Topics Courses Archive

Spring 2025 Course Descriptions

CS391 A1: Web Application Development (must also enroll in A2, A3, A4, or A5 discussion section)

Prerequisites: CS111, CS112, CS210

Instructor: Prof. Taymaz Davoodi

CS391 R1: Computer & Memory Architectures (must also enroll in R2 or R3 discussion section)

Prerequisites: CS350

This course covers key concepts concerning the design and operation of modern computing and memory architectures, with an emphasis on hands-on experimentation and evaluation. The course first covers the design of central processing units (CPU), going from single-cycle designs to pipelined architectures. Next, multi-core CPUs are reviewed, along with private caches and cache coherence protocols. The course then dives into the design of high-performance memory subsystems. We, therefore, review modern multi-level cache hierarchies, bus architectures, and protocols. Finally, we study the design of main memory technologies such as DRAM. The course also touches on the interaction between processing units and Input/Output (I/O) devices through modern high-throughput interfaces such as USB and PCI-Express. The practical component of the course will cover the development of hardware modules using System Verilog.

Instructor: Prof. Renato Mancuso

CS391 S1 Spark! Software Engineering Immersion (must also enroll in S2 or S3 discussion section)

Prerequisites: CS111, prior knowledge of Python & Javascript helpful

Students will be introduced to all concepts required to work on a modern web development project. This course is intentionally taught with very little prerequisite knowledge to enable students to begin learning these skills earlier in their college path. Students begin by learning basic skills required to build a functioning web application. During the second half of the course, students will be allocated to teams and assigned a project to work on over the course of the semester. Students will submit their final application as their final project on the last day of classes.

Instructors: Michael Levinger, Uwe Meding, Langdon White

CS391 T1: Algorithms to live by (must also enroll in T2 or T3 discussion section)

Prerequisites: CS112, CS131, CS237

The course will be based on the algorithmic principles described in the popular science book “Algorithms to Live By: The Computer Science of Human Decisions” by Brian Christian and Tom Griffiths. We will cover concepts such as (1) optimal stopping (2) Explore vs. Exploit (3) Sorting and Searching (4) Caching and Memory (5) Game theory and Decision making (6) Handling overwhelm and staying sane. We will discuss the main algorithmic techniques and results related to these concepts and how we can apply them to everyday life.

Instructor: Evimaria Terzi

CS392 C1: Competitive Programming I

**Previously posted as CS392 B1**

Prerequisites: CS111, prior knowledge in C/C++ or Java

This course covers advanced algorithms necessary to compete in the ACM International Collegiate Programming Contest (ICPC) and similar contests. Active involvement in weekly contests is a mandatory component of the course. Topics covered include standard library classes and data structures, competitive programming contest strategies, string manipulation, divide and conquer, dynamic programming, graph algorithms, number theory, computational geometry, and combinatorics.

Professor: Tiago Januario

Course Website: https://cs-people.bu.edu/januario/teaching/CS392/sp25/index.html

CS392 E1: Windows .NET Application Programming with C#

Prerequisites: CAS CS112

Students will utilize agile software engineering practices in this hands-on course to design and implement data driven applications using C# and the .NET Framework. We will start by comparing and contrasting the .NET framework with other frameworks as well as native code, exploring the advantages and drawbacks of running code in managed vs. unmanaged environments. Students will subsequently design and implement several simple C# programs, focusing on data driven UI components and event based programming, after which students will be grouped into small teams, collaborating on a larger final project. As the course proceeds students will begin looking at more complex topics including fundamental design patterns, while implementing more complex applications. Topics will include: reading and writing from files/streams/databases, exception handling, multithreading, memory management, networking, delegates, generics and LINQ. Students will also perform their own research and development as they choose which 3rd party APIs to incorporate into their final project. No previous application development experience is required, but a very strong understanding of object-oriented programming is required. The syntax for C#, Java, and C++ are nearly identical. Students will be expected to leverage their understanding of Java and/or C++ syntax to help pivot to C#. This will be a highly interactive, project oriented and team based course. Attendance, participation and collaboration are mandatory and part of your grade.

Instructor: Shereif El-Sheikh

CS392 M1 Rust, in Practice and in Theory

Prerequisites: CS210, CS320

Rust is a type-safe, memory-safe programming language that is becoming a popular alternative to C and C++ in settings where performance and memory usage are major concerns. It’s self-described as having “high-level ergonomics” and “low-level control.” Practically speaking, this means clear, concise code with fewer memory bugs. Theoretically speaking, this means the use of a rich type system based on the notion of linearity to enforce memory-safety before any code has actually been run. Despite its popularity, Rust is still daunting to learn, even for experienced programmers. There are several concepts in Rust that don’t appear in any other popular languages. And even if you become a proficient Rust programmer, it doesn’t mean you have a deep understanding of how Rust works, or why it is a better alternative to other low-level languages. In this course, we’ll spend the first half of the semester learning Rust. This can include topics like borrowing, lifetimes, traits, smart pointers, and concurrency. We’ll spend the second half implementing a subset of Rust. This will help us better understand the details of Rust’s type system and borrow checker.

Instructor: Nathan Mull

CS392 X1: Modern Compiler Construction in Python

Prerequisites: CS111 and CS320 (or something equivalent to CS320)

Modern Compiler Construction in Python is a course that introduces students to some basics in the design and implementation of compilers. In this course, we teach the theory behind various components of a compiler as well as the programming techniques involved to put the theory into practice. In particular, we adopt a style of modern compiler construction that builds a compiler by stringing a sequence of translations sharing a common closure-based interpreter-like structures. The chosen programming language for implementation is Python 3. However, you can seek the instructor’s approval to choose a functional programming language as your implementation language if you so wish.

Instructor: Prof. Hongwei Xi

CS501 E1 or E2: Topics in Computer Science: Mobile Application Development

Prerequisites: No previous mobile application development experience is required, but a strong understanding of object-oriented programming

Students will utilize agile software engineering practices in this hands-on course to design and implement mobile applications using Kotlin, JetPack, and the Android SDK. Students will initially implement several small mobile applications utilizing core android technologies, after which students will be grouped into small teams of 2-3, collaborating on a larger final project. Topics will include UI development, navigation, using third party APIs, data persistence, and gestures. Note: Students should register for either the E1 or E2 section other, but not both

Instructor: Ron Czik

CS598 G1 Introduction to Information Security (must also enroll in G2 or G3 discussion section

Prerequisites: For graduate students the prerequisite is CS 611 or programming maturity. For undergraduates, the prerequisite is CS210.

Provides basic concepts needed for understanding information security. Discusses vulnerabilities, design principles, basic algorithms, security definitions, and analytical methods. Covers web security, cryptography, networking, network security and data privacy. Also addresses social, ethical, and policy aspects of security.

Instructor: Sharon Goldberg

CS 599 A1: Programming Language Foundations for Concurrency

Prerequisites: CS 210 and CS 320

Software systems around us are getting more and more complex by the day with multi-processor systems, microservices, and distributed programming. At the center of all this complexity lies concurrency, i.e., processes can execute in arbitrary orders and failures (e.g., network faults, node crashes, etc.) are pervasive. Building software systems is no longer straightforward (probably never was) and the issues introduced by concurrency cannot be ignored. This course will study the foundations of where these challenges arise from and will equip students with tools to handle these challenges.

The ultimate goal is for students to build robust concurrent software systems using state-of-the-art PL tools and techniques. This course satisfies both the MS and PhD Software breadth requirement.

Instructor: Prof. Ankush Das

Course Website: https://ankushdas.github.io/CS599.html

CS599 D1: Artificial General Intelligence

Prerequisites: Any one of the following classes or an equivalent courses

GRS CS 640 Artificial Intelligence
CAS CS 542 Machine Learning
CAS CS 585 Image and Video Computing
CAS CS 505 Introduction to Natural Language Processing
CAS CS 523: Deep Learning

The Artificial General Intelligence course provides the mathematical foundations and definitions of robust, responsible, and safe AGI systems. The class addresses reasoning, decision-making, consciousness, and intelligence. It covers foundation models, attention mechanisms, optimization, pre-training, fine-tuning, and inference, integration of language, vision, and action, self-supervised methods for alignment and reasoning by reinforcement learning and chain-of-thought training. Topics include video analysis and synthesis, autonomous agents and world models, self and social models, multi-agent systems, collective intelligence, self-improvement, genetic and evolutionary methods, continuous learning, and open exploration. The course emphasizes robustness, safety, and control by examining misuse, risks, and mitigation strategies. It concludes with discussions on applications of AGI and their societal impact in the fields of climate science, education, quantum computing, finance, and robotics. The class covers recent work published in Nature, Science, PNAS, ICLR, NeurIPS, ICML, and AAAI, and commercial AI systems, and is based on the instructor’s new book in progress titled Artificial General Intelligence: Mathematical Foundations with Cambridge University Press.

Professor: Prof. Iddo Drori

CS599 N1: Robot Brains! Designing Computing Systems for Robotics

Prerequisites: Recommended for undergraduates in their junior or senior year, and graduate students. Undergraduates must have taken CS210 or equivalent.

Robots that can safely interact with people can help us in our everyday lives, with applications such as elder care and assistive technologies. Achieving this will require addressing critical challenges including real-time performance; limited power and energy budgets; and strict safety, security, and reliability guarantees. Good news: designing computing hardware and software systems for robotics is a promising solution to this “moonshot” technological goal! In this research seminar class, we will survey current work in the emerging subfield of computing systems for robotics. Students will read academic research papers, lead and participate in class discussions, complete short written response assignments, and collaborate in small groups on a final project. Students from all backgrounds and disciplines are welcome and encouraged– diverse viewpoints and ideas are essential to the success of this fundamentally interdisciplinary new subfield.

Instructor: Sabrina Neuman

CS599 P1: Multimodal Machine Learning (must also enroll in P2-P3 discussion section)

Prerequisites: CS542 (Machine Learning) or equivalent

Many applications of artificial intelligence rely on reasoning about data from many different sources (e.g., images, video, language, sound, infrared, Lidar, etc). This course serves as an introduction to methods that aim at using the fusion of these data sources to accomplish downstream tasks. For example, automatically captioning an image requires reasoning about visual and text data, a robot searching for a person lost in a cave may rely on infrared and sound, and autonomous vehicles may use Lidar and video information. This class will explore machine learning and statistical techniques that aim to understand the relationship between modalities. Students will also learn about some of the common issues that arise when dealing with multiple modalities such as data scarcity, positive-unlabeled learning, structured prediction, and the challenges in evaluating these systems.

Professor: Bryan Plummer

Course site: See link on https://www.bryanplummer.com/

CS599 R1: Sublinear Algorithms

Prerequisites: CS 537 and proficiency in understanding and writing mathematical proofs

This course will cover the design and analysis of algorithms that are restricted to run in sublinear time. Such algorithms are typically randomized and produce only approximate answers. A characteristic feature of sublinear algorithms is that they do not have time to access the entire input. Therefore, input representation and the model for accessing the input play an important role. We will study different models appropriate for sublinear algorithms. The course will cover sublinear algorithms discovered in a variety of areas, including graph theory, algebra, geometry, and discrete mathematics, and introduce many techniques that are applied to analyzing sublinear algorithms.

Professor: Sofya Raskhodnikova

Course site: https://cs-people.bu.edu/sofya/sublinear-course/

CS599 S1: Private Data Analysis, Learning, and Inference

Prerequisites: Probability (CS 537 or equivalent)

How can we learn from a data set of sensitive information while providing meaningful privacy to the individuals whose information it contains? The course explores this question, starting from the problems faced by straightforward solutions and moving on to rigorous state-of-the-art solutions using differential privacy. The class will focus on foundations, but also delve into some applied work and on some of the social, ethical, and legal context for the subject. The coursework involves mathematical and programming assignments, as well as a final course project.

Professor: Adam Smith

Course site: https://dpcourse.github.io/

Fall 2024 Course Descriptions

CS391 A1 – Web Application Development

Prerequisites: CS 111, CS 112, and CS 210

Web Application Development is a comprehensive course empowering students to build dynamic web apps. Through hands-on projects, they learn essential code management with Git/GitHub, frontend languages like HTML/CSS, and interactive app development with JavaScript. React is introduced to simplify UI creation and promote code reusability. Students explore industry-standard tools like Next.js for efficient API handling and Vercel for deployment. MongoDB ensures secure data storage. Advanced topics include React Native with GraphQL/Apollo for mobile app development and React-VR with Three.js for immersive web experiences. Graduates master full-stack development and deployment.

Professor: Taymaz Davoodi

CS599 D1 – Artificial General Intelligence

Prerequisites: Any one of the following courses at Boston University or an equivalent (Principles of Machine Learning CS 542, Artificial Intelligence CS 640, Deep Learning CS 523. Image and Video Computing CS 585, Introduction to Natural Language Processing CS 505)

The Artificial General Intelligence course includes three parts: (1) Decision Making and Con- sciousness, (2) Human Intelligence, and (3) Artificial General Intelligence. The first part covers the elements required for a conscious neural network. Consciousness does not mean intelligence, does not require biology, and only requires combining existing machine learning and AI capabili- ties of: (1) self-report, (2) conversational ability available in language models, (3) domain-general abilities available in a generalist agent, (4) sensory processing available in vision-language trans- formers, (5) the ability to act available in vision-language-action transformers, (6) world models, (7) self models, (8) recurrent processing available in sequence models, (9) a global workspace, and (10) a unified agency. The second part of the class covers human intelligence from a neuroscience perspective, including synaptic transmission, perception, movement, emotion, and motivation. We then describe human development and emergent behavior, followed by the neural mech- anisms of learning, memory, language, and cognition. The third part presents a path toward artificial general intelligence including foundation models including mixture of experts and state space models, multi-agent systems, latent space processing, and their mathematical foundations, quality-diversity, open ended exploration, self-improvement, social models, and safety. Topics covered include pre-training, fine-tuning, and inference of an open source foundation model and agents, AI diversity and open ended exploration, and meta, multi-task, few-shot, continual, and lifelong learning. The class covers recent work published in Nature, Science, PNAS, ICLR, NeurIPS, ICML, and AAAI, and is based on notes for a book in progress titled Artificial General Intelligence: Mathematical Foundations by Iddo Drori, with Cambridge University Press.

Professor: Iddo Drori

CS 599 A1: Rounding Techniques in Approximation Algorithms

Prerequisites: Strong undergraduate-level knowledge of algorithms, linear algebra, and probability. Some familiarity with linear programming and at least one of CS 530, 531, or 537 is recommended but not required. Motivated, mathematically mature undergraduate students who have excelled in CS 237 and CS 330 are also welcome.

Description: We will survey a simple but powerful framework for designing approximation algorithms known as “Relax and Round.” Given a (possibly NP-Hard) discrete optimization problem, this framework first relaxes it into a polynomial time solvable one over a continuous domain. It then solves this easier problem, whose solution can have fractional coordinates: for example, it could assign a variable to be half true and half false in a SAT formula.

In the final step, we round this fractional solution to an integer one. Our goal will be to find a rounding procedure that, like a good translator, finds an integer solution that approximately preserves the key properties of the fractional one such as cost. There are many beautiful methods known for performing this rounding step, which will be our main focus. We will discuss many of them, such as the use of integral polytopes, iterative rounding, iterative relaxation, and independent and dependent randomized rounding. The course will emphasize graph problems as well as results from the past decade with exciting open directions.

Course Website: https://nathan-klein.github.io/rounding

Spring 2024 Course Descriptions

CS391 A1 – Web Application Development

Prerequisites: CAS CS 210; or consent of instructor

Web Application Development is a comprehensive course that equips students with practical skills to build dynamic and immersive web applications. Through hands-on exercises and projects, students learn to structure and style web pages using HTML and CSS, create interactive experiences with JavaScript, develop reusable components with React, interact with relational databases using decoupling tools such as ORM and DAO. Additionally, students explore the exciting world of Web-XR, enabling them to build virtual reality experiences with React-VR. By the end of the course, students have the necessary tools and knowledge to develop robust web applications with seamless integration of databases, interactive functionality, and immersive VR experiences. Students are expected to have basic knowledge of OOP principles, coding conventions, and I/O subsystems.

Professor: Taymaz Davoodi

CS392 C1 – Algorithms for Competitive Programming

**Previously posted as CS392 B1**

Prerequisites: CS112 and CS131. Strong performance in CS 112 and CS 131 is expected. An assessment test might be administered in the first week to provide feedback on readiness to take this class.

This course covers essential algorithms necessary to compete in the ACM International Collegiate Programming Contest (ICPC) and similar contests. Active involvement in weekly contests is a mandatory component of the course. Topics covered include standard library classes and data structures, competitive programming contest strategies, string manipulation, divide and conquer, dynamic programming, graph algorithms, number theory, computational geometry, and combinatorics.

Professor: Tiago Januario

CS392 E1 – Intermediate Application Development in C#

Prerequisites: CAS CS 112 and CS210; or consent of instructor

As the course proceeds students will begin looking at more complex topics including fundamental design patterns, while implementing more complex applications. Topics will include: reading and writing from files/streams/databases, exception handling, multithreading, memory management, networking, delegates, generics and LINQ. Students will also perform their own research and development as they choose which 3rd party APIs to incorporate into their final project.

No previous application development experience is required, but a strong understanding of object-oriented programming is required.

The syntax for C#, Java, and C++ are nearly identical. Students will be expected to leverage their understanding of Java and/or C++ syntax to help pivot to C#.

This will be a highly interactive, project oriented and team based course. Attendance, participation and collaboration are mandatory and part of your grade.

Professor: Shereif El-Sheikh

CS391 S1/S2 – Spark! Software Engineering Immersion

Professor: James Kunstle, Langdon White

CS400 A1 – Type Theory and Mechanized Reasoning

**Previously posted as CS491 A1**

Prerequisites: CS131, CS330, CS320 (CS 332 recommended but not required)

Introduction to basic concepts in type theory as it relates to programming languages, mathematics, philosophy, and linguistics. Possible topics include constructive logic, the lambda calculus, simple type theory, polymorphism, type inference, normalization, evaluation, substitution, functional programming, the Curry-Howard isomorphism, dependent type theory, type universes, mechanized mathematics and proof assistants, Kripke semantics and category theoretic semantics, Girard’s paradox.

Professor: Nathan Mull

CS595 T1/T2 Blockchains and their Applications

**Previously posted as CS599 T1/T2**

Prerequisites: One of the following:
A. Students with substantial Computer Science background: graduate or advanced undergraduate students in Computer Science or Computing & Data Sciences
Prerequisites: CAS CS 330 or CDS DS 320 or equivalent, CAS CS 237 or CDS DS 122 or equivalent
B. Business School graduate students (MBA, masters, or PhD) who have also taken at least one course that included substantial hands-on programming in a general-purpose programming language such as Python, Java, C, or JavaScript.
Law School students with a suitable background are also welcome. Exceptions, such as equivalent industry or informal experience, will be considered.

Blockchain technology amalgamates technical tools, economic mechanisms, and system design patterns. It facilitates the construction of information systems with novel combinations of robustness, decentralization, privacy, cost, and flexibility. Beyond their initial use in cryptocurrencies such as Bitcoin, blockchains have become a promising and powerful technology in business, financial services, law, and other areas.

This course covers blockchain technology in a comprehensive, systematic, and interdisciplinary way. It surveys major approaches, variants, and applications of blockchains in these areas. Beyond a solid grasp of the principles, the course aims to build familiarity with practice through numerous case studies and hands-on projects.

To facilitate its interdisciplinary perspective, this course will be open to two categories of students: students with Computer Science background (graduate or advanced undergraduate), and graduate students with a substantial Business or Law background and a working knowledge of computer programming.

Projects will be done in heterogeneous teams combining these categories, and will center on devising and analyzing sample applications of blockchain technology, including both prototype implementations and analysis of its business/legal implications.

Topics covered: disentangling “blockchain”; cryptographic prerequisites; assets and their representations; on-chain programming; state consensus; deployments; decentralized applications (Dapps/Web3); protocol governance; protocol revenue and business models; market structure; privacy and authorization; regulation.

More information can be found at this link.

Notes for Questrom students

1. While this course is explicitly designed to accommodate Questrom students, its formal listing this year is as a Computer Science. Thus, to count as an elective towards Questrom graduate degree requirements, you need to submit aGraduate Elective Request.
2. This is a 4 credit course, unlike Questrom’s usual 3 credits. To avoid extra tuition cost, full-time MBA students can have their tuition cap adjusted by their academic advisor. Professional Evening MBA may contact the instructor for alternative solutions.

Professor: Eran Tromer

CS599 D1 Programming Language Foundations for Concurrency

Prerequisites: CAS CS 320 (Concepts of Programming Languages) or equivalent; CAS CS 210 (Computer Systems) or equivalent; or consent of instructor.This course is aimed at viewing concurrent and distributed software systems from a programmer’s perspective.

We will begin with mathematically defining a programming language using its syntax, type system, and semantics. The course will then cover programming abstractions that are usually used for concurrency, i.e., shared-memory and message-passing. While discussing these abstractions, the course will introduce a variety of type systems and languages that would help us write safe concurrent programs. While discussing these type systems, the course will introduce the unique challenges that concurrency poses to type systems and how they can be addressed. The course will also dive into the deep rooted logical foundations for these type systems via Curry-Howard isomorphism. In this course, students would learn about substructural logics, session types and advanced concepts such as refinement and probabilistic types.

The course would involve a combination of theoretical assignments, fun programming exercises, and paper reading where students would build their own prototypical languages, both in theory and implementation.

More information can be found on the course webpage, linked here.

Professor: Ankush Das

CS 599 G1 Formal Methods in Security and Privacy

Prerequisites: The course has a significant component based on analysis of algorithms, and formal techniques. So, the following are required classes: CAS CS 237 or equivalent; CAS CS 320 or equivalent; CAS CS 330 or equivalent; or consent of instructor. Additionally, some rudimentary understanding of probability and statistics is expected.

Security and privacy breaches are constantly on the news. Often these breaches are due to vulnerabilities in the design and implementations of software components. In this class we will study some of the formal tools that have been developed to formally support the correctness of software with respect to security and privacy requirements. We will focus on a few security and privacy properties such as: information flow control and non-interference, provable security, and differential privacy.

The course consists of a series of lectures on different formalisms that have been developed to reason about security and privacy properties. The basic formalism we will use is the one provided by relational program logics. We will first study a deterministic logic which is useful for reasoning about information flows and non-interference. Then, we will study a probabilistic extension of this logic which supports reasoning about cryptographic security and differential privacy. We will see how different natural proofs from cryptography and differential privacy can be expressed using this formalism. We will also experiment practically with these topics on different examples by using the EasyCrypt tool.

Professor: Marco Gaboardi and Alley Stoughton

CS599 L1 Fine Grained Complexity: An Introduction

Prerequisites: Mathematical maturity, a familiarity with reductions and a familiarity with reductions (e.g. NP hardness reductions) will be expected for this class.

In this course we will introduce fine-grained complexity. Fine-grained complexity studies the constants in the exponents of algorithms. We will give techniques for achieving conditional lower bounds on problems like: diameter, sparse all-pairs shortest paths, longest common subsequence, and more. This class will also cover techniques for worst-case to average case reductions in fine-grained complexity. Mathematical maturity, a familiarity with reductions and a familiarity with reductions (e.g. NP hardness reductions) will be expected for this class.

Professor: Andrea Lincoln

Fall 2023 Course Descriptions

CS392 D1 – Web Application Development

Prerequisites: CAS CS 210; or consent of instructor

Professor: Taymaz Davoodi

CS392 E1 – Intermediate Application Development in C#

Prerequisites: CAS CS 112; or consent of instructor

No previous application development experience is required, but a strong understanding of object-oriented programming is required.

The syntax for C#, Java, and C++ are nearly identical. Students will be expected to leverage their understanding of Java and/or C++ syntax to help pivot to C#.

This will be a highly interactive, project oriented and team based course. Attendance, participation and collaboration are mandatory and part of your grade.

Professor: Shereif El-Sheikh

CS392 S1 – Spark! Software Engineering Immersion

Students will be introduced to all concepts required to work on a modern web development project. This course is intentionally taught with very little prerequisite knowledge to enable students to begin learning these skills earlier in their college path. Students begin by learning basic skills required to build a functioning web application. During the second half of the course, students will be allocated to teams and provided a choice of projects to develop over the course of the semester. Students will submit their final application as their final project on the last day of classes.

Professor: James Kunstle

CS599 C1 – The Meta-Complexity Frontier

Complexity theory studies the cost of solving computational problems using various resources such as time, space, and logic gates. Even after decades of effort, there is a huge gap between what we conjecture about the limits of efficient computation and what we can actually prove. For example, it appears that deciding if a string is “compressible” (MCSP) requires brute-force search, but we cannot (yet) prove this.

This class covers classical and recent “barrier” theorems that explain why it is so difficult to prove lower bounds for computational resource costs. Then, we study emerging connections between meta-mathematics of complexity (provability of theorems) and meta-computational problems like MCSP (existence of algorithms). These modern results offer tantalizing “magnification conditions” — seemingly weak conjectures that, if proven, would immediately imply breakthroughs in complexity theory.

We will explore the inherent tension: either complexity breakthroughs are much closer than they appear or even “weak” lower bounds are inherently difficult to prove. This will prepare students to articulate and work at the research frontier in (meta-)complexity.

Professor: Marco Carmosino

CS599 D1 – Artificial General Intelligence

The Artificial General Intelligence course includes three parts: (1) Decision Making and Consciousness, (2) Human Intelligence, and (3) Artificial General Intelligence. The first part covers the elements required for a conscious neural network. Consciousness does not mean intelligence, does not require biology, and only requires combining existing machine learning and AI capabilities covered in the first part of the class: (1) self-report, (2) conversational ability available in language models, (3) domain-general abilities available in a generalist agent, (4) sensory processing available in vision-language transformers, (5) the ability to act available in vision-language-action transformers, (6) world models, (7) self models, (8) recurrent processing available in sequence models, (9) a global workspace, and (10) a unified agency.

The second part of the class covers human intelligence from a neuroscience perspective, including synaptic transmission, perception, movement, emotion, and motivation. We then describe human development and emergent behavior, followed by the neural mechanisms of learning, memory, language, and cognition.

The third part presents a path toward artificial general intelligence including quality-diversity, open ended exploration, self-improvement and social models. Topics covered include pre-training, fine-tuning, and inference of an open source GPT-4, quality-diversity and open ended exploration, and meta, multi-task, few-shot, continual, and lifelong learning.

Professor: Iddo Drori

CS599 N1 – Robot Brains! Designing Computing Systems for Robotics

Prerequisites: Recommended for undergraduates in their third or fourth year, and graduate students. Undergraduates in their first or second year may join with permission of the instructor.

Robots that can safely interact with people can help us in our everyday lives, with applications such as elder care and assistive technologies. Achieving this will require addressing critical challenges including real-time performance; limited power and energy budgets; and strict safety, security, and reliability guarantees. Good news: designing computing systems for robotics is a promising solution to this “moonshot” technological goal!

In this research seminar class, we will survey current work in the emerging subfield of computing systems for robotics. Students will read academic research papers, lead and participate in class discussions, complete short written response assignments, and collaborate in small groups on a final project. Depending on scope, it is possible that these projects might lead to future academic publications. Students from all backgrounds and disciplines are welcome and encouraged– diverse viewpoints and ideas are essential to the success of this fundamentally interdisciplinary new subfield.

Professor: Sabrina Neuman

Spring 2023 Course Descriptions

CS Practicum

CS 501 E1 & E2 Mobile App Development

Course Description: Students will utilize agile software engineering practices in this hands-on course to design and implement mobile applications using Java and the Android SDK. Students will initially implement several small mobile applications utilizing core android technologies, after which students will be grouped into small groups, collaborating on a larger final project. Topics will include UI development, action bars, multi-touch, gestures, database and file I/O. Students will also learn to make rich applications by consuming location and sensor information from device hardware.

Prerequisites: No previous mobile application development experience is required, but a strong understanding of object-oriented programming and database development (from CS 112 and CS 460 or equivalent) is necessary.

Instructor: Ron Czik

CS Topics

CS 599 S1 Privacy in Statistics and Machine Learning

Course Description: How can we learn from a data set of sensitive information while providing meaningful privacy to the individuals whose information it contains? The course explores this question, starting from the problems faced by straightforward solutions and moving on to rigorous state-of-the-art solutions. The class will focus on foundations, but also delve into some applied work and on some of the social, ethical, and legal context for the subject. Students will be required to complete some mathematical assignments, some programming assignments, and a final course project.

Course Topics: The exact set of topics will evolve as the course proceeds, but a representative list includes:

Attacks on statistical data privacy
What does “privacy” mean in learning and statistics?
Defining privacy: differential privacy and its variants
Achieving privacy: algorithmic tools for differential privacy
Legal and ethical frameworks relating to privacy
Connections to other areas of computer science and statistics

Prerequisites: Students should have a solid grounding in probability (e.g. CS 237/537), linear algebra (e.g. CS 132), multivariate calculus (e.g. MA 225), and algorithms (CS 330). Students should be comfortable reading and writing mathematical proofs involving algorithms and probability. It is strongly recommended that students have taken an additional course in statistics or machine learning beyond CS 237. Programming assignments will be in Python.

Instructor: Adam Smith

Fall 2022 Course Descriptions

CS Practicum

CS 501 E1 & E2 MOBILE APP DEVELOPMENT

Instructor: Ron Czik

CS 599 L1 – User-Centric Systems for Data Science

Course Description: Understanding the behavior of data systems is hard. Questions like “Why does the system return certain results?” and “Why is the execution slow?” arise too often in large-scale data analysis. Answering such questions is still a cumbersome task that requires considerable amount of resources as well as manual work by experts. The course focuses on algorithmic techniques and design principles that help users get meaningful insights into the functionality of data processing systems. In the first part of the course, we will discuss methods for explaining system outputs, including approaches from databases, recommendation engines, and interpretable ML. In the second part, we will focus on techniques that help users understand system performance. We will discuss traditional and causal profiling, end-to-end tracing, root-cause analysis, and invariant checking techniques.

Prerequisites: Strong programming skills, and basic knowledge of data structures, algorithms and computer systems (CS 111, CS 112, CS 210, or equivalent experience).

Instructor: John Liagouris

CS 599 P1 – Applied Machine Learning

Course Description: Covers practical skills in machine learning including techniques for clustering, classification, regression, feature selection, and model compression. Emphasizes hands-on application of methods via programming on real-world datasets.

Prerequisites: CAS CS 132 or MA 242 (or equivalent); CAS CS237 (or equivalent); CAS CS 111 (CS 112 recommended, or equivalent experience); familiarity with calculus; CAS CS 365 recommended.

Instructor: Bryan Plummer

Spring 2021 Course Descriptions

Undergraduate Only

CS 391 E1 FOUNDATIONS OF DATA SCIENCE COUNTS TOWARD GROUP D REQUIREMENT FOR CS MAJOR

Course Description: This course is intended as the first to take for students interested in the aspects of computer science related to data analysis and data management. It specifically serves as a preparation including, but not limited, to the courses CS460, CS506, CS542 and CS565. Course topics will cover data collection, cleaning and visualization. Data modeling and basics of data bases. Mathematical foundations of data science including linear algebra, (multivariate) calculus and convex optimization. Topics in data mining, such as similarity and distance functions, clustering, ranking, networks. Introduction to machine learning. Prediction methods, e.g. regression and common measures.

Prerequisites: CS 112, CS 131, CS 132, and CS 237 required; CS 330 recommended; or consent of instructor.

Instructor: Dora Erdos

Undergraduate and Graduate

CS 501 E1 & E2 MOBILE APP DEVELOPMENT

Instructor: Shereif El-Sheikh

CS 501 T1 & T2 SPARK! PRACTICUM

CS 501 T1: Spark! Software Engineering Practicum

CS 501 T2: Spark! Machine Learning Practicum

Course Description:

The Spark! Practicum offers computer science students the opportunity to apply their computer science and engineering skills by working on real-world projects. The course offers a range of project options where students can improve their technical skills, while also gaining the “soft skills’ necessary to deliver projects aligned to the partner’s goals. These include teamwork and communications skills and software development processes. All students participating in the course are expected to complete the project including a final presentation to the partner organization.

Prerequisites:

CS 501 T1: CS 411 or equivalent, or instructor’s consent

CS 501 T2: CS 542 or equivalent, or instructor’s consent

Instructor: Dharmesh Tarapore

CS 591 A1 RESEARCH OPERATING SYSTEMS PROCESS MANAGEMENT

Course Description: In this class we will be examining 3 approaches to operating systems principles and mechanisms associated with the construction and management of program execution. Specifically we will consider approaches which advance the role of the OS beyond manager to optimizer: 1) the imposition and exploitation of a state vector representaiton in the context of a hybrid library OS, 2) interrupt level energy tracing an its exploitation for optimization, and 3) systemic integration of signal tracing mechanisms and the exploitation of suffix trees. You must have a solid technical understanding of X86-64 VMM implementation, X86-64 systems assembly programming, device driver and interrupt handling, and an understanding of the Linux address space management subsystem. In addition to these technical skills you should be familiar with the literature from SOSP, OSDI and ASPLOS dealing with library OS structure, deterministic execution, record and replay, and tracing. Further you should have a working knowledge of the literature associated with ASC, EbbRT, and SUESS. MSc and undergraduates require the approval of the instructor to register.

Instructor: Jonathan Appavoo

CS 591 C1 GEOMETRY PROCESSING

Course Description:The need for manipulating and analyzing geometric data, in the form of meshes, point clouds, and polygon soups is ubiquitous in many computing applications: graphics for films and gaming, computational fabrication and design, virtual and augmented reality, to name just a few. This has served as fertile ground for the application of ideas from the mathematical fields of differential geometry and topology. In this course, we will read and discuss research papers in this vein, and students will team up in pairs or triplets to complete a free-form final project that implements or extends some of these works. Topics that may be covered include: mesh parametrization, volumetric deformation, spectral processing, applied optimization, discrete notions of curvature, discrete exterior calculus and vector fields, and surface registration and correspondence. Suggestions from students will also be taken into consideration.

Prerequisites: Course participants should have a strong foundation in linear algebra and calculus (multivariable especially), and a good programming base in order to complete a suitable final project. The basic prerequisites are CS 112, CS 132 or MA 242, and MA 225. Additional background in differential geometry or topology could be quite helpful, but is not necessary, as projects may be tailored towards students’ level of knowledge.

Instructor: Ed Chien

CS 591 G1 FORMAL METHODS FOR SECURITY AND PRIVACY

Course Description: Security and privacy breaches are constantly on the news. Often these breaches are due to vulnerabilities in the design and implementations of software components. In this class we will study some of the formal tools, such as type systems, formal verification techniques, and program analysis, that have been developed to formally support the correctness of software with respect to security and privacy requirements. We will focus on readings about techniques designed to formally support security and privacy properties such as: information flow control, differential privacy, provable security, oblivious data structures, and universal composability.

Prerequisites:CAS CS 237 or equivalent; CAS CS 320 or equivalent; CAS CS 330 or equivalent; or consent of instructor.

Instructor: Marco Gaboardi & Alley Stoughton

CS 591 K1 DATA STREAM PROCESSING & ANALYTICS

Course Description: Modern data-driven applications require continuous, low-latency processing of large-scale, rapid data events such as videos, images, emails, chats, clicks, search queries, financial transactions, traffic records, sensor measurements, etc. Extracting knowledge from these data streams is particularly challenging due to their high speed and massive volume.
Distributed stream processing has recently become highly popular across industry and academia due to its capabilities to both improve established data processing tasks and to facilitate novel applications with real-time requirements. In this course, we will study the design and architecture of modern distributed streaming systems as well as fundamental algorithms for analyzing data streams.
Specifically, we will cover the following topics:
– Distributed streaming systems design and architectures
– Fault-tolerance and processing guarantees
– State management
– Windowing semantics and optimizations
– Basic data stream mining algorithms (e.g. sampling, counting, filtering)
– Query languages and libraries for stream processing (e.g. Complex Event Processing, online machine learning)
– Streaming applications and use-cases
– Modern streaming systems, such as Apache Flink, Kafka, and Beam

Prerequisites: To attend this course, students need to have a solid background on data management and database systems (CS 460/660 or equivalent) and distributed systems (CS 451/651 or equivalent). Programming skills and prior experience with Java and/or Scala are also necessary.

Instructor: Vasiliki Kalavri

CS 591 P1 MULTIMODAL MACHINE LEARNING

Course Description: Many applications of artificial intelligence rely on reasoning about data from many different sources (e.g., images, video, language, sound, infrared, Lidar, etc). This course serves as an introduction to methods that aim at using the fusion of these data sources to accomplish some downstream task. For example, automatically captioning an image requires reasoning about visual and text data, a robot searching for a person lost in a cave may rely on infrared and sound, and autonomous vehicles may use Lidar and video information. This class will explore machine learning and statistical techniques that aim to understand the relationship between modalities. Students will also learn about some of the common issues that arise when dealing with multiple modalities such as data scarcity, positive-unlabeled learning, structured prediction, and the challenges in evaluating these systems.

Prerequisites: CS542 (Machine Learning) or equivalent, or consent of instructor. In addition, CS 591 Deep Learning and CS 507 Intro to Optimization are recommended. Students should also be comfortable reading and discussing current research papers.

Instructor: Bryan Plummer

CS 591 S1 PRIVATE DATA ANALYSIS

Course Description: How can we analyze sensitive data sets—computing useful summaries, training accurate learning models, and drawing valid inference—without endangering the privacy of the individuals whose data we are processing? The course will take a rigorous approach to the problem, covering attacks on privacy, current definitional approaches, and state of the art algorithms. The course will mostly focus on “differential privacy”, an emerging standard for private data analysis.

Prerequisites: Undergraduate-level background in algorithms (CS330), graduate-level background in probability (e.g. CS537 or similar). Graduate-level background in statistics or stochastic processes is acceptable; please consult instructor.

Instructor: Adam Smith

CS 591 S2 FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY IN AI

Course Description: Enabling the responsible development of artificial intelligence technologies is one of the major challenges we face as the field moves from research to practice. Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learning in many current and future real-world applications. Now there are calls from across the industry (academia, government, and industry leaders) for technology creators to ensure that AI is used only in ways that benefit people and “to engineer responsibility into the very fabric of the technology.” Overcoming these challenges and enabling responsible development is essential to ensure a future where AI and machine learning can be widely used across different domains. This course will pursue a cross-disciplinary investigation of several areas under the responsible AI umbrella (fairness, interpretability, and accountability). Students will learn about state-of-the-art research and best practices in the covered domains and use available open-source fairness and interpretability toolkits to apply their learnings to publicly available datasets from healthcare, finance, and other domains.

Prerequisites: Graduate standing or permission of instructor. Intermediate knowledge of machine learning algorithms. Intermediate knowledge of machine learning and programming and experience with a high-level programming language (i.e. Java, C++, Python), data structures and basic algorithms.

Instructor: Mehrnoosh Sameki

Graduate Only

GRS CS 791 N1 APPLIED MACHINE LEARNING FOR PUBLIC HEALTH

Course Description: Machine learning (ML) methods are being used in the analysis of data (e.g., text, image, sound, video and biological data) to understand disease and health trends, and to improve individual and population health. The goal of this course is to provide students with prior knowledge of ML techniques with the opportunity to gain hands-on experience working on a data analysis project. Throughout the semester, students will work in groups to apply ML to solve a specific problem submitted by one of our research partners. Weekly lectures will focus on exposing students to (a) applications of ML to public health problems, (b) openly available computational resources, and (c) ongoing research by experts working in ML and health. The course is open to both Masters and PhD students.

Prerequisites: Completion of CS 542 (Machine Learning) or a similar course or permission of the instructor.

Instructor: Elaine Nsoesie, Assistant Professor of Global Health, School of Public Health

Fall 2020 Course Descriptions

Undergraduate Only

CS 391 G1 Introduction to Information Security counts toward Group D requirement for CS Major

Course Description: Provides basic concepts needed for understanding information security. Discusses vulnerabilities, concerns, design principles, basic algorithms, and analytical methods. Covers system, network, and application security, as well as introductions to cryptography and data privacy. Social, legal and political aspects of security are also addressed. This class aims to be a first course in information security, and is a gateway to all other security and cryptography course offerings.

Prerequisites: CS 210 and one Group B course (CS 132, CS 235, or CS 237)

Instructor: Sharon Goldberg

CS 391 T1 Foundations of Data Science counts toward Group D requirement for CS Major

Course Description:

This course is intended as the first to take for students interested in the aspects of computer science related to data analysis and data management. It specifically serves as a preparation including, but not limited, to the courses CS460, CS506, CS542 and CS565. Course topics will cover data collection, cleaning and visualization. Data modeling and basics of data bases. Mathematical foundations of data science including linear algebra, (multivariate) calculus and convex optimization. Topics in data mining, such as similarity and distance functions, clustering, ranking, networks. Introduction to machine learning. Prediction methods, e.g. regression and common measures.

Prerequisites: CS 112 and CS 131 or consent of instructor.

Instructor: Evimaria Terzi

Undergraduate and Graduate

CS 501 T1 & T2 Spark! Practicum

CS 501 T1: Spark! Software Engineering Practicum

CS 501 T2: Spark! Machine Learning Practicum

Course Description:

Prerequisites:

CS 501 T1: CS 411 or equivalent, or instructor’s consent

CS 501 T2: CS 542 or equivalent, or instructor’s consent

Instructor: Dharmesh Tarapore

CS 591 A1 Parallel Computing and Programming

Course Description: This course is an introduction to the theory, techniques, and practices of parallel computing. We will begin by exploring the principle models of parallel computation, such as shared-memory, threads, thread pools, pipelines, and message passing, as well as the challenges these approaches pose, and known methods for addressing them. Next, we will consider various systems and settings that support parallel computation, from multicore architectures and other hardware devices to distributed systems. Finally, we will get acquainted with what parallel programming allows us to do: the design and analysis of distinctively parallel algorithms, their benefits, and their limitations. Students will prepare programming exercises, including the simulation of distributed systems, as well as a team-based final project. Students will need to have, or to acquire, an acquaintance with C++ and Python for this course.

Prerequisites: Systems Programming (CS 210 or equivalent), Data Structures and Algorithms (CS 112 or equivalent), Design and Analysis of Algorithms (CS 330 or equivalent).

Instructor: Amittai Aviram

CS 591 B2 Networks & Markets: Theory & Application

Course Description: Recently, the concept of a network has outgrown the narrower engineering mindset of a collection of interconnected machines to become more broadly relevant in a variety of applied settings which feature connectedness. Biological networks, social networks, online advertising networks, and networks involving hyperlinks, i.e., the WWW, are all examples of domains in which the theory and practice of networking science has now being applied. In parallel with this trend is the rise of online markets as a mediation point for commercial activity. Beginning with the advent of Internet platforms like eBay that employ online auctions, are many fascinating new markets: pay-per-click advertising markets, prediction markets, and two-sided platforms such as Uber and Airbnb. All of these application domains draw deeply on established methodologies that are highly familiar to computer scientists, notably graph theory and algorithms. However, they also build on theoretical foundations that is often unfamiliar territory to computer scientists, such as auction design, mechanism design, and the theory of matching markets. Finally, many important new technologies incorporate a mix of networks and markets, including information networks and recommender systems. In this class, we will build on the highly successful undergraduate text of Easley and Kleinberg to learn about the underlying theory of networks and markets, understand how modern-day digital applications connect to these foundations, and conduct our own independent projects to explore an aspect of a digital market more deeply.

In this class, we will consider networks and markets from a broad and inter-disciplinary perspective, drawing primarily from insights from the Computer Science, Economics, and Marketing communities. This course is designed for students who are potentially interested in either pursuing a career in or conducting research related to online networked platforms. Please note that this course is not about entrepreneurship per se, but will provide useful background for prospective entrepreneurs. The capstone project of the course will be a research effort, conducted by teams of two or three, in which students conduct a quantitative measurement-driven analysis of a computational aspect of an e-commerce firm or of consumer behavior with respect to an e-commerce marketplace.

Prerequisites: This course is designed for declared CS majors who are fulfilling 400-level electives, as well as Masters students and entering Ph.D. students. CS 112, CS 131, and some knowledge of probability and statistics is required. CS 330 is suggested as a co-requisite. While students’ backgrounds will vary, it is expected that students are nearing completion of an undergraduate CS major or are beginning their graduate studies. Seniors who are not CS majors should seek the instructor’s permission to enroll.

Instructor: John Byers

CS 591 E1 & E2 Mobile App Development

Instructor: Shereif El-Sheikh

CS 591 L1 User-Centric Systems for Data Science

The course focuses on algorithmic techniques and design principles that help users get meaningful insights into the functionality of data processing systems. In the first part of the course, we will discuss methods for explaining system outputs, including approaches from databases, recommendation engines, and interpretable ML.

In the second part, we will focus on techniques that help users understand system performance. We will discuss traditional and causal profiling, end-to-end tracing, root-cause analysis, and invariant checking techniques.

The course website can be found here.

Prerequisites: Strong programming skills, and basic knowledge of data structures, algorithms and computer systems (CS 111, CS 112, CS 210, or equivalent experience).

Instructor: John Liagouris

CS 591 R1 Sublinear Algorithms

Course Description: This course will cover the design and analysis of algorithms that are restricted to run in sublinear time. Such algorithms are typically randomized and produce only approximate answers. A characteristic feature of sublinear algorithms is that they do not have time to access the entire input. Therefore, input representation and the model for accessing the input play an important role. We will study different models appropriate for sublinear algorithms. The course will cover sublinear algorithms discovered in a variety of areas, including graph theory, algebra, geometry, image analysis and discrete mathematics, and introduce many techniques that are applied to analyzing sublinear algorithms. We will learn techniques for analyzing randomized algorithms and proving lower bounds on their complexity.

Prerequisites: A course on randomness in computing (equivalent to CS 237 or CS 537) and on algorithm design and analysis (equivalent ot CS 330). You need to be comfortable with mathematical proofs. Most of the assignments in this course require proving some statement and some creativity in finding the proof will be necessary.

Instructor: Sofya Raskhodnikova

CS 591 S1 Fairness, Accountability, and Transparency in AI

Instructor: Mehrnoosh Sameki

CS 591 W1 Towards Universal Natural Language Understanding

Course Description: Current human language technology systems exist primarily for languages where there is a high demand or widespread use. Given that there are thousands of languages in the world where neither annotated data nor existing technology exists, the quest for universal human language technology coverage remains. In this course, we will explore the challenges and ways to extend state-of-the-art machine learning algorithms to problems that involve natural language data for low resource languages for which no automated human language technology capability exists. We will discuss if the barrier to building universal natural language understanding is simply the lack of annotated data or if there is a need for new representations or algorithms for language understanding that can extend to many languages without large training data. This is a research-oriented course on statistical natural language processing (NLP). The course will focus on understanding and extending state-of-the-art machine learning algorithms to problems such as information extraction, named entity recognition, machine translation, and related tasks involving natural language data for many languages. This course involves two primary activities: (1) reading and discussing current research papers and (2) developing a novel approach to extend a past NLP research to problems under low resource settings. It assumes background in basic machine learning. Prior NLP experience is helpful, but not required.

Prerequisites: CAS CS 542 – Machine Learning or equivalent or consent of instructor

Instructor: Derry Wijaya

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