the current state of digital privacy from multiple perspectives, including technology, law, policy, ethics, economics, and surveillance.

This course introduces students to the design and implementation of networked and distributed systems. Topics include the basics of networking (including Internet architecture, TCP/IP, Wi-Fi, and routing), distributed protocols, foundations of cloud computing, reliability, fault tolerance, and security in distributed systems, and introduction to performance evaluation. Course labs and projects include a significant implementation component and require working knowledge of C/C++.

This course introduces the basic architectural and design principles of computer networking including the design of communication protocols, congestion control, routing and switching, Internet, data center networks and wireless networks.

Systems for Large-scale ML is a new advanced course at Cornell University designed to equip students with hands-on expertise in designing and operating scalable machine learning systems. With the rise in popularity of large ML models like GPT, LLaMA, and DeepSeek, tackling systems-level challenges of distributing training and inference workloads on multi-accelerator hardware while ensuring fault tolerance has become a crucial skill for both graduate and undergraduate students in computer science. The course will teach students to tackle systems challenges in both training and inferring from large-scale ML models. We will combine theory and hands-on teaching through lectures, assignments, and projects.

Introduction to the principles of computer graphics in two and three dimensions. Topics include digital images, filtering and antialiasing, 2-D and 3-D affine geometry, ray tracing, perspective and 3-D viewing, the graphics pipeline, curves and surfaces, and human visual perception. Homework assignments require some Java programming.

Provides CS 4620 students with hands-on experience in computer graphics programming on modern graphics hardware. This course emphasizes effective use of graphics APIs and the architecture of graphics applications. A final project involves building a substantial interactive graphics system. The course uses Javascript and WebGL for code development.

Methods for interactive computer graphics, targeting applications including games, visualization, design, and immersive environments. Introduces students to state-of-the-art interactive techniques and programmable shading. Programming assignments use C++ and OpenGL, and students also propose and implement an open-ended final project.

This course introduces students to fundamental physically based modeling techniques used in computer graphics for animation of rigid and deformable solids, virtual characters, fluids and gases, and other systems. Students learn the techniques by implementing a series of interactive computer programs that apply a range of representative simulation methods to simple, primarily 2D systems, and by proposing and implementing a final project.

This course presents an introduction to virtual and augmented reality technologies, with focus on fundamental principles from 3D math, human perception, graphics, and interaction. Concepts from the contributing fields of computer vision, computer graphics and human computer interaction will be introduced in the context of virtual and augmented reality. Students will be tasked with creating their own virtual or augmented reality application as a course project.

An in-depth introduction to computer vision. The goal of computer vision is to compute properties of our world - including the 3D shape of an environment, the motion of objects, and the names of things - through analysis of digital images or videos. The course covers a range of topics, including low-level vision, 3D reconstruction, and object recognition, as well as key algorithmic, optimization, and machine learning techniques, including deep learning. This course emphasizes hands-on experience with computer vision and includes several programming projects.

This course will cover contemporary advances in computer vision. We will cover quick background on classical computer vision before delving into neural networks, modern methods for 3D reconstruction such as neural radiance fields, modern recognition systems based on vision-language models and multimodal language models, and recent trends in synthesis or image generation.

This course focuses on the design and implementation of 3D user interfaces, with emphasis on prototyping interaction techniques for virtual and augmented reality applications. The course will introduce concepts from human-computer interaction and give an overview of interaction techniques for 3D tasks such as selection and manipulation, travel, and system control in virtual environments. Student assignments include implementing classic 3D interaction techniques for head-mounted displays by extending modern interaction toolkits. Students will be introduced to software design patterns and best practices for creating modular, high-fidelity 3D interaction prototypes. All assignments will be implemented using Unity 3D, C#, and the XR Interaction Toolkit.

Human-Computer Interaction (HCI) and design theory and techniques. Methods for designing, prototyping, and evaluating user interfaces. Basics of visual design, graphic design, and interaction design. Understanding human capabilities, interface technology, interface design methods, prototyping tools, and interface evaluation tools and techniques.

Introduction to major topics in artificial intelligence, including heuristic search, game-playing, knowledge representation and reasoning, planning, probabilistic inference, sequential decision-making and reinforcement learning.

Provides an applied treatment of modern causal inference using machine learning to handle high-dimensionality and nonparametric estimation. Formulates key causal questions in the languages of structural equation modeling and potential outcomes. Presents methods for estimating and constructing confidence intervals on causal and structural parameters using machine learning, including de-biased machine learning, and for learning how to predict heterogeneous treatment effects. Introduces tools from machine learning and deep learning developed for prediction purposes and discusses how to adapt them to causal inference. Emphasizes the applied and practical perspectives with real-world-data examples and assignments. Requires basic knowledge of statistics and machine learning and programming experience in R or Python.

This course covers algorithmic and computational tools for solving optimization problems with the goal of providing decision-support for business intelligence. We will cover the fundamentals of linear, integer and nonlinear optimization. We will emphasize optimization as a large-scale computational tool, and show how to link programming languages with optimization software to develop industrial-strength decision-support systems. We will demonstrate how to incorporate uncertainty into optimization problems. Throughout the course, we will cover a variety of modern applications, including pricing and marketing for e-commerce, ad auctions on the web, and on-line ride-sharing.

This course constitutes an introduction to natural language processing (NLP), the goal of which is to enable computers to use human languages as input, output, or both. NLP is at the heart of many of today's most exciting technological achievements, including machine translation, automatic conversational assistants and Internet search. The course will introduce core problems and methodologies in NLP, including machine learning, problem design, and evaluation methods. Ithaca only: Expect each of the roughly four connected programming assignments to take tens of hours, although this time is distributed over multiple weeks; to require writing code to massage raw-ish data into different formats and other accessory functions as well as to implement core algorithms; and to necessitate much independent examination of documentation.

Robotics is interdisciplinary and draws inspiration from many different fields towards solving a variety of tasks in real-world environments using physical systems. This course is a challenging introduction to basic computational concepts used broadly in robotics. By the end of this course, students should have a fundamental understanding of how the different sub-fields of robotics such as kinematics, state-estimation, motion planning, and controls come together to develop intelligent behaviors in physical robotic systems. The mathematical basis of each area will be emphasized, and concepts will be motivated using common robotics applications. Students will be evaluated using a mixture of theoretical and programming exercises throughout the semester.

Robot and automated systems often need to move in and around people. This technical and human-centered course surveys current physical, computation and sensing technologies that make mobile human robot interaction possible, and delves into key research problems in interaction, human modelling, and repair which are needed to tackle the grand challenge of mobile HRI. As part of this course, students will build working prototypes of mobile robots, and deploy them for study in human environments. The 5000-level version of this course focuses on platform development; the 6000-level version of this course focuses on the production of human participant research studies using mobile HRI.

How do we get robots out of the labs and into the real world with all it's complexities? Robots must solve two fundamental problems -- (1) Perception: Sense the world using different modalities and (2) Decision making: Act in the world by reasoning over decisions and their consequences. Machine learning promises to solve both problems in a scalable way using data. However, it has fallen short when it comes to robotics. This course dives deep into robot learning, looks at fundamental algorithms and challenges, and case-studies of real-world applications from self-driving to manipulation.

Creating robots capable of performing complex tasks autonomously requires one to address a variety of different challenges such as sensing, perception, control, planning, mechanical design, and interaction with humans. In recent years many advances have been made toward creating such systems, both in the research community (different robot challenges and competitions) and in industry (industrial, military, and domestic robots). This course gives an overview of the challenges and techniques used for creating autonomous mobile robots. Topics include sensing, localization, mapping, path planning, motion planning, obstacle and collision avoidance, and multi-robot control.

This Master's level course will take a hardware-centric view of machine learning systems. From constrained embedded microcontrollers to large distributed multi-GPU systems, we will investigate how these platforms run machine learning algorithms. We will look at different levels of the hardware/software/algorithm stack to make modern machine learning systems possible. This includes understanding different hardware acceleration paradigms, common hardware optimizations such as low-precision arithmetic and sparsity, compilation methodologies, model compression methods such as pruning and distillation, and multi-device federated and distributed training. Through hands-on assignments and an open-ended project, students will develop a holistic view of what it takes to train and deploy a deep neural network.

An introduction to the mathematical and algorithms design principles and tradeoffs that underlie large-scale machine learning on big training sets. Topics include: stochastic gradient descent and other scalable optimization methods, mini-batch training, accelerated methods, adaptive learning rates, parallel and distributed training, and quantization and model compression.

The course provides an introduction to machine learning, focusing on supervised learning and its theoretical foundations. Topics include: regularized linear models, boosting, kernels, deep networks, generative models, online learning, and ethical questions arising in ML applications.

Machine learning is increasingly driven by advances in the underlying hardware and software systems. This course will focus on the challenges inherent to engineering machine learning systems to be correct, robust, and fast. The course walks through the development of a software library for machine learning from scratch, with each assignment requiring students to build models in their own library. Topics will include: tensor languages and auto-differentiation; model debugging, testing, and visualization; fundamentals of GPUs; compression and low-power inference. Guest lectures will cover current topics from ML engineers.

This class is an introductory course to deep learning. It covers the fundamental principles behind training and inference of deep networks, the specific architecture design choices applicable for different data modalities, discriminative and generative settings, and the ethical and societal implications of such models.

Machine Learning (ML) is a ubiquitous technology. This course, which is a follow up to an introductory course on ML will cover topics that aim to provide a theoretical foundation for designing and analyzing ML algorithms. This course has three basic blocks. First block will provide basic mathematical and statistical toolset required for formalizing ML problems effectively and analyzing them. This block will include topics like generalization, sample complexity of learning algorithm and understanding the inherent challenges in various ML frameworks and models. The second block will provide the foundations in algorithms design and optimization techniques required for building and analyzing various ML algorithms. This block will cover topics like gradient descent, stochastic gradient descent, algorithm design for online learning and computational challenges in ML. ML algorithms are deployed in real world and make decisions that affect real world users. The third block, will cover topics on how to formally reason about and how to design ML methods that address social and user related concerns that ML algorithms need to deal with. This block will cover topics such as fairness, privacy, the right to be forgotten and other such issues and how to build ML algorithms that address or assuage these concerns.

Learn and apply key concepts of modeling, analysis and validation from machine learning, data mining and signal processing to analyze and extract meaning from data. Implement algorithms and perform experiments on images, text, audio and mobile sensor measurements. Gain working knowledge of supervised and unsupervised techniques including classification, regression, clustering, feature selection, and dimensionality reduction.

Students will learn deep neural network fundamentals, including, but not limited to, feed-forward neural networks, convolutional neural networks, network architectures, optimization methods, practical issues, recurrent neural networks, transformers, generative models, foundation models, current limitations of deep learning, and visualization techniques. We still study applications to problems in computer vision and to a lesser extent other domains such as natural language and audio processing.

Reinforcement Learning is one of the most popular paradigms for modelling interactive learning and sequential decision making in dynamical environments. This course introduces the basics of Reinforcement Learning and Markov Decision Process. The course will cover algorithms for planning and learning in Markov Decision Processes. We will discuss potential applications of Reinforcement Learning and their implications. We will study and implement classic Reinforcement Learning algorithms.

Explores the power and limitations of efficient computation. Understanding how the notion of efficient computation changes with respect to resources such as time, space, randomness, advice, and interaction. Concrete computational models that we will study will include Turing machines, Boolean circuits, Decision trees, and Branching Programs. Advanced topics may include error-correcting codes, probabilistic checkable proofs, and circuit lower bounds.

Methodology for developing and analyzing efficient algorithms. Understanding the inherent complexity of natural problems via polynomial-time algorithms, advanced data structures, randomized algorithms, approximation algorithms, and NP-completeness. Additional topics may include algebraic and number theoretic algorithms, circuit lower bounds, online algorithms, or algorithmic game theory.

A rigorous introduction to the theoretical foundations of the cryptography that powers much of the modern world. Topics include one-way functions, secret-key encryption, zero-knowledge proofs, signatures, public-key encryption etc. As this is a theoretical class, the emphasis will be on formal definitions and proofs.

Covers the mathematical foundations for access to information. Topics include high dimensional space, random graphs, singular value decomposition, Markov processes, learning theory, and algorithms for massive data.

The course examines how the computing, economic and sociological worlds are connected and how these connections affects these worlds. Tools from computer science, game theory and mathematics are introduced and then used to analyze network structures present in everyday life. Topics covered include social networks, web search, auctions, markets, voting, and crypto-currencies (e.g. bitcoin).

Off-campus internship with industry in which a student gains knowledge and experience in the field of computer science.

Independent or group project under the direction of a CS field member or researcher. Projects involve the development of a computer science application (software or hardware) useful in exploring and/or solving an engineering problem with a computer science focus.

This is an onboarding course that first-year PhD students in Computer Science are required to attend during their first semester at Cornell. This course seeks to more formally present some of the hidden curriculum that is so often the marker of success in graduate school. Topics will include: your health, wellness, and finances; selecting an advisor and research direction; applying for grants and fellowships; giving a professional talk; setting and managing expectations; choices and consequences in computing.

Study of programming paradigms: functional, imperative, concurrent, and probabilistic programming. Mathematical foundations: inductive definitions, fixed points, and formal semantics. Models of programming languages including the lambda calculus. Type systems, polymorphism, modules, and object-oriented constructs. Program transformations, program logic, and applications to programming methodology.

This course provides an introduction to novel programming languages for controlling computer networks. It will examine recent proposals based on logic, functional, and streaming languages, as well as tools for establishing the correctness of programs written in such languages using SAT/SMT solvers, model checkers, and proof assistants. Evaluation will be based on class participation and projects.

In recent years, it has become practical to build large software systems using formal proof assistants. Examples of such certified systems include the seL4 microkernel, the CompCert C compiler, the Vellvm LLVM compiler, and the Bedrock library. This course provides a hands-on introduction to programming using the Coq proof assistant. Assessment is based on participation and a substantial course project.

An introduction to category theory, with a focus on material with established applications to computer science and programming languages, in particular. The course emphasizes developing comfort with abstraction and instantiation.

This is a hands-on course about implementing programming languages. It covers intermediate representations, classic optimization, runtime systems, and more advanced techniques such as parallelization, just-in-time compilation, and garbage collection. Course work consists of reading and discussing both classic and modern research papers and implementation projects based on the LLVM compiler infrastructure.

This seminar course will explore algorithmic fairness through a non-ideal lens. Rather than imagining what algorithmic fairness should look like in an ideal world, we will explore what the research tells us it looks like today, and what can be done given the incentives and practical constraints of the world we are in. For instance, the “silver bullets” of algorithmic fairness are often abstract calls made to increase participation, transparency, and regulation. However, each approach faces difficulties in practice. In an era where algorithmic fairness faces resistance from many fronts, it is critical that we foreground concrete proposals in addition to abstract ideals. In doing so, we’ll also discuss the consequences of taking on a perspective that might be overly reformist at the expense of radical re-imagining, and think through whether complementarity exists.

Runtime Verification (RV) is a lightweight formal method for checking program executions against specifications. Foundations, algorithms, and tools for major approaches to RV will be covered, including monitor synthesis, specification languages, parametric monitoring, monitorability, instrumentation, and static analysis for reducing RV overhead. Students will become familiar with recent research results and challenges in RV, gain experience with RV tools, and conduct a research project.

Recent advances in Machine Learning have led to remarkable results in natural language processing, video generation, code generation, etc. On one hand, Machine Learning enables solving challenging software engineering problems through data-driven techniques. On the other hand, Machine Learning systems present novel software engineering challenges that traditional methods cannot handle. This course will explore research in this important intersection of software engineering and machine learning. Topics that will be covered include 1) foundational software engineering concepts, such as testing, debugging, and program analysis, 2) software engineering techniques for improving the quality of machine learning systems, and 3) the use of machine-learning techniques (including Large Language Models) to improve software engineering.

An introduction to program synthesis: the problem of automatically generating programs from specifications of their desired behavior. Program synthesis draws on the fields of programming languages and artificial intelligence with the aim of helping to improve software engineering by automatically generating code; help expand the usability of computers by allowing non-coders to harness the power of programming languages; and help us build more interpretable, symbolic AI systems that can write new code. Covers classic areas such as programming-by-example, constraint-based synthesis using SMT solvers, type-directed program synthesis, and inductive logic programming. Covers recent developments in mixed discrete/continuous and neural/symbolic program synthesis. Covers deep learning methods for building efficient, scalable program synthesizers.

Probabilistic programming languages are a powerful tool to express randomized computations and model uncertain behavior. This seminar surveys recent research on such languages, from the perspective of programming languages, logic, and verification. The topic naturally divides into three sections. The first part of the course covers the semantics of probabilistic programming languages: what do such programs mean mathematically, especially when the languages are extended with operators for conditioning and inference? The second part of the course covers verification: what does it mean for probabilistic programs to be correct, and how can we formally verify correctness? Finally, the last part of the course covers applications of probabilistic programs.

Stable and efficient algorithms for linear equations, least squares, and eigenvalue problems. Direct and iterative methods are considered. Numerical programming is used extensively.

Matrices and linear systems can be data-sparse in a wide variety of ways, and we can often leverage such underlying structure to perform matrix computations efficiently. This course will discuss several varieties of structured problems and associated algorithms. Example topics include randomized algorithms for numerical linear algebra, Krylov subspace methods, sparse recovery, and assorted matrix factorizations.

The course will be divided into modules. The course will start with an overview of parallel machines and parallel programming. The course then will cover parallel computing topics in machine learning and deep learning combinatorial scientific computing, heterogeneous parallel programming and architectures, and high-performance domain-specific languages.

A discussion of numerical methods in the context of data analysis, machine learning, and network science problems. The course will focus on matrix and tensor decompositions, numerical algorithms for graph data, least squares, regression, and iterative methods.

Covers a variety of advanced issues ranging from transaction management to query processing to data mining. Involves extensive paper reading and discussion.

This seminar will discuss 1) how to do academic research which accomplishes social change and 2) how to increase the impact of academic research by writing about findings to a mass audience and to policymakers. Each week, we will spend one lecture discussing academic papers which accomplished social change and one lecture discussing writing for a mass audience. The seminar will feature guest lecturers from academia and journalism, and students will work on a final writing project which communicates research findings to a mass audience.

Bayesian modeling and data analysis is a powerful tool for computational research. It consists of writing a probability model and then fitting it with observed data, while handling uncertainty. The model can be flexible, encompassing hierarchy, spatio-temporal dynamics, graphs, and high-dimensionality. This course is a graduate, hands-on introduction to Bayesian analysis in Stan and/or Pyro. The focus will be on writing and fitting models in practice for computational research, including the applied Bayesian statistics workflow: model building, checking, and evaluation. The course will also discuss research papers that use such methods.

Advanced course in systems, emphasizing contemporary research in distributed systems. Topics may include communication protocols, consistency in distributed systems, faulttolerance, knowledge and knowledge-based protocols, performance, scheduling, concurrency control, and authentication and security issues.

The overall objective of the course is to train students to acquire the skills required to become a faculty member in operating systems. This includes understanding the broader landscape of operating systems and its applications, the ability to give clear presentations on the undergraduate and graduate level, write peer review assessments of research papers, conduct independent research, and write a final research report. The course focuses on one or more core focus areas within operating systems research, such as storage systems, memory systems, networking, distributed systems or security. Additionally, students conduct an independent research project including a coding demo and produce a written report on their findings.

A survey of modern security and privacy technologies. Topics include exploitation techniques, web and mobile security, uses and misuses of cryptography in secure systems, attacking and defending secure network protocols, data privacy and anonymity, censorship resistance, electronic payments. This course includes a major project component in the form of major programming assignments and/or other activities.

This course explores state-of-the-art network architectures and protocols through a review of recent research literature, discussions during lectures and class projects. Students will complete a semester-long research project based on one of the topics covered in the class.

This graduate-level course will systematically explore the fundamentals of optical interconnects in computer systems. The course will discuss the design, implementation and value of optical interconnects of a variety of sizes: long-haul optical interconnects, datacenter-scale optical interconnects and server-scale optical interconnects.

Advanced course in realistic image synthesis, focusing on the computation of physically accurate images.

Computational imaging is the holistic design of imaging systems together with algorithms, blending ideas from computer vision, optics, imaging, and machine learning to overcome the limits of traditional cameras and imaging systems (e.g. capturing the first image of the black hole and imaging light-fields). This course will provide an overview of the state of the art in computational imaging. We will learn how to mathematically model different aspects of imaging systems, such as noise, aberrations, and light propagation. In addition, we will learn how to formulate and solve imaging inverse problems using both classical and modern deep-learning-based approaches. Throughout the course, we will discuss exciting active research topics such as lensless imaging, compressive imaging, phase microscopy, time-of-flight imaging, and tomography. The class will culminate in an open-ended final project.

This course will introduce the core problems of computer vision and discuss both classical approaches based on the geometry and physics of image formation as well as current approaches based on recent advances in deep learning. Topics include stereopsis and 3D reconstruction, optical flow, image segmentation, object recognition, feature representations of images and patches, and convolutional networks.

The ability to infer, model, and utilize 3D information from perceptual input is crucial to various intelligent systems (e.g., self-driving vehicles, mobile robots) and AI tasks (e.g., 2D image/3D asset generation, robot manipulation). The course will investigate the fundamentals and the latest advances in 3D vision as well as their applications in different fields. The topics include image formation, multi-view geometry, (neural) 3D representations, learning-based 3D algorithms, neural rendering, generative models, etc. The course will feature a mixture of lectures, paper presentations (both classic and modern), and a group final project. The students will play around various algorithms and models and improve or propose a creative use of them.

This course will focus on computational tools and methods for content creation in different domains. We will cover methods used in interactive artistic applications (visual arts, film, music, video games) as well as domains where complete automation is the goal (e.g., photometric reconstruction and image-based rendering, data augmentation). The course will involve a mix of lectures given by the instructor and presentations/discussions about research papers that will be assigned to students. Topics will include: different representations of images (e.g., vector and raster graphics), 3D models (e.g., surface manifolds vs volumetric representations), and audio (e.g., time signals vs short-time spectra), as well as parametric control of these representations and their relation to other types of data. A theory or program heavy project will be completed.

Covers a variety of areas in AI, including knowledge representation, automated reasoning, learning, game-playing, and planning, with an emphasis on computational issues.

This is a studio style course that emphasizes collaborative learning. It leverages the AI for Science Schmidt postdoc fellowship program and other programs. Schmidt postdoc fellows will present their research and progress. We will discuss background literature, related research, and possible research directions in class. Throughout the semester, students will learn various AI research methodologies, including AI/ML code, packages, and software, and engage in discussions on current advancements and applications of AI in science and engineering.

Graduate-level introduction to technologies for the computational treatment of information in human-language form, covering modern natural-language processing (NLP) and/or information retrieval (IR). Possible topics include language modeling, word embeddings, text categorization and clustering, information extraction, computational syntactic and semantic formalisms, grammar induction, machine translation, latent semantic analysis (LSI), and clickthrough data for web search.

Robust language understanding has the potential to transform how we interact with computers, extract information from text and study language on large scale. This research-oriented course examines machine learning and inference methods for recovering language structure and meaning. Possible topics include structured prediction and deep learning, methods for situated language understanding, language grounding, and learning to generate text.

More and more of life is now manifested online, and many of the digital traces that are left by human activity are increasingly recorded in natural-language format. This research-oriented course examines the opportunities for natural language processing to contribute to the analysis and facilitation of socially embedded processes. Possible topics include sentiment analysis, learning social-network structure, analysis of text in political or legal domains, review aggregation systems, analysis of online conversations, and text categorization with respect to psychological categories.

Robot manipulation is the ability for a robot to interact physically with objects in the world and manipulate them towards completing a task. It is one of the greatest technical challenges in robotics, due primarily to the interplay of uncertainty about the world and clutter within it. As robots become integrated into complex human environments, robot manipulation is increasingly necessary to assist humans in these unstructured environments. Robotic manipulation will enable applications like personal assistant robots in the home and factory worker robots in advanced manufacturing. This course is a mixture of lectures and paper presentations and covers the fundamental theory, concepts, and systems of robot manipulation, including both software and hardware. Topics we will cover this semester include perception, state estimation, robot arm kinematics and dynamics, task and motion planning, machine learning, controls, human-robot interaction towards various robot manipulation tasks. The course features a semester-long group project in which students propose, formalize, and execute a working robotic manipulation system towards a real-world task. The scope of possible components is quite broad and extends beyond traditional robotics issues into other aspects of CS. This course is offered to prepare a student for Ph.D. research in robot manipulation.

How can we guarantee robots will never cause harm? How can we prove that complicated mechanical systems, controlled by computers and programmed by people, will always behave as expected under changing conditions and in a variety of uncertain environments? How do we formalize what such behaviors are? Guaranteeing safety, predictability and reliability of robots is crucial for the assimilation of such systems into society, be it at home or in the workplace. While every robotics researcher working with or on a robot is aware of safety issues, only recently the robotics community has begun looking at ways to either formally prove or grarantee by design different behavioral properties such as safety and correctness. This class will present recent results on the topic of formal methods for robotics and automation that combine and extend ideas from control theory, dynamical systems, automata theory, logic, model checking, synthesis, and hybrid systems.

As robots move from factory floors and battlefields into homes, offices, schools, and hospitals, how can we build robotic systems made for human interaction? Course will cover core engineering, computational, and experimental techniques in human-robot interaction (HRI). Lectures will cover key algorithms in Probabilistic Robotics, including Bayesian Networks, Markov Models, HMMs, Kalman and Particle Filters, MDP and POMPD, Supervised Learning, and Reinforcement Learning. Seminal and recent papers in HRI will be discussed, including topics such as: generating intentional action, reasoning about humans, social navigation, teamwork and collaboration, machine learning with humans in the loop, and human-robot dialog. Students will learn methods for designing and analyzing HRI experiments. Presentation of papers in class, and an HRI-related research project in teams will be required. Intended for M.Eng to PhD students from multiple disciplines including MAE, CS, ECE and IS.

Robot and automated systems often need to move in and around people. This technical and human-centered course surveys current physical, computation and sensing technologies that make mobile human robot interaction possible, and delves into key research problems in interaction, human modelling, and repair which are needed to tackle the grand challenge of mobile HRI. As part of this course, students will build working prototypes of mobile robots, and deploy them for study in human environments. The 5000-level version of this course focuses on platform development; the 6000-level version of this course focuses on the production of human participant research studies using mobile HRI.

Advances in machine learning have fueled progress towards deploying real-world robots from assembly lines to self-driving. Learning to make better decisions for robots presents a unique set of challenges. Robots must be safe, learn online from interactions with the environment, and predict the intent of their human partners. This graduate-level course dives into the various paradigms for robot learning and decision making and heavily focuses on algorithms, practical considerations, and features a strong programming component.

Deep learning has become a pivotal force in recent robotics research advancements, from estimating the state of the world to solving complex long-horizon tasks. The new paradigm shifts from traditional feature and model engineering to learning task-relevant representations from raw data. This is fueled by increasingly more affordable hardware and diverse data sources from which algorithms may learn from. This graduate-level course examines how deep learning approaches have been applied to robotics problems, including various topics of robot perception and control. We will also discuss the recent trend of large-scale representation learning and foundation models for robotics.

Gives a graduate-level introduction to machine learning and in-depth coverage of new and advanced methods in machine learning, as well as their underlying theory. Emphasizes approaches with practical relevance and discusses a number of recent applications of machine learning in areas like information retrieval, recommender systems, data mining, computer vision, natural language processing and robotics. An open research project is a major part of the course.

This course on machine learning theory introduces basic results, tools, and techniques used in analysis of statistical and online learning algorithms. The course also introduces the basics of computational learning theory.

Extends and complements CS 3780 (formally CS 4780) and CS 5780, giving in-depth coverage of new and advanced methods in machine learning.

Generative models are a class of machine learning algorithms that define probability distributions over complex, high-dimensional objects such as images, sequences, and graphs. Recent advances in deep neural networks and optimization algorithms have significantly enhanced the capabilities of these models and renewed research interest in them. This course explores the foundational probabilistic principles of deep generative models, their learning algorithms, and popular model families, which include variational autoencoders, generative adversarial networks, autoregressive models, and normalizing flows. The course also covers applications in domains such as computer vision, natural language processing, and biomedicine, and draws connections to the field of reinforcement learning.

Graduate-level introduction to system-focused aspects of machine learning, covering guiding principles and commonly used techniques for scaling up to large data sets. Topics will include stochastic gradient descent, acceleration, variance reduction, methods for choosing metaparameters, parallelization within a chip and across a cluster, and innovations in hardware architectures. An open-ended project in which students apply these techniques is a major part of the course.

Statistical topic models such as LDA provide a powerful tool for discovering themes in large unlabeled text corpora. They are increasingly popular in a wide range of fields, both as a data-driven alternative to manual document coding methods, and also as an example of a difficult but tractable problem in statistical inference. This course will cover Bayesian model construction, inference techniques, evaluation, and applications beyond text such as community detection in networks and population admixture in genetics.

State-of-art intelligent systems often need the ability to make sequential decisions in an unknown, uncertain, possibly hostile environment, by actively interacting with the environment to collect relevant data. Reinforcement Learning is a general framework that can capture the interactive learning setting. This graduate level course focuses on theoretical and algorithmic foundations of Reinforcement Learning. The topics of the course will include: basics of Markov Decision Process (MDP); Sample efficient learning in discrete MDPs; Sample efficient learning in large-scale MDPs; Off-policy policy optimization; Policy gradient methods; Imitation learning & Learning from demonstrations; Contextual Bandits. Throughout the course, we will go over algorithms, prove performance guarantees, and also discuss relevant applications. This is an advanced and theory-heavy course: there is no programming assignment and students are required to work on a theory-focused course project.

A mathematically rigorous course on lattices. Lattices are periodic sets of vectors in high-dimensional space. They play a central role in modern cryptography, and they arise naturally in the study of high-dimensional geometry (e.g., sphere packings). We will study lattices as both geometric and computational objects. Topics include Minkowski's celebrated theorem, the famous LLL algorithm for finding relatively short lattice vectors, Fourier-analytic methods, basic cryptographic constructions, and modern algorithms for finding shortest lattice vectors. We may also see connections to algebraic number theory.

Computational complexity theory is devoted to understanding the limitations of efficient computation (with respect to computational resources such as time, space and randomness). This course will be a graduate level introduction to various aspects of complexity theory, with basics topics including time/space complexity, NP completeness, and the polynomial hierarchy, and advanced topics such as the PCP theorem, randomness and derandomization, circuit lower bounds, etc.

This course offers a graduate-level introduction to the theory of probabilistic proof systems. The area has deep connections to many aspects of theoretical computer science, including complexity theory and hardness of approximation, coding theory, cryptography and quantum computing. Topics covered will include the PCP theorem, interactive proofs, zero knowledge, succinct arguments, and quantum proof systems.

Topics in computational complexity theory focusing on the use of randomness. Topics include pseudorandom generators, randomness extractors, and applications to explicit constructions of combinatorial objects. The course project will involve an in-depth study on a topic introduced in class (based on relevant research papers), with the expectation to produce a high quality survey article and a final presentation.

Meta-complexity refers to the computational complexity of problems that are themselves about computations and their complexity. Such problems include the Minimum Circuit Size Problem and the Time-bounded Kolmogorov Complexity Problem, the study of which originated in the 1950s/60s and predate the modern study of Complexity theory. Meta-complexity provides a unifying framework for a variety of central tasks in several areas of computer science, including computational complexity, cryptography, and learning theory, and there has been a recent explosion of works connecting these areas through the lens of Meta-complexity. In this course, we will focus on these recent development, with a particular focus on connections with Cryptography.

This course will focus on the 'Analysis of Boolean Functions' with the objective to unravel properties of Boolean functions by studying their Fourier spectra. The harmonic analysis of Boolean functions has become a powerful tool in theoretical computer science, leading to groundbreaking results in various areas such as social choice theory, hardness of approximation, learning theory, pseudorandomness, property testing and circuit complexity. In fact, the tools developed in this area have found key applications beyond computer science, in particular leading to key developments in areas of random graphs, statistical physics, combinatorics and metric spaces. The course will aim to provide an in-depth introduction to this field of study.

Methodology for developing and analyzing efficient algorithms. Understanding the inherent complexity of natural problems via polynomial-time algorithms, advanced data structures, randomized algorithms, approximation algorithms, and NP-completeness. Additional topics may include algebraic and number theoretic algorithms, circuit lower bounds, online algorithms, or algorithmic game theory.

Predictive algorithms influence and shape society. The use of machine learning to make predictions about people raises a host of basic questions: What does it mean for a predictive algorithm to be fair to individuals from marginalized groups? On what basis should we deem a predictive algorithm to be valid? And when should we trust (or distrust) a predictor's output? This course surveys recent developments in the theory of responsible machine learning. We overview new paradigms for formulating learning problems and highlight key algorithmic tools in the study of fairness, validity, and robustness. Topics covered include: Multicalibration and Outcome Indistinguishability, Omniprediction, Performative Prediction, Distributional Robustness, and Verification of Learning.

A rigorous introduction to the theoretical foundations of the cryptography that powers much of the modern world. As this is a theoretical class, the emphasis will be on formal definitions and proofs. E.g., what does it mean to communicate securely? Can I prove that I am who I claim to be without revealing additional information (such as information that allows others to impersonate me)?Topics include one-way functions, pseudorandom number generators, public-key encryption, zero-knowledge proofs, digital signatures, etc. We will also see some more exotic primitives, such as fully homomorphic encryption, and we might briefly discuss program obfuscation and/or cryptocurrency.

Quantum information and computation have had a profound impact on cryptography, and understanding the connections between them is an active area of research. This course will cover a selection of cutting-edge topics in quantum cryptography, including quantum attacks on classical protocols, provable security against quantum attacks, cryptographic protocols that leverage quantum resources (e.g. quantum key distribution, quantum money, etc.), and connections to quantum complexity theory.

Algorithmic Game Theory combines algorithmic thinking with game-theoretic, or more generally, economic concepts. Designing and analyzing large-scale multi-user systems and as well as such markets, requires good understanding of tools from algorithms, game theory, and graph theory. The course will develop mathematically sophisticated techniques at the interface between algorithms and game theory, and will consider their applications to markets, auctions, networks, as well as the Internet.

Information networks such as the World Wide Web are characterized by the interplay between heterogeneous content and a complex underlying link structure. This course covers recent research on algorithms for analyzing such networks and models that abstract their basic properties. Topics include combinatorial and probabilistic techniques for link analysis, centralized and decentralized search algorithms, generative models for networks, and connections with work in the areas of social networks and citation analysis.

Kleene algebra is the algebra of regular expressions and finite automata, structures of fundamental importance in computer science. Kleene algebra is the algebraic theory of these objects, although it has many other natural and useful interpretations: relational algebra, programming language semantics, program logics, automata and formal languages, network programming, computational geometry, and the design and analysis of algorithms. In this course we will explore the theory and applications of this system, including models, deductive systems, completeness and complexity results, and applications in the areas mentioned above. A final paper or project will be due on which the course grade will be based. It will be either an independent study with a final paper and presentation, or a software project and presentation, at the students' choice.

Weekly meeting for the discussion and study of important topics in the field.

The Programming Languages Discussion Group meets weekly to discuss papers in the area of programming languages, program analysis, and compilers. The goal is to encourage interactions and discussions between students, researchers, and faculty with interests in this area. The seminar is open to everybody interested in languages and compilers. First-year and second-year students are especially encouraged to participate.

This seminar covers classic papers in the area of programming languages. The goal is to give students an in-depth introduction to some of the most important ideas in the field, to provide a foundation for advanced research. Participants will be expected to read one or two papers each week, and to lead one presentation each semester. The seminar is open to everybody interested in programming languages. First and second-year graduate students are especially encouraged to participate.

Talks on various methods in scientific computing, the analysis of their convergence properties and computational efficiency, and their adaptation to specific applications.

The database seminar is the weekly meeting of students and faculty interested in data management and data mining at Cornell. We typically discuss one or two papers on related topics per session. We focus on recent and seminal papers of general interest.

This course will allow students to gain an in-depth learning about a subarea in computer systems, either a classical one or an emerging one. Students will read and discuss recent papers in the selected subarea, and work on a collaborative project. Each offering of the course may choose a different subarea.

The Systems Research Seminar discusses recent, interesting papers in the systems area, broadly defined to span operating systems, distributed systems, networking, architecture, databases, security, and programming languages. The goal is to foster technical discussions among the Cornell systems research community.

PhD-level seminar on special topics in computer systems.

An advanced PhD-level seminar on special topics in Computer Graphics. Recent papers in a selected research area will be read and presented. Topics include rendering, inverse rendering, surface reflection, material capture, and computational fabrication.

Informal weekly seminar in which current topics in computer vision are discussed.

The Graphics/Vision Research Seminar discusses recent research in the areas of computer graphics and computer vision. The goal is to foster technical discussions and collaboration among the Cornell graphics and vision research community.

The AI seminar will meet weekly for lectures by graduate students, faculty, and researchers emphasizing work-in-progress and recent results in AI research.

Reading group on advanced topics in machine learning.

This course, the NLP seminar, is a weekly meeting for people currently or soon to be actively doing research in NLP. (Students simply looking to learn more about NLP should not enroll, but should take one of our lecture courses instead.) One participant leads discussion each week, either of a recently published paper or of their own work in progress.

Informal seminar in which current topics in robotics are discussed.

The theory seminar will meet weekly for lectures by graduate students, faculty, and researchers emphasizing work-in-progress and recent results related to theory of computing.

Seminar for discussing recent or classical papers in cryptography.

Independent research for CS PhD students who have not yet passed their A-exam.