Course Listing

This is CS50, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming, for concentrators and non-concentrators alike, with or without prior programming experience. (More than half of CS50 students have never taken CS before!) This course teaches you how to solve problems, both with and without code, with an emphasis on correctness, design, and style. Topics include computational thinking, abstraction, algorithms, data structures, and computer science more generally. Problem sets inspired by the arts, humanities, social sciences, and sciences. More than teach you how to program in one language, this course teaches you how to program fundamentally and how to teach yourself new languages ultimately. The course starts with a traditional but omnipresent language called C that underlies today's newer languages, via which you'll learn not only about functions, variables, conditionals, loops, and more, but also about how computers themselves work underneath the hood, memory and all. The course then transitions to Python, a higher-level language that you'll understand all the more because of C. Toward term's end, the course introduces SQL, via which you can store data in databases, along with HTML, CSS, and JavaScript, via which you can create web and mobile apps alike. Course culminates in a final project. See https://cs50.harvard.edu/college for advice, FAQs, syllabus, and what's new. Email the course's heads at heads@cs50.harvard.edu with questions.

Fundamentals of computer systems programming, including data representation, storage, process management, and synchronization. This course provides a solid background in systems programming and an understanding of the interactions between computer software and hardware. Topics include C++ and assembly language programming, operating systems kernels and system calls, memory hierarchy and caching, processes, threads, and synchronization.

People use generative AI to build all kinds of artifacts, from travel itineraries to software systems, and these sorts of things require structure, a clarity of purpose, and an understanding of today's AI systems. Students will learn how to partner with AI to build complex tools from software components, for both personal use and sharing with others. This involves learning how to effectively direct an AI, understand what it did (and didn't do), employ software engineering techniques like testing and version control, and evaluate different AI models. 

Imagine you are a developer at -- or regulating, from your position as CTO of the Federal Trade Commission -- a company that capitalizes on large amounts of personal data for use in a wide range of settings, from analyzing markets to advising judges on parole decisions, to selecting candidates to interview, to testing drugs. How might you think about incorporating societal values, such as privacy, fairness, and statistical validity? What mathematical guarantees are achievable, and what is impossible? How does privacy differ from cryptographic secrecy, and which concept is appropriate for which setting?

This class will provide an introduction to the theoretical underpinnings of algorithmic fairness, differentially private data analysis, cryptography, and ensuring statistical validity, emphasizing common underlying themes and conceptual breakthroughs.   

Artificial Intelligence (AI) is already making a powerful impact on modern technology, and is expected to be even more transformative in the near future. The course introduces the ideas and techniques underlying this exciting field, with the goal of teaching students to identify effective representations and approaches for a wide variety of computational tasks. Topics covered in this course are broadly divided into search and planning, optimization and games, and uncertainty and learning. Special attention is given to ethical considerations in AI and to applications that benefit society. For more information please see the course website.

Modern AI 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 (RL) is a general framework that can capture the interactive learning setting and has been used to design intelligent agents that achieve high-level performance in challenging applications such as Go, computer games, robotic manipulation, health care, and education.

This course provides an introduction to reinforcement learning covering a range of problem formulations, algorithms, and theory. The four main themes of the course are (1) Markov decision processes (Bellman equations/optimality, planning, UCB, unknown environments, linear quadratic control, exploration, imitation learning), (2) bandits (epsilon-greedy, UCB, Thompson sampling, contextual bandits, linear bandits, exploration in MDPs), and (3) methods for large-scale systems (policy gradient methods, deep RL, Monte Carlo tree search, Q-learning). There will also be an Embedded Ethics lecture on ethical issues arising in reinforcement learning. The assignments will focus on a mix of algorithmic and statistical principles, along with their programming implementations.

From computational thinking to workforce arguments, there is considerable interest in and excitement about including computer science education for all K–12 students. Yet, unlike other disciplines with a much longer history in formal schooling, the interest in computer science education is not yet supported by commensurate attention to research and teacher practice. In this course, we will examine the state of K–12 computing education: questioning its value, examining its history, and imagining and contributing to its potential. The course will be organized as both a reading group and a lab, building a community of people who are committed to K–12 CS education. Each week you will read classic and current research, and write accompanying memos to document your evolving understandings of the field. Throughout the course, either individually or with partners, you will develop an independent project that explores the design of K–12 computer science learning experiences. Some examples of possible projects include: designing CS-standalone or cross-curricular learning activities and curriculum, building a programming language for novices, developing an annotated bibliography, critically analyzing policy documents such as curriculum frameworks and standards from around the world, or contributing to current K–12 CS education research initiatives.

As manufacturing processes approach the physical limits of transistor density, efficient code must exploit parallelism to scale with available computing resources. Scientific software developers must therefore adopt a “think parallel” mindset to solve complex problems across academia, industry, and society. This course introduces parallel programming and its relationship to computer architectures, with an emphasis on high performance computing. Students will develop experience with programming models such as OpenMP, MPI, and CUDA, applying these techniques in homework and a term project.

Information Theory originated in the seminal work of Shannon (1948) that aimed to formalize and quantify information. Since the 1990s tools and concepts from Information Theory have played a central role in theoretical computer science. Notable examples include the Parallel Repetition Theorem of Raz (1994), the development of the Information Complexity to understand Communication Complexity (2001). Today Information Theoretic measures and tools influence many aspects of CS theory including analysis of streaming algorithms, differential privacy and game theory. This course will introduce the basic concepts in information theory and then sample topics of interest to CS theory.

Probabilistic techniques and tools for the design and analysis of algorithms. Designed for all first-year graduate students in all areas.

Interplay between computation and economics. Rotating topics in mechanism and market design, strategic aspects of machine learning and AI, information elicitation and forecasting, and other emerging areas. Readings in AI, theoretical CS, multi-agent systems, economic theory, and operations research.

How can we use tools from statistical learning theory to design better auctions? Can we use cryptography to better implement matching mechanisms? And how should we approach formally proving that welfare in Nash equilibria for many games is not "much worse" than in the social optimum? This course explores the application of diverse ideas, techniques, and solution aesthetics from theoretical computer science to derive meaningful new insights into classic economic problems. The three main themes are approximation theorems (including bounding the loss in revenue or welfare due to lack of information, to strategic behavior, or to impracticality of the optimal mechanism); various notions of complexity (including computational complexity, communication complexity, and sample complexity); and cryptographic tools (including cryptographic commitments, multiparty computation, and zero-knowledge proofs). Economic applications mostly include analysis of equilibria, pricing, and mechanism design.

Review of the fundamental structures in modern processor design. Topics include computer organization, memory system design, pipelining, and other techniques to exploit parallelism. Discussion of modern topics including GPU architectures, datacenter architecture, mobile/embedded SoC architectures, and machine learning acceleration as time permits. Emphasis on a quantitative evaluation of design alternatives and an understanding of performance and energy consumption issues.

This course focuses on efficient AI computations aimed at reducing the cost of AI model training and inference. Students will learn systematic methods for implementing parallel and distributed computations for computer vision and language models such as CNNs and Transformers across multiple computing cores or nodes. They will also learn techniques for co-designing machine learning models, data curation methods, computing algorithms, and system architectures.

Techniques to be studied include systolic arrays, low-bitwidth arithmetic, model pruning, quantization, distillation, low-rank fine-tuning, dynamic selection of submodels (e.g., experts) based on input, speculative decoding, synthetic data generation with stable diffusion, data and model security, scheduling for efficient memory access, reasoning with reinforcement learning, and test-time computing for reasoning.

As a part of programming assignments, students will utilize large language models to generate code that can leverage AI accelerating techniques learned in this course.

Upon successful completion of this course, students will be equipped to address the challenging tasks of designing and utilizing energy-efficient, high-performance AI accelerators.

This is a graduate-level course on computer networks. This course offers an in-depth exploration of a subset of advanced topics in networked systems. We will discuss the latest developments in the entire networking stack, the interactions between networks and high-level applications, and their connections with other system components such as compute and storage.

In this year's edition, we will use machine learning as a prime example to understand its unique requirements and challenges in the context of networking. As machine learning applications increasingly rely on larger models and faster accelerators, the demand for enhanced networking capabilities becomes imperative. Throughout this course, we will study cutting edge networking solutions and principles for  co-designing networks with compute and storage, to meet the evolving needs of machine learning applications. The course will include lectures, in-class presentations, paper discussions, and a research project.

More information of this course is at https://github.com/minlanyu/cs243-site.

The contents and course requirements of CS 244R are similar to those of CS 1440R, with the exception that students enrolled in CS 2440R are expected to do substantial system implementation and conduct rigorous performance analysis for their design projects, and perform graduate-level work to earn the same letter grade.

In this course, students will learn how to utilize tools powered by large language models (LLMs) to address real-world design challenges, including the generation of code and circuits. This field represents the forefront of technology and is rapidly evolving, with new insights and lessons emerging frequently. For example, recent advancements in reasoning models have significantly improved the success rate of producing designs that are not only correct but also efficient. As a result, there is a growing emphasis on the computational efficiency of inference, which enhances deeper reasoning capabilities. Related research includes developing methods for automatically generating testbenches to evaluate and validate designs created by LLMs.

A student's work includes the following activities: (1) reviewing articles about various experiments that explore the use of LLM tools in design, (2) creating presentations based on these articles and delivering them in class, and (3) collaborating in teams of 2 or 3 on a design project that utilizes LLM-assisted tools, applying the knowledge gained from the literature.

Seminar course exploring recent research in computer architecture. Topics vary from year to year and will include subjects such as multi-core architectures, energy-efficient computing, reliable computing, and the interactions of these issues with system software. Students read and present research papers, undertake a research project.

Seminar course exploring recent research in programming languages. Topics vary from year to year. Students typically read and present research papers, undertake a research project.

This course explores practical attacks on modern computer systems, explaining how those attacks can be mitigated using careful system design and the judicious application of cryptography. The course discusses topics like buffer overflows, web security, information flow control, and anonymous communication mechanisms such as Tor. The course includes several small projects which give students hands-on experience with various offensive and defensive techniques; the final, larger project is open-ended and driven by student interests.

Students will read, analyze, present, and discuss research papers in data visualization. Throughout the semester, we will examine seminal works and recent state-of-the-art research in information visualization, scientific visualization, and visual analytics. Students will collaborate in small groups on a semester-long visualization project and present their findings through written reports and oral presentations in a conference-style format. Through interactive discussions, peer feedback, and formal design critiques, we will analyze both published visualization research and each other's work.

The course explores major areas of research and practice at the intersection of design, technology, and social impact. Specifically, we will explore the current state of research and interesting real-world examples related to the design, evaluation, and implementation of interventions comprising of technical, social, and organizational elements. We will also explore leading theories and methods for anticipating broader, indirect societal impacts of such intervention. Course activities will involve discussion of primary literature, some guided instruction, assignments, and a major research project.

Students will read, write about, prepare presentations about, and discuss human-computer interaction (HCI) and HCI-relevant work with a focus on papers about interfaces and automation that work especially well with (or clash against) human cognitive capabilities. Papers will primarily be on the building and evaluation of novel systems, as well as theories of and studies characterizing human cognition relevant to human-AI interaction scenarios. As a semester-long final project, students will pursue a research project of their own design in self-organized groups and present their findings in writing and orally in a conference-style format, as means to understand more deeply the processes behind HCI research.

Agents are developing capabilities at increasing rates, but do those agent capabilities translate into human capabilities, with respect to creating agents that help us do the things we need to do and be who we want to be? In particular, by doing some short-term task e.g. writing a piece of code, current agents often fail us in longer term tasks e.g. making sure that we can also maintain that code and take responsibility for that code to our teams. As another example, a health assistance agent that is optimized to create engaging visualizations of your health data might be satisfying to look at, but what we really want to evaluate is whether it actually helps you improve your health. In this course, we'll survey the literature and create AI systems that are designed to effectively support human tasks via a combination of reinforcement learning, probabilistic methods, and human factors/design elements. After a few initial assignments, the course will be focused on reading papers, discussion, and a semester-long project.

Modern Artificial Intelligence (AI) 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. This graduate-level course focuses on the theoretical and algorithmic foundations of Reinforcement Learning (RL). The four main themes of the course are: (1) fundamentals (MDPs, computation, statistics, generalization); (2) provably efficient exploration (and high-dimensional RL); (3) policy optimization (e.g., policy gradient methods); and (4) trending topics (control, offline RL, partial observability, RL for foundation models).

Vision as an ill-posed inverse problem: image formation, two-dimensional signal processing; feature analysis; image segmentation; color, texture, and shading; multiple-view geometry; object and scene recognition; and applications.

The ability to connect devices over long distances, via the internet, changed our world.  The second phase of this revolution, that we are still living in today, came about when these devices became wireless.  Now we are at the cusp of a new phase of this evolution where devices are connected, wireless, and controlled – i.e. the robot revolution.

Multi-robot systems are becoming more pervasive; from future autonomous vehicle fleets, to drones, to manufacturing robots.  As a result, the question of how to control, coordinate, and secure these systems has been a growing topic in the robotics literature in recent years.  In this seminar-style course we will do a deep dive into this topic by reviewing classic and recent results in multi-agent planning and control literature.  We will cover a wide gamut of applications from control of groups of flying drones, to decision making in autonomous car networks, to space exploring CubeSats.

This class will treat both the theory and the practical applications behind multi-robot systems. Students with mathematical inclinations and exposure to graph theory, probability theory, linear algebra, and algorithms will derive the most benefit from this course.

Recent years have seen AI successfully applied to societal challenge problems. Indeed, recognizing the potential of AI for tremendous social impact in the future, "AI for social impact" is growing as a subdiscipline within AI. In this course, we will discuss successful case studies of  use of AI for public health, environmental sustainability, public safety and public welfare. Simultaneously, we will discuss key foundations of the area of AI for social impact. To that end, among other topics, we will focus on challenges in AI for Social Impact, what makes projects successful, how to investigate project impact in the field and ethical considerations for such projects. A key part of this course will be AI4SI projects with non-profits.

This will be a graduate level course on challenges in alignment and safety of artificial intelligence. We will consider both technical aspects as well as questions on societal and other impact on the field. This is a fast-moving area and it will be a fast-moving course. I will expect students to be able to pick up technical knowledge on their own. In a sense, the programming language for this course will be English: students will be allowed and encouraged to use AI tools for all homework and assignments. On the other hand, this means that expectations will be raised: it may well be the case that I would expect you to do assignments in a week that in previous years would have taken a month.

This is a reading and discussion-based seminar designed for entering Computer Science Ph.D. students. This course prepares students to manage the difficult and often undiscussed challenges of Ph.D. programs through sessions on research skill building (e.g. paper reading, communication), soft skill building (e.g. managing advising relationships, supporting your peers), and academic culture (e.g. mental health in academia, power dynamics in scientific communities), as well as research and professional-oriented discussions. This is a full-year, 4-unit course, meeting once a week in each of the fall and the spring. Students must complete both terms of this course (CS 2901 and CS 2902) within the same academic year to receive credit.

This is a reading and discussion-based seminar designed for entering Computer Science Ph.D. students. This course prepares students to manage the difficult and often undiscussed challenges of Ph.D. programs through sessions on research skill building (e.g. paper reading, communication), soft skill building (e.g. managing advising relationships, supporting your peers), and academic culture (e.g. mental health in academia, power dynamics in scientific communities), as well as research and professional-oriented discussions. This is a full-year, 4-unit course, meeting once a week in each of the fall and the spring. Students must complete both terms of this course (CS 2901 and CS 2902) within the same academic year to receive credit.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.

Experimental or theoretical research project on acceptable problems in computer science supervised by a SEAS faculty member, and/or supervised reading on topics not covered by regular courses of instruction. The project or reading must be arranged between the student and individual SEAS faculty supervisor prior to enrolling in the course.