Provisional University Bulletin (2026-2027)

• Minor
• Major (BA/BS)
• Combined BA/MA
• Master of Science
• Doctor of Philosophy

Undergraduate Major and Minor

The undergraduate program in computer science teaches the theoretical fundamentals and practical aspects of computing, preparing students for creative jobs in the computer industry and/or for graduate school. In addition, our curriculum is a stimulating and useful preparation for a number of indirectly related professions, such as law, medicine, and economics.

Graduate Program in Computer Science

The graduate program in computer science is concerned with the fundamental concepts arising in the development and use of computing systems, including the study of computational complexity and information theory, the design and analysis of serial and parallel algorithms, the design of programming languages, systems, and artificial intelligence.

Graduate Program in Computational Linguistics

The graduate program in computational linguistics is computer programming-intensive, and is concerned with the scientific study and processing of language from a computational perspective. It is an interdisciplinary field, which draws on linguistics (phonology, syntax, semantics, and pragmatics), computer science (artificial intelligence, theory of computation, and programming methods), and mathematics (probability, calculus, and linear algebra).

Undergraduate Major and Minor

The Association for Computing Machinery (ACM) has developed a curriculum for computer science that is published annually. Much of the attached text is copied and reworded from the text for Computer Science Curriculum 2023.

Objectives

Computing is a broad field that extends well beyond the boundaries of computer science, drawing its foundations from a wide variety of disciplines. Computer science increasingly plays a methodological role in other disciplines and the impact of technology is a significant subject of study in fields like political science and journalism.

Majors in computer science learn to utilize concepts from many different fields, integrate theory and practice, and appreciate the value of good engineering design. They develop a high-level understanding of systems as a whole and an appreciation for the structure of computer systems, and the processes involved in their construction and analysis, that transcends the implementation details of the various components. They attain an understanding of the theoretical underpinnings of the discipline and how that theory influences practice. Many recurring themes such as abstraction, complexity, and evolutionary change are encountered during their course of study. Students are also educated as engineers, learning how to design, develop, and maintain large, complex software systems and applications, integrating them into workplaces, and developing interfaces to them that support human use. Given the rapid continuous development of the field, majors are educated to be lifelong learners.

In recognition of the increasing significance of computer science as an important discipline for many students throughout the university, the department also offers a selection of courses relevant to students majoring in other disciplines or students double majoring in computer science and another concentration. Several courses enable students to develop skills and understanding of web technology and the impact of the Internet on society. Other courses provide students with the relevant skills and knowledge for assessing the adoption of new technology within an organization. Non-majors can learn the computing and modeling skills that support their work in their core discipline. Students in the social sciences can learn how the skills and methods from their core discipline are relevant for the development of user-friendly technology, or the introduction of technology into the workplace.

Skills

Majors in computer science will develop a number of skills.

1. Understanding the elements of computational thinking.
2. Understanding the trade-off involved in design choices.
3. Identifying and analyzing criteria and specifications appropriate to specific problems, and planning strategies for their solution. Analyzing the extent to which a computer-based system meets the criteria defined for its current use and future development.
4. Deploying appropriate theory, practices, and tools for the specification, design, implementation, and maintenance as well as the evaluation of computer-based systems.
5. Specifying, designing, and implementing computer-based systems.
6. Evaluating systems in terms of general quality attributes and possible tradeoffs presented within the given problem.
7. Applying the principles of effective information management, information organization, and information-retrieval skills to information of various kinds, including text, images, sound, and video.
8. Awareness of security issues. Exposure to issues associated with access, encryption, and networking.
9. Applying the principles of human-computer interaction to the evaluation and construction of a wide range of materials including user interfaces, web pages, multimedia systems and mobile systems.
10. Learning to deploy effectively the tools used for the construction and documentation of software, with particular emphasis on understanding the whole process involved in using computers to solve practical problems.
11. Awareness of the existence of publicly available software (such as APIs or open source materials). Effective engagement in open-source projects. Exposure to effective software reuse.
12. Operating computing equipment and software systems effectively.

Additional skills prepare the majors for success as professionals:

1. Communication. Making succinct presentations to a range of audiences about technical problems and their solutions. This may involve face-to-face, written, or electronic communication.
2. Teamwork. Learning to work effectively as a member of a disciplinary or interdisciplinary development team.
3. Numeracy. Understanding and explaining the quantitative dimensions of a problem.
4. Self-management. Managing one's own learning and development, including time management and organizational skills
5. Professional development. Learning the skills necessary to continue one's own professional development after graduation.

Knowledge

1. Discrete structures
2. Programming fundamentals
3. Graphics and visual computing
4. Algorithms and complexity
5. Intelligent systems and Machine Learning
6. Computational Linguistics
7. Operating systems, concurrency, and security
8. Distributed Computing and Networking Data management
9. Programming languages Software engineering (exposure to different programming paradigms)
10. Numerical methods, simulation and modeling
11. Human-Computer Interaction
12. Entrepreneurial Computer Science

Social Justice

The department does not currently offer a specific course to cover social and professional issues. However, many of these issues are distributed throughout the curriculum. Lifelong learning is a common theme of many of the elective courses. There are many opportunities for experiential learning and the development of oral communication and teamwork skills. Open software, freedom of information, patent and copyright law, software pirating, social inclusion and the digital divide, and the effect of the Internet on political discourse and free speech, are among the topics covered in the Internet & Society course.

Upon Graduating

The BS curriculum is intended for students who want to work as computer scientists in a technical firm or plan to attend graduate school in computer science. The BS requires more rigorous training in the analytic and engineering foundations of computer science. It includes a breadth component that exposes students to a range of topics within computer science, and it also includes a depth component that requires students to explore a sequence of courses in one area of computer science.

The BA requirement reduces the ACM curriculum to the basic set of skills and knowledge. Students graduating with the BA will be prepared to work as application programmers or in secondary areas related to computation like informatics or library science. The BA curriculum is also ideal for students who plan to use their degree in computer science as a basis for graduate study in a professional school or graduate programs in other areas of science or social science.

Graduate Program in Computer Science

Master of Science in Computer Science or Bachelor of Science/Master of Science in Computer Science

Students graduating from the Master's program in Computer Science are expected to:

1. Attain programing and software engineering skills sufficient for building large scale applications and web-based applications.
2. Have a foundational understanding of abstraction in computing, data structures, algorithms, and operating systems.
3. Demonstrate a graduate-level understanding of at least two of the following areas of computer science: systems, artificial intelligence, machine learning, computational linguistics, applied interdisciplinary computer science, and theory.
4. Attain the skills needed to allow further learning and growth in areas of knowledge and skills used within Computer Science.
5. Attain competency and familiarity with advanced elements of computational thinking.
6. Understand the trade-offs involved in software design choices.
7. Be prepared to enter the job market in Computer Science, or to begin PhD studies in Computer Science, or closely related fields.

Doctor of Philosophy in Computer Science

Students graduating with a PhD in Computer Science are expected to:

1. Attain research expertise and complete a significant body of original research that advances a specific field of computer science.
2. Communicate fundamental knowledge of their field of research, as well as details of their own research, in both written and oral form, to expert and non-expert audiences.
3. Teach computer science topics effectively, for those interested in teaching careers.
4. Be competitive for appropriate positions in industry and academia.

Graduate Program in Computational Linguistics

Master of Science in Computational Linguistics

Students who graduate with the MS in Computational Linguistics (CL) are expected to:

1. Attain programming competency and familiarity with theory and algorithms sufficient for carrying out work in the research and development of modern CL/Natural Language Processing (NLP) applications.
2. Have a foundational understanding of data structures, discrete mathematics, and probability and statistics, within computer science (CS).
3. Have foundational competency in and knowledge of the techniques used within theoretical syntactic and formal semantic analysis and investigation, within linguistics.
4. Attain competency and familiarity with corpus creation and analytics, as well as the design and implementation of the machine learning algorithms and techniques used in CL/NLP.
5. Attain the skills needed to allow further learning and growth in areas of knowledge and skills used within CL/NLP, CS, and linguistics.
6. Understand the ethics of appropriate behavior of the field and its practice.
7. Be prepared to enter the job market in CL/NLP, or to begin PhD studies in CL/NLP, CS, or (for students with prior study of the field) linguistics.

It is recommended that students have completed the courses COSI 12b, COSI 29a, and COSI 21a by the end of their sophomore year since they are prerequisites for advanced courses in the major.

In order to declare the major or minor, students must have completed COSI 12b and COSI 21a with a passing grade. Upon completion of these courses, students must fill out the form declaration (see https://www.brandeis.edu/computer-science/undergraduate/advising-faqs.html) and schedule a meeting with the Undergraduate Advising Head (UAH).

The requirements are listed below and include many options. Students can schedule an appointment with the UAH for further information.

Please see the Graduate School of Arts and Sciences Admissions section of the Bulletin for information on Admission to the program. For information about the program’s specific requirements and decision criteria, please visit the department website.

Dylan Cashman
Data science and advanced visual analytics for machine learning.

Joe Delfino
Software Engineering, scalability, distributed and high availability computing, architecture and design.

Antonella Di Lillo, Program Director
Computer vision, pattern recognition, image processing and analysis.

Lotus Goldberg, Vice Chair of Computational Linguistics and Linguistics
Theoretical syntax and the syntax-semantics interface. Ellipsis and null anaphora. Corpus methodology for linguistic analysis and theory.

Timothy Hickey
Scientific visualization. Groupware. Interval arithmetic and constraints. Programming languages.

Pengyu Hong
Computational biology. Image processing. Statistical machine learning.

Constantine Lignos
Computational linguistics. Natural language processing. Language acquisition and change. Psycholinguistics.

Hongfu Liu
Data mining. Machine learning and related applications in social media. Computer vision and bioinformatics.

Harry Mairson
Logic in computer science. Lambda calculus, type theory, and functional programming. Algorithms and complexity.

Sophia Malamud, Computational Linguistics Director of Graduate Studies
Linguistics. Formal semantics and pragmatics of natural languages. Bilingualism, language acquisition and development.

Ju Hyoung Mun
Embedded Computer Systems, Data Systems and Networking

Olga Papaemmanouil
Distributed data management. Databases. Large-scale networked systems.

Keith Plaster, Linguistics Undergraduate Advising Head
Phonological theory. Historical linguistics. Morphology. Heritage linguistics.

James Pustejovsky, Chair of Computational Linguistics and Linguistics
Artificial intelligence. Computational linguistics. Qualitative spatiotemporal reasoning. Lexical semantics. Corpus linguistics.

Elijah Rivera
Computer science education (focusing on pedagogic theories, techniques, and tools). Cognitive aspects of programming. Programming language design (especially human factors). Formal modeling and verification. Program synthesis.

Subhadeep Sarkar
Data management, storage engine, systems, data privacy, and cloud computing.

Liuba Shrira, Co-Chair, Director of Graduate Studies of the Computer Science MS Program
Operating systems. Distributed systems. Long-lived storage systems.

Konstantinos Solomos
Web security and privacy. Large-scale internet measurements. Cybercrime.

Nianwen Xue, Chair
Linguistic structures. Language processing. Natural computational linguistics.

The minimum requirements for the computer science minor are six courses:

1. COSI 12b and COSI 21a.
2. Four additional computer science courses. Only one cross-listed course may be counted toward the elective requirement. COSI 98a may only be used once as an elective. No COSI course numbered 10 or lower may be used toward the minor. Note that COSI 13a Introduction to Programming with Data is not recognized as an elective for the minor.
3. No course with a final grade below C- can count toward fulfilling the requirements for the minor in computer science.
4. No course taken pass/fail may count toward requirements for the minor.

Students with no prior programming experience must satisfactorily complete COSI 10a prior to enrolling in COSI 12b. Students may be exempted from COSI 10a if they either: 1) received a score of 5 on the AP Computer Science A exam (AP Computer Science Principles test does not currently replace any COSI courses), or 2) successfully complete the online placement exam.

Required of All Majors

Foundational Literacies: As part of completing the Computer Science major, students must:

• Fulfill the writing intensive requirement by successfully completing one of the following: COSI 12b or any COSI-subject course with the WI-designation.
• Fulfill the oral communication requirement by successfully completing: COSI 125a, COSI 126a, COSI 142a, COSI 167a, or any OC-designated course within the Division of Science.
• Fulfill the digital literacy requirement by successfully completing one of the following: COSI 12b, COSI 21a, COSI 114a, COSI 114b, or COSI 131a. 

Degree of Bachelor of Arts

The minimum requirements for the Computer Science major include 9 courses:

1. Four core courses: COSI 12b, COSI 21a , COSI 29a, and COSI 131a.
2. Electives: At least five additional COSI courses. Only one cross-listed course may be counted toward the elective requirement. COSI 98a may only be used once as an elective. COSI 99d and COSI courses numbered 10 or lower may not be used toward the major. Note that COSI 13a Introduction to Programming with Data is not recognized as an elective for the major.
3. No course with a final grade below C- can count toward fulfilling the requirements for the major in computer science.
4. No course taken pass/fail may count toward requirements for the major. 

Students with no prior programming experience must satisfactorily complete COSI 10a prior to enrolling in COSI 12b. Students may be exempted from COSI 10a if they either: 1) received a score of 5 on the AP Computer Science A exam (AP Computer Science Principles test does not currently replace any COSI courses), or 2) successfully complete the online placement exam.

Degree of Bachelor of Science

The minimum requirements for the Computer Science major include 14 courses:

1. Six core courses: COSI 12b, COSI 21a, COSI 29a, COSI 121b, COSI 130a, and COSI 131a.
2. Mathematics courses: Math 8a (MATH 36a, ECON 83a, BIOL 51a, or PSYC 51a may substitute for MATH 8a) and MATH 10a.
3. Electives: at least six additional COSI courses. Only two cross-listed courses may be counted toward the elective requirement. COSI 98a may only be used once as an elective. COSI 99d and COSI courses numbered 10 or lower may not be used toward the major. Note that COSI 13a Introduction to Programming with Data is not recognized as an elective for the major.
4. No course with a final grade below C- can count toward fulfilling the requirements for the major in computer science.
5. No course taken pass/fail may count toward requirements for the major.

Students with no prior programming experience must satisfactorily complete COSI 10a prior to enrolling in COSI 12b. Students may be exempted from COSI 10a if they either: 1) received a score of 5 on the AP Computer Science A exam (AP Computer Science Principles test does not currently replace any COSI courses), or 2) successfully complete the online placement exam.

Honors

Graduation with honors in computer science requires completion and defense of a senior honors thesis. Students interested in senior thesis should contact prospective faculty mentors by the spring of their junior year and should take note of the prerequisites for enrollment in COSI 99d (Senior Research). To initiate Honors research, the students must have their Senior Thesis Application form signed by the faculty mentor, the Undergraduate Advising Head; and submit the signed form to the Computer Science Office no later than the end of the spring semester of their junior year.

Available only to Brandeis students who have completed all requirements for the undergraduate Bachelors degree and have performed well in the computer science major and have completed three 100-level COSI electives in addition to those required for their undergraduate degree. Students should apply for the program through the Graduate School of Arts and Sciences and in consultation with their Undergraduate Advising Head in their senior year, at which time they should propose a course of study for the fifth year that typically consists of six graduate-level courses of which at most three may be chosen from the following courses: COSI 210a and COSI 293g. Note: COSI 210a can be repeated at most once for credit.

The five-year BA/MS or BS/MS degree program in Computational Linguistics is designed for outstanding undergraduate students who will have completed all requirements for the undergraduate BA or BS degree within four years at Brandeis, with a major in Linguistics and/or Computer Science. Eligibility for the program is normally limited to students who have maintained a minimum 3.500 GPA in all linguistics and computer science courses taken, and who will have completed at least the following courses before finishing their undergraduate degree:

• COSI 10a
• COSI 12b
• COSI 114a
• COSI 115b

For information on the other application requirements and timeline, please see the Computational Linguistics MS program’s admissions page.

The Objectives, Learning Goals, and final degree earned for the Combined BA/MS or BS/MS Program in Computational Linguistics are the same as those for the Master of Science in Computational Linguistics. 

In this program, students complete the MS in Computational Linguistics by completing the first year courses for the two-year version of the MS in Computational Linguistics at any time during their undergraduate studies, and then completing the two-year degree’s second year courses in one additional (fifth) year of study. The MS degree provides a solid foundation for professional work in the field of computational linguistics or for additional graduate study in computational linguistics, computer science and/or theoretical linguistics. Interested students should consult with Computational Linguistics advising faculty as early as possible during their undergraduate studies, to make sure that all requirements can be planned for and completed on time.

Program of Study

Students admitted to the program must fulfill the following requirements, in addition to completing their undergraduate degree: a schedule of course work designed in conjunction with and approved by the director of graduate studies consisting of a minimum of 24 credits, standardly taken as six four-credit courses. Among these six courses, all students are required to complete the following:

• COSI 231a, taken in the fall term
• COSI 232b, taken in the spring term
• At least one exit requirement course, which cannot be taken until the final semester of the program, unless permission is granted for a two-semester exit requirement (in which case the first of the two semesters in which the exit requirement course is taken will be the first semester of this two-semester program). The exit requirement course is chosen from among:
  • the computational linguistics internship course COSI 293b
  • the computational linguistics capstone project course COSI 295a
  • the computational linguistics master's thesis course COSI 299a. Students must electronically deposit their thesis to ProQuest ETD. For instructions on how to do this, visit the Thesis and Dissertation Guide.
• Students should try to complete all of the following foundational background courses during their undergraduate studies. For any of these courses that were not completed before completing the undergraduate degree, at the determination of the director of graduate studies, students are also required to take the course during the MS year:

• COSI 21a, which if required is taken in the fall term
• COSI 230b, which if required is taken in the spring term
• LING 120b, which if required is taken in the fall term
• LING 130a, which if required is taken in the spring term
• LING 160b, which if required is taken in the fall term

Students who are required to take less than three of these foundational background courses during the MS year must also take the number of computer science, linguistics, and/or mathematics elective courses of their choice needed to reach the six total four-credit courses required for the degree. Students in the program may choose to take additional electives beyond the degree requirements if they wish.

Residence Requirement

The minimum in-person residence requirement is one year after completing the BA or BS degree.

Students may submit a written request to count a course from another department to satisfy one of the required computer science electives. Approval of such a request is based on the relationship of this course to the student's other computer science electives.

Students pursuing the Environmental Studies major can petition for ENVS 110a to be counted toward their COSI electives requirement.

Master of Science in Computer Science

Program of Study

The MS program in computer science is a two-track program, Master of Science (3 semesters) and Master of Science for Non-Majors (4 semesters), designed for outstanding students with undergraduate degrees in any field.

Admitted students with an undergraduate degree in Computer Science must satisfactorily complete an approved schedule of:

1. Nine 4-credit COSI courses numbered 100 or above. Note: COSI 210a can be repeated at most once for credit. 
2. Students who have not successfully completed an Operating Systems course in their undergraduate studies must include COSI 131a in their nine electives. 

In addition to the above requirements, students with an undergraduate degree in a field other than Computer Science must satisfactorily complete or be exempted from three undergraduate core courses:

1. COSI 12b
2. COSI 21a
3. COSI 29a 

At most two core courses can be taken a semester. Students must maintain a minimum average of a B+ in the three undergraduate courses required.

Students with no prior programming experience must satisfactorily complete COSI 10a prior to enrolling in COSI 12b. Students will be exempted from COSI 10a if they successfully complete the online placement exam.

Students can enroll in either the MS Coursework-only track, or the MS Project track. The latter requires them to successfully complete the Masters Project course (COSI 300a or COSI 300b) in the semester after they have completed all of their other coursework.

Students can take up to three (3) 100-level cross listed courses.

Residence Requirement

The minimum in-person residency requirement is three semesters.

Master of Science in Computational Linguistics

Program of Study

The two-year MS program in computational linguistics is designed for outstanding students, preferably with an undergraduate degree in one or more of linguistics or computer science. The MS provides a solid foundation for professional work in the field of computational linguistics, computer science, or (especially for students who enter with prior study of the field) theoretical linguistics.

Students must complete a schedule of course work designed in conjunction with and approved by the director of graduate studies consisting of a minimum of 48 credits, standardly taken as 12 four-credit courses. Among these 12 courses, all students are required to complete:

• COSI 114a, taken in fall of the first year
• COSI 115b, taken in spring of the first year
• COSI 230b, taken in spring of the first year
• COSI 231a, taken in fall of the second year
• COSI 232b, taken in spring of the second year
• At least one exit requirement course, which cannot be taken until the final semester of the program, unless permission is granted for a two-semester exit requirement (in which case the first of the two semesters in which the exit requirement course is taken will be the second-to-last semester of the program). The exit requirement course is chosen from among:
  • the computational linguistics internship course COSI 293b
  • the computational linguistics capstone project course COSI 295a
  • the computational linguistics master's thesis course COSI 299a. Students must electronically deposit their thesis to ProQuest ETD. For instructions on how to do this, visit the Thesis and Dissertation Guide.

At the determination of the director of graduate studies, students are also required to take any of the following foundational background courses for which the student lacks equivalent course background from studies completed prior to entering the program. The director of graduate studies will determine whether the student’s prior course background is sufficient in each particular content area. Depending on this student background, a student may be required to take between none and all six of the following courses:

• COSI 10a, which if required is taken in fall of the first year
• COSI 12b, which if required is taken as soon as the prerequisites are met, and otherwise in fall of the first year
• COSI 21a, which if required is taken as soon as the prerequisites are met, and otherwise in fall of the first year
• LING 120b, which if required is taken in fall of the first year
• LING 130a, which if required is taken in spring of the first year
• LING 160b, which if required is taken in fall of the first year

Students who are not required to take all six foundational background courses must also take the number of computer science, linguistics, and/or mathematics elective courses of their choice needed to reach the 12 total four-credit courses required for the degree. Students in the program may choose to take additional electives beyond the degree requirements if they wish.

Residence Requirement

The minimum in-person residence requirement is two years.

International Master's students need to complete two academic semesters in F1 status to be eligible to take COSI 293g, which counts for one academic credit. In the case that a student coming from another university in the U.S. with a year on an F1 visa is legally able to engage in practical training before the two semesters are up, such training must be approved by the Computer Science department as well as gain CPT authorization and is expected to be a continuation of a summer job or internship in a computer science oriented position. Please note that students may only take COSI 293g classes twice during their degree at Brandeis, and may want to delay until they have more computer science experience. For more information regarding Curricular Practical Experience, please refer to the ISSO CPT page.

Requirements for the Degree of Doctor of Philosophy in Computer Science

Adviser

PhD students are expected to work closely with faculty on research projects of mutual interest. By the end of the second year, the student must obtain the consent of a computer science faculty member to serve as adviser and dissertation committee chair.

Course Requirements

Satisfactory completion of an approved schedule of nine computer science courses (36 credits) numbered 100 or above.

COSI 210a may be used a maximum of two times towards the 9 required courses. COSI 293G and COSI 393G may be taken, combined, no more than 8 times (for 8 total credits) towards these required 9 classes (36 credits).

Note COSI 400D courses do not count toward the nine required courses.

Students may petition the director of graduate studies to substitute courses from other departments that are relevant to their research area. Students who have obtained a Master’s degree in Computer Science may petition to reduce their course requirements by a maximum of 6 courses. This reduction in course requirements does not affect the three-year in-person residency requirement.

Each summer, full-time PhD students are required to do work related to their research area under the supervision of a faculty member. The work can take the form of dissertation research, or can involve an industrial internship. Students doing dissertation research over the summer should register for CONT 250. Students doing an industrial internship must have the consent of their advisor and should register for CONT 250 and COSI 393g. Registration for Graduate Summer Term does not count toward the residency requirement. The summer registration fee will be waived each summer.

Secondary Advisor/Mentor Requirement for PhD Students

To enhance academic and professional development, all PhD students in the Computer Science program will be assigned a secondary advisor or mentor at the start of their program. This individual, distinct from the primary advisor, will provide additional guidance on navigating coursework, research expectations, and other aspects of the PhD journey.

Key Policies:

• The secondary advisor/mentor serves as a supplemental resource for students but does not replace the role of the primary advisor.
• Students may choose to include their secondary advisor/mentor on their dissertation committee, though this is not required.
• If a student wishes to change their secondary advisor/mentor, they must get written agreement from the new mentor and notify the current mentor and the DGS by email. The change must be finalized before the end of their second year in the program.

This initiative aims to ensure that all PhD students have robust academic and professional support throughout their studies.

Teaching Requirement

Teaching is a critical skill required in both academic and industrial careers that follow graduation. All PhD students are required to participate in undergraduate teaching during the course of their studies. Every graduate teaching assistant (TA) is supervised by a member of the faculty, who serves as a mentor to improve the quality of the TA's teaching. Please see the GSAS section on Teaching Requirements and the program handbook for more details.

Dissertation Committee, Proposal and Defense

The advisor and the Director of Graduate Studies will establish a thesis committee in accordance with the GSAS requirements in this bulletin. The candidate must submit an approved written thesis proposal, along with an oral presentation to the thesis committee before the end of the 4th year in the program. A public defense of the completed dissertation is required. A draft of the thesis must be available for public display in the Computer Science office for at least two weeks prior to the dissertation defense.

Residence Requirement

The minimum in-person residency requirement is three years.

Requirements for the Degree of Doctor of Philosophy in Computer Science with an Additional Specialization in Quantitative Biology

Program of Study

Students wishing to obtain the specialization must first gain approval of the Director of Graduate Studies. This should be done as early as possible, ideally during the first year of graduate studies.

Every student, whether or not currently in residence, must register at the beginning of each term. All graduate students will be evaluated by the program each spring. At this evaluation the records of all graduate students will be carefully reviewed with reference to the timely completion of coursework and non-course degree requirements, the quality of the work and research in progress and the student’s overall academic performance in the program.

COSI 10a Introduction to Problem Solving in Python
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Open only to students with no previous programming background. Students may not take COSI 10a if they have received a satisfactory grade in COSI 12b or COSI 21a. May not be taken for credit by students who took COSI 11a in prior years. Does not meet the requirements for the major or minor in Computer Science.

Introduces computer programming and related computer science principles. Through programming, students will develop fundamental skills such as abstract reasoning and problem solving. Students will master programming techniques using the Python programming language and will develop good program design methodology resulting in correct, robust, and maintainable programs. Usually offered every semester.

COSI 12b Advanced Programming Techniques in Java
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Prerequisite: COSI 10a or successful completion of the COSI online placement exam.

Studies advanced programming concepts and techniques utilizing the Java programming language. The course covers software engineering concepts, object-oriented design, design patterns and professional best practices. This is a required foundation course that will prepare you for more advanced courses, new programming languages, and frameworks. Usually offered every year.

COSI 13a Introduction to Programming with Data
[ sn ]

Does not meet the requirements for the major or minor in Computer Science.

 

Introduces students with little or no programming experience to fundamental methods for structuring and transforming data. Students develop skills in data organization and algorithmic reasoning, and learn to express solutions as clear, step-by-step procedures in code. The course prepares students for further study in data-focused courses. Usually offered every year.

COSI 21a Data Structures and the Fundamentals of Computing
[ dl sn ]

Prerequisite: COSI 12b. Graduate students may take this course concurrently with COSI 12b with permission of the Director of Graduate Studies.

Focuses on the design and analysis of algorithms and the use of data structures. Through the introduction of the most widely used data structures employed in solving commonly encountered problems. Students will learn different ways to organize data for easy access and efficient manipulation. The course also covers algorithms to solve classic problems, as well as algorithm design strategies; and computational complexity theory for studying the efficiency of the algorithms. Usually offered every year.

COSI 29a Discrete Structures
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Covers topics in discrete mathematics with applications within computer science. Some of the topics to be covered include graphs and matrices; principles of logic and induction; number theory; counting, summation, and recurrence relations; discrete probability. Usually offered every year.

COSI 45a Effective Communication for Computer Scientists
[ oc ]

Prerequisite: One 100-level COSI elective. Yields half-course credit.

Teaches the basics of good oral communication and presentation, such as structuring a presentation, body language, eye contact, pace and appropriateness for the audience. It will cover, with practice, a range of speaking engagements majors might meet in academia and industry including: presentation of a research paper, software architecture proposal, business elevator pitch, research funding proposal, and so on. Students will present a project already created for a 100-level COSI elective. They will give the presentation in class, receive feedback based on the practices taught and then have a chance to give the presentation a second time. Usually offered every semester.

COSI 91g Introduction to Research Practice

COSI 93a Research Internship and Analysis

Provides students with an opportunity to work in a computer science research lab for one semester, pursuing a project that has the potential to produce new scientific results. Students and the faculty member mutually design a project for the semester that supports the research agenda of the group. Students must attend all research group meetings and present their findings in oral and written form at the end of the semester. The project typically includes background research, some lab work, and collaboration with other group members. Course requires signature of the instructor, is subject to the availability of undergraduate research positions, and is typically open only to juniors and seniors.

COSI 98a Independent Study

Open to exceptional students who wish to study an area of computer science not covered in the standard curriculum. Usually offered every year.

COSI 98b Independent Study

Yields half-course credit. Open to exceptional students who wish to study an area of computer science not covered in the standard curriculum. Usually offered every year.

COSI 99d Senior Research

Prerequisites: Open only to seniors. Submission of a thesis proposal during the spring semester of the junior year. This proposal must be signed by a faculty member who has agreed to supervise the thesis and approved by the Undergraduate Advising Head.

Research assignments and preparation of a report under the direction of an instructor. Usually offered every year.

COSI 101a Fundamentals of Artificial Intelligence
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Prerequisites: COSI 21a.

Survey course in artificial intelligence. Introduction to Lisp and heuristic programming techniques. Topics include problem solving, planning natural language processing, knowledge representation, and computer vision. Usually offered every year.

COSI 103a Fundamentals of Software Engineering
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Prerequisite: COSI 12b.

In this course, you will learn (and practice) the design and construction of large bodies of software using modern software engineering practices, including object oriented design, test driven development, working data, and project management. You will be challenged to solve different kinds of problems, using different approaches and different tools. The course also aims to teach the basic and expected knowledge and practice within industry for entry level developers. Usually offered every year.

COSI 104a Introduction to Machine Learning
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Prerequisites: COSI 12b. Cannot be taken by students who completed COSI/ECON 148b or ECON 148b.

Machine learning is essential to gaining insights into large-scale data and making decisions in a wide spectrum of real world applications. This course will provide you a basic understanding of machine learning techniques (e.g., linear regression, logistic regression, decision trees, neural networks, clustering, state machines and Markov decision processes, etc.), how to evaluate their performance, and demonstrating how these models can be used to solve real-world problems. In addition, this course will give you hands-on experience utilizing these machine learning models. Usually offered every year.

COSI 106b Automation in Software Development
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Prerequisite: COSI 21a.

Programming has always involved some level of automation, from the very first compilers and testing frameworks, to the modern world seemingly dominated by large language models (LLMs). In this course, we’re going to take a tour through many ways that automation has influenced the core programming process: code generation, testing, debugging, maintenance, and documentation. We’ll look at both classical automation techniques, newer AI-driven approaches, and even hybrid approaches - all of which are used industrially in the present day.

This course is a survey of the landscape: we cannot make you an expert in every tool in a semester. Instead, you will get hands-on exposure to a large variety of tools, and will see how they fit into the broader programming ecosystem. Along the way, we’ll also reflect on the trade-offs and limitations of automation, and its human impacts. We’ll wrestle with questions of responsibility, sustainability, correctness, beauty, and ultimately what kind of software world we want to build. Usually offered every year.

COSI 107a Introduction to Computer Security

Prerequisite: COSI 12b.

Introduces students to foundational concepts in computer and network security. Focuses on topics essential in many areas of contemporary software development, including security for application software, uses of cryptography, secure communications in the World-Wide Web, and issues in data privacy. Across these areas students will learn from real-world systems analyzing systems for threats and vulnerabilities, mechanisms of attack, and methods to design and implement defenses against attack. Course assignments will include techniques for both attack and defense as well as written and oral presentations about security issues, design, and implementation. Usually offered every year.

COSI 108a Designing and Analyzing Games
[ sn ]

An introduction to the fundamentals of game design, focusing on the decisions that shape a game’s mechanics, goals, rules, and themes. Through a combination of hands-on group activities, discussions, and written work, students develop original game prototypes and engage in iterative playtesting and critique. The course emphasizes data-informed design approaches, rapid prototyping, and effective idea pitching, while encouraging experimentation and learning through failure. Usually offered every year.

COSI 112a Modal, Temporal, and Spatial Logic for Language
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Prerequisites: COSI 29a, COSI 121b, LING 130a, or PHIL 6a.

Examines the formal and computational properties of logical systems that are used in AI and linguistics. This includes (briefly) propositional logic and first order logic, and then an in-depth study of modal logic, temporal logic, spatial logic, and dynamic logic. Throughout the analyses of these systems, focuses on how they are used in the modeling of linguistic data. Usually offered every second year.

COSI 114a Fundamentals of Natural Language Processing I
[ dl sn ]

Prerequisite: COSI 12b or enrollment in the Master of Science in Computational Linguistics program. LING 100a is recommended but not required.

Introduces the computational properties of natural language and the algorithms and machine learning methods used to process it. Students will develop an understanding of natural language processing (NLP) by implementing statistical NLP algorithms and building NLP tools in Python. Topics include statistical properties of natural language, text classification, language modeling, and machine learning and programming techniques for NLP. Usually offered every year.

COSI 115b Fundamentals of Natural Language Processing II
[ dl sn ]

Prerequisites: COSI 114a, plus at least one of LING 160b, MATH 10a, MATH 10b, MATH 15a, MATH 20a, MATH 22a, or equivalent knowledge.

Provides a fundamental understanding of the problems in natural language understanding by computers, and the theory and practice of current computational linguistic systems. Of interest to students of artificial intelligence, algorithms, and the computational processes of comprehension and understanding. Usually offered every year.

COSI 116a Information Visualization

Prerequisite: COSI 12b. Only MS4 students without COSI 12b can register with approval of the instructor.

Introduces foundational principles, methods, and techniques of visualization to enable creation of effective visual representations of information. Covers the design and evaluation of novel visual encodings for diverse and heterogeneous data, including numerical, ordinal, nominal, and temporal data, network data, and multimedia data. Provides an overview of relevant principles of human vision, perception, and psychology related to the derivation of insights from visual analysis. Create visualizations in Tableau, Python, and JavaScript. Requires programming in Python, JavaScript, HTML, and CSS. Requires extensive writing including documentation, explanations, and discussions of findings from data analyses. Students will choose from datasets across diverse topics such as climate science, sustainability, urban planning, and healthcare data to develop their own visual analyses. Students will analyze data in groups and present their findings both in slide-form and in a writeup that will be publishable in an online setting. Usually offered every year.

COSI 119a Autonomous Robotics
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Prerequisites: COSI 21a, or instructor permission.

Become part of the team developing 'Campus Rover', our long term project. Explore the fundamental 'big questions' in robotics: How do robots know what to do? How do they see the world? How do they know where they are? How do they know where to go? How do they control their bodies? How should robots behave around people? How can we get them to work together? Learn and understand Robot Operating System (ROS) and how software for robots is built. Solve gradually more advanced robotic problems, work with real robots in our Robotics Lab. This is a hands-on course, emphasizing real world implementations. Usually offered every year.

COSI 120a Topics in Computer Systems
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Prerequisite: COSI 21a.

Content will vary from year to year. May be repeated for credit. Prerequisites may vary with the topic area; check with instructor for details. Usually offered every third year.

COSI 121b Structure and Interpretation of Computer Programs
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Prerequisites: COSI 21a and COSI 29a.

An introduction to idioms of programming methodology, and to how programming languages work. Principles of functional programming, data structures and data abstraction; state, imperative and object-oriented programming; lazy data structures; how an interpreter works; metalinguistic abstraction and programming language design; syntax analysis, lexical addressing, continuations and explicit control; continuation-passing style, metacircular and register-machine compilers. Usually offered every year.

COSI 123a Statistical Machine Learning
[ qr sn ]

Prerequisite: COSI 21a,  COSI 104a, MATH 8a, and either MATH 22a or the combination of MATH 15a and MATH 20a.

Focuses on learning from data using statistical analysis tools and deals with the issues of designing algorithms and systems that automatically improve with experience. This course is designed to give students a thorough grounding in the methodologies, technologies, mathematics, and algorithms currently needed by research in learning with data. Usually offered every year.

COSI 124a Information Extraction in Natural Language Processing
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Prerequisite: COSI 114a.

Explores the theory, design, and implementation of systems that extract structured information from natural language. Examines the history of information extraction (IE) through the lens of the programs, evaluations, data sets, and advances in machine learning that have shaped research in this area, beginning with early rule-based and statistical systems and concluding with modern neural network and large language model-based systems.

Students will develop IE systems in Python, building their own end-to-end system and applying it in a project. Students will also study IE ontologies and create new IE data sets. Usually offered every second year.

COSI 125a Human-Computer Interaction
[ oc sn ]

Prerequisite: COSI 12b.

Covers the basic theory and concepts of human-computer interaction. Topics include methodology for designing and testing user interfaces, interaction styles and techniques, design guidelines, and adaptive systems. The laboratory work is designed to give the student practice in a set of basic techniques used in the area of human-computer interaction. Usually offered every second year.

COSI 126a Unsupervised Learning and Data Mining
[ oc sn ]

Prerequisites: COSI 21a, COSI 104a, and either MATH 15a or MATH 22a. MATH 124a or knowledge of optimization is recommended.

Focuses on unsupervised learning and introduces the basic concepts of cluster analysis, feature selection, outlier detection for large-scale and big data analysis. Some advanced unsupervised topics, such as ranking, auto-encoder, generative adversarial network, and self-supervised learning will be introduced as well. Usually offered every second year.

COSI 127b Database Management Systems
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Prerequisites: COSI 21a and COSI 29a.

Introduces database structure, organization, and languages. Studies relational and object-oriented models, query languages, optimization, normalization, file structures and indexes, concurrency control and recovery algorithms, and distributed databases. Usually offered every second year.

COSI 128a Introduction to Computer Networking
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Prerequisite: COSI 21a.

Provides a comprehensive introduction to the principles and practices of computer networking. Students will engage with the principles of data communication, network architectures, and protocols, including state-of-the-art networking technologies. Usually offered every year.

COSI 130a Introduction to the Theory of Computation
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Prerequisite: COSI 29a or the equivalent.

Introduces topics in the theory of computation, including: finite automata and regular languages, pushdown automata and context-free languages, context-sensitive languages and Type 0 languages, Turing machines and Church's thesis, the halting problem and undecidability, and introduction to NP and PSPACE complete problems. Usually offered every year.

COSI 131a Fundamentals of Computer Systems
[ dl sn ]

Prerequisites: COSI 21a.

Fundamental structures of a computer system from hardware abstractions through machine and assembly language, to the overall structure of an operating system and key resource management abstractions. Usually offered every year.

COSI 132a Information Retrieval
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Prerequisites: COSI 21a and either COSI 114a or strong Python programming skills.

Explores the theory and practice of textual information retrieval, including text indexing; Boolean, vector space and probabilistic retrieval models; evaluation; interfaces; linguistic issues; web search; QA and text classification. Students will implement algorithms and design and build a search-based application. Usually offered every year.

COSI 135b Computational Semantics
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Prerequisites: COSI 10A or COSI 12b or equivalent, and COSI 121b or LING 130a, or permission of the instructor. 

A study of the computational treatment of core semantic phenomena in language. After a review of first-order logic and the lambda calculus, the course focuses on three core topics: interrogative structures, including semantics of questions, question-answering systems, dialogue, entailment, commonsense knowledge; meaning update and revision; and computational lexical semantics. Usually offered every second year.

COSI 136a Automatic Speech Recognition
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Prerequisite: COSI 114a or permission of the instructor.

Charts the evolution of automatic speech recognition by exploring the central question: What does it mean to statistically model speech? We begin with the foundational HMM-GMM tradition, which treated speech as a sequence of acoustic events modeled by probabilistic state machines. These acoustic models were historically guided by separate n-gram language models that imposed statistical constraints on word order. We then pivot to the modern deep learning era, examining how neural network architectures—which leverage components like attention, convolutions, and recurrent layers—revolutionized feature representation and sequential modeling to learn a single, unified statistical representation directly from audio. Usually offered every year.

COSI 138a Computational Linguistics Second Year Seminar
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Open to students in the final term of the MS program in Computational Linguistics. Also open to PhD students in Computational Linguistics.

A seminar on research methods, writing, and presentations, and in abstract writing. Aims to help students learn to prepare and deliver oral presentations and written papers of their research work, according to the standards used and expected in this field, both in industry job and academic settings. This will be useful to students applying for jobs in industry or for further graduate work at the Ph.D. level, as well as for the work carried out in such jobs and academic study. Usually offered every year.

COSI 141a System and Network Security
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Prerequisites: COSI 131a and a working knowledge of Python and C programming.

Covers network and systems security, including cryptographic techniques, common attacks (malware, exploitation, DoS), and defenses (firewalls, intrusion detection, proxies). Students will explore security challenges in protocols like DNS and email, as well as modern threats. Through theory and hands-on exercises, they will develop skills to assess and implement security solutions. Usually offered every year.

COSI 142a Embedded Systems Development
[ oc sn wi ]

Prerequisites: COSI 10a and COSI 131a.

A project-based course that teaches the foundational aspects of embedded systems and the development process utilizing Raspberry Pi. Students will will create prototypes of embedded systems, including applications for IoT devices. Usually offered every year.

COSI 143b Data Management for Data Science
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Prerequisite: COSI 21a.

This experiential class will study techniques and systems for ingesting, processing, analyzing, and visualizing large data sets. The end goal of the class is to familiarize students with the data management tools and concepts that can support the full-stack of data science pipelines. Usually offered every second year.

COSI 144b Advanced Topics in Security and Privacy
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Prerequisites: COSI 131a and either COSI 107a or COSI 141a.

Provides an advanced study of security and privacy with a focus on the modern web as a dominant computing platform. It explores how design choices, coding practices, and deployment models can expose online systems to threats. Topics cover offensive web security techniques such as cross-site request forgery, cross-site scripting, and SQL injection, together with defenses like code sandboxing and Content Security Policy. The course also addresses privacy threats across online platforms, including PII leakage, tracking, and fingerprinting, as well as mobile security and emerging vulnerabilities in large language models. Students gain practical experience through hands-on assignments, critical reading and discussion of research papers, and a semester-long project that combines system design, analysis, and evaluation. Usually offered every year.

COSI 146a Principles of Computer System Design
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Prerequisites: COSI 131a.

Topics on the design and engineering of computer systems: techniques for controlling complexity; strong modularity using client-server design; layering; naming; networks; security and privacy; fault-tolerant systems, atomicity and recovery; performance; impact of computer systems on society. Case studies of working systems and readings from the current literature provide comparisons and contrast. Usually offered every second year.

COSI 147a Distributed Systems
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Prerequisite: COSI 131a and C/C++/UNIX programming skills.

This course covers abstractions and implementation techniques for the design of distributed systems. Topics include: distributed state sharing, coherence, storage systems, naming systems, security, fault tolerance and replication, scalability and performance. The assigned readings for the course are from current literature. Usually offered every second year.

COSI 149b Practical Machine Learning with Big Data
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Prerequisites: COSI 165b.

In this experiential learning course, students will learn and practice machine learning techniques (such as regression, clustering, decision trees, support vector machines, assemble techniques, and deep-learning) to tackle real problems in industry and/or interdisciplinary research. Usually offered every second year.

COSI 150a Compiler Design
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Prerequisites: COSI 21a and COSI 29a.

The goal of this course is to provide an overview of the theory and practice of Compiler Design. This course will cover the fundamental components of Compiler Design including scanning, lexical analysis, parsing, semantic analysis, static analysis, and code generation. Usually offered every second year.

COSI 152a Web Application Development
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Prerequisites: COSI 12b.

Introduces web programming that covers the fundamental languages and tools, including HTML/CSS for page layout, javascript/ajax for client-side integration, and server-side programming in Java, Ruby, and SQL. The course also discusses security, scaling/optimization, and multi-tier architectures. Usually offered every second year.

COSI 153a Mobile and Web Development
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Recommended prerequisite: COSI 12b.

Introduces the design and analysis of mobile applications that covers the architecture of mobile devices, APIs for graphical user interfaces on mobile devices, location-aware computing, social networking. Also covers the theory and practice of space and time optimization for these relatively small and slow devices. Usually offered every second year.

COSI 159a Computer Vision
[ oc sn wi ]

Prerequisites: COSI 10a and COSI 104a.

Designed for undergraduate and graduate students majoring/minoring in computer science, the course covers core topics in image/video understanding, such as, object detection/recognition/tracking (with applications in face detection, gesture detection, pose detection), image segmentation (saliency detection, semantic segmentation, co-segmentation), image enhancement (super resolution, image recovery), visual relationship mining (spatial relationship, kinship), 3D reconstruction, image generation, optical flow, and video segmentation. It will also touch several advanced computer vision topics, such as, multi-view image clustering, image captioning, image generation from text, and visual question and answering. Usually offered every second year.

COSI 165b Deep Learning
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Prerequisites:  COSI 21a, COSI 104a, MATH 8a, MATH 10a, and either MATH 15a or MATH 22a, and proficiency in Python programming.

Due to its powerful capability and excellent performance in solving real-world problems, deep learning has become one of the most important machine learning techniques. This course covers the core methods and algorithms of deep learning techniques. Students learn models and algorithms of deep learning, apply deep learning tools, and work on related homework and course projects. Usually offered every year.

COSI 166b Capstone Project for Software Engineering
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Prerequisites: COSI 21a, or instructor permission.

Teaches modern software engineering concepts, emphasizing rapid prototyping, unit testing, usability testing, and collaborative software development principles. Students apply these concepts by building a complex software system in small teams of programmers/developers using current platforms and technologies. Usually offered every year.

COSI 167a Advanced Data Systems
[ oc sn ]

Prerequisite: COSI 127b.

Explores the principles of designing data systems tackling challenges such as optimizing the use of ever-evolving hardware (storage, computation, network), ensuring efficient collection of incoming data, querying dynamic data collections, parallelizing query processing, and more. Students will gain a comprehensive understanding of the key drivers of innovation in data systems: hardware and workloads, delving into the detailed analysis of recent and anticipated trends in both areas. Usually offered every year.

COSI 180a Algorithms
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Prerequisites: COSI 21a and COSI 29a.

Explores a wide range of algorithms for various computational problems, focusing on their fundamental design principles. Usually offered every second year.

COSI 190a Introduction to Programming Language Theory
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Prerequisite: COSI 121b or familiarity with a functional programming language, set theory and logic.

An introduction to the mathematical semantics of functional programming languages. Principles of denotational semantics; lambda calculus and its programming idiom; Church-Rosser theorem and Böhm's theorem; simply typed lambda calculus and its model theory: completeness for the full type frame, Statman's 1-section theorem and completeness of beta-eta reasoning; PCF and full abstraction with parallel operations; linear logic, proofnets, context semantics and geometry of interaction, game semantics, and full abstraction. Usually offered every second year.

COSI 210a Independent Study

Usually offered every year. May be repeated once for credit.

COSI 212b Interactive Machine Learning Systems
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Prerequisites: COSI 21a, COSI 104a, and COSI 116a.

Provides a study of recent progress in interactive machine learning systems research. Students will gain experience reading through computer science research literature and will learn about typical system components of research prototypes. Students will be exposed to modern machine learning, database, and system development tools and will gain experience building tools. Students will take turns presenting papers each week. In addition, each student will be required to build their own interactive machine learning system by the end of the semester. Usually offered every second year.

COSI 216a Topics in Natural Language Processing

Prerequisites vary by topic. The course can be repeated for credit with the approval of the instructor if the student has not previously taken the course covering the same topic.

Reviews recent trends in computational approaches to linguistics, semantics, knowledge representation for language, and issues in parsing and inferences. Topics vary by year. Usually offered every second year.

COSI 217a Topics in Adaptive Systems

Prerequisite: COSI 21a. COSI 101a or COSI 123a are recommended but not required. The course can be repeated for credit with the approval of the instructor if the student has not previously taken the course covering the same topic.

In nature, systems with greater complexity than any designed by humans arise without a designer. The central question explored is: How can complex modular organization arise without an intelligent designer? The class reads about theories of organization in different settings and scales (cells, brains, minds, behavior, society, economies), and studies papers, models, and algorithms from a variety of fields that might shed light on the issue. Usually offered every third year.

COSI 217b Natural Language Processing Systems

Prerequisites vary by topic.

This course looks at building coherent systems designed to tackle real applications in computational linguistics. Particular topics will vary from year to year, but each call will consider some of the following: machine (aided) translation, speech interfaces, information retrieval/extraction, natural language question answering systems, dialogue systems, summarization, computer-assisted language learning, language documentation/linguistics hypothesis testing, and handwriting recognition. Usually offered every year.

COSI 230b Natural Language Annotation for Machine Learning
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Prerequisite: Either COSI 115b, or COSI 114a plus concurrent enrollment in COSI 115b.

Studies corpus linguistics, the computational study of any naturally occurring fragment of language, a key area for data mining, information extraction, and machine learning. Students model, annotate, train, test, evaluate, and revise their own corpus for machine learning. Usually offered every second year.

COSI 231a Advanced Machine Learning Methods for Natural Language Processing
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Prerequisite: COSI 115b.

An introductory graduate-level course covering fundamental concepts in statistical Natural Language Processing (NLP). Provides an in-depth view of linear and neural machine learning methods used in NLP, including supervised and unsupervised methods used in text classification, sequence labeling, sequence-to-sequence transduction, and parsing of syntactic and semantic structures. Usually offered every year.
Benjamin Wellner or Nianwen Xue

COSI 232b Advanced Machine Learning Methods for Natural Language Processing II
[ sn ]

Prerequisite: COSI 231a.

An advanced graduate-level core course exploring state-of-the-art techniques and applications in natural language processing. Covers recent NLP advancements, focusing on large language models, pretrained model fine-tuning, transfer learning, and few-shot learning, and their applications in current NLP tasks such as knowledge extraction, text generation, and dialogue systems. Usually offered every year.

COSI 233a Discourse and Dialog

Prerequisite: COSI 231a.

A critical element of interactive systems is managing the dialog between the system and the user. This course will explore multiple approaches to implementing dialog management components, both symbolic and statistical, including what contextual information must be represented in the dialog states and the decision processes to move from one state to another. Students will implement a dialog manager using industry standards. Usually offered every second year.

COSI 241a Advanced Topics in Graph Mining

Prerequisite: COSI 21a.

Covers many recent techniques and advances in graph mining research. Students read and discuss literature, make presentations, and work on related research projects. Usually offered every second year.

COSI 293a Computer Science Graduate Internship

Permission of the graduate advising head required.

Provides graduate students with the opportunity to participate in industrial experiences in computer science through the completion of a paid or an unpaid internship lasting a minimum of ten weeks. This internship must be approved and monitored by a faculty member.

COSI 293b Computational Linguistics Research Internship

Permission of the graduate advising head required.

Offers graduate students an opportunity to engage in industrial research in computational linguistics by completing a paid or unpaid internship of at least ten weeks duration approved and monitored by a faculty member.

COSI 293g Master's Research Internship

Permission of the Director of Graduate Studies required. Yields quarter-course credit. May be repeated for credit. Intended for Master's students.

Offers graduate students an opportunity to engage in industrial research in computer science by completing a paid or unpaid internship of at least ten weeks duration approved and monitored by a faculty member. Usually offered every semester.

COSI 295a Computational Linguistics Capstone Project

Permission of the graduate advising head required.

Offers graduate students an opportunity to develop an independent project of original research as an active member of a computational linguistics research laboratory. Usually offered every year.

COSI 299a Computational Linguistics Master's Thesis

Permission of the graduate advising head required. May be repeated once for credit.

COSI 300a Master's Project

Usually offered every year.

COSI 300b Master's Project

Usually offered every year.

COSI 393g Graduate Research Internship

Permission of the Director of Graduate Studies required. Yields quarter-course credit. May be repeated for credit. For Ph.D. students only.

Offers Ph.D students an opportunity to engage in industrial research in computer science by completing a paid or unpaid internship of at least ten weeks duration and forty hours per week, approved and monitored by a faculty member. Usually offered every summer.

COSI 400d Dissertation Research

Specific sections for individual faculty members as requested.

COSI 12b Advanced Programming Techniques in Java
[ dl sn wi ]

Prerequisite: COSI 10a or successful completion of the COSI online placement exam.

Studies advanced programming concepts and techniques utilizing the Java programming language. The course covers software engineering concepts, object-oriented design, design patterns and professional best practices. This is a required foundation course that will prepare you for more advanced courses, new programming languages, and frameworks. Usually offered every year.

COSI 21a Data Structures and the Fundamentals of Computing
[ dl sn ]

Prerequisite: COSI 12b. Graduate students may take this course concurrently with COSI 12b with permission of the Director of Graduate Studies.

Focuses on the design and analysis of algorithms and the use of data structures. Through the introduction of the most widely used data structures employed in solving commonly encountered problems. Students will learn different ways to organize data for easy access and efficient manipulation. The course also covers algorithms to solve classic problems, as well as algorithm design strategies; and computational complexity theory for studying the efficiency of the algorithms. Usually offered every year.

COSI 114a Fundamentals of Natural Language Processing I
[ dl sn ]

Prerequisite: COSI 12b or enrollment in the Master of Science in Computational Linguistics program. LING 100a is recommended but not required.

Introduces the computational properties of natural language and the algorithms and machine learning methods used to process it. Students will develop an understanding of natural language processing (NLP) by implementing statistical NLP algorithms and building NLP tools in Python. Topics include statistical properties of natural language, text classification, language modeling, and machine learning and programming techniques for NLP. Usually offered every year.

COSI 115b Fundamentals of Natural Language Processing II
[ dl sn ]

Prerequisites: COSI 114a, plus at least one of LING 160b, MATH 10a, MATH 10b, MATH 15a, MATH 20a, MATH 22a, or equivalent knowledge.

Provides a fundamental understanding of the problems in natural language understanding by computers, and the theory and practice of current computational linguistic systems. Of interest to students of artificial intelligence, algorithms, and the computational processes of comprehension and understanding. Usually offered every year.

COSI 131a Fundamentals of Computer Systems
[ dl sn ]

Prerequisites: COSI 21a.

Fundamental structures of a computer system from hardware abstractions through machine and assembly language, to the overall structure of an operating system and key resource management abstractions. Usually offered every year.

COSI 45a Effective Communication for Computer Scientists
[ oc ]

Prerequisite: One 100-level COSI elective. Yields half-course credit.

Teaches the basics of good oral communication and presentation, such as structuring a presentation, body language, eye contact, pace and appropriateness for the audience. It will cover, with practice, a range of speaking engagements majors might meet in academia and industry including: presentation of a research paper, software architecture proposal, business elevator pitch, research funding proposal, and so on. Students will present a project already created for a 100-level COSI elective. They will give the presentation in class, receive feedback based on the practices taught and then have a chance to give the presentation a second time. Usually offered every semester.

COSI 125a Human-Computer Interaction
[ oc sn ]

Prerequisite: COSI 12b.

Covers the basic theory and concepts of human-computer interaction. Topics include methodology for designing and testing user interfaces, interaction styles and techniques, design guidelines, and adaptive systems. The laboratory work is designed to give the student practice in a set of basic techniques used in the area of human-computer interaction. Usually offered every second year.

COSI 126a Unsupervised Learning and Data Mining
[ oc sn ]

Prerequisites: COSI 21a, COSI 104a, and either MATH 15a or MATH 22a. MATH 124a or knowledge of optimization is recommended.

Focuses on unsupervised learning and introduces the basic concepts of cluster analysis, feature selection, outlier detection for large-scale and big data analysis. Some advanced unsupervised topics, such as ranking, auto-encoder, generative adversarial network, and self-supervised learning will be introduced as well. Usually offered every second year.

COSI 142a Embedded Systems Development
[ oc sn wi ]

Prerequisites: COSI 10a and COSI 131a.

A project-based course that teaches the foundational aspects of embedded systems and the development process utilizing Raspberry Pi. Students will will create prototypes of embedded systems, including applications for IoT devices. Usually offered every year.

COSI 166b Capstone Project for Software Engineering
[ oc sn ]

Prerequisites: COSI 21a, or instructor permission.

Teaches modern software engineering concepts, emphasizing rapid prototyping, unit testing, usability testing, and collaborative software development principles. Students apply these concepts by building a complex software system in small teams of programmers/developers using current platforms and technologies. Usually offered every year.

COSI 167a Advanced Data Systems
[ oc sn ]

Prerequisite: COSI 127b.

Explores the principles of designing data systems tackling challenges such as optimizing the use of ever-evolving hardware (storage, computation, network), ensuring efficient collection of incoming data, querying dynamic data collections, parallelizing query processing, and more. Students will gain a comprehensive understanding of the key drivers of innovation in data systems: hardware and workloads, delving into the detailed analysis of recent and anticipated trends in both areas. Usually offered every year.

COSI 12b Advanced Programming Techniques in Java
[ dl sn wi ]

Prerequisite: COSI 10a or successful completion of the COSI online placement exam.

Studies advanced programming concepts and techniques utilizing the Java programming language. The course covers software engineering concepts, object-oriented design, design patterns and professional best practices. This is a required foundation course that will prepare you for more advanced courses, new programming languages, and frameworks. Usually offered every year.

COSI 142a Embedded Systems Development
[ oc sn wi ]

Prerequisites: COSI 10a and COSI 131a.

A project-based course that teaches the foundational aspects of embedded systems and the development process utilizing Raspberry Pi. Students will will create prototypes of embedded systems, including applications for IoT devices. Usually offered every year.

COSI 159a Computer Vision
[ oc sn wi ]

Prerequisites: COSI 10a and COSI 104a.

Designed for undergraduate and graduate students majoring/minoring in computer science, the course covers core topics in image/video understanding, such as, object detection/recognition/tracking (with applications in face detection, gesture detection, pose detection), image segmentation (saliency detection, semantic segmentation, co-segmentation), image enhancement (super resolution, image recovery), visual relationship mining (spatial relationship, kinship), 3D reconstruction, image generation, optical flow, and video segmentation. It will also touch several advanced computer vision topics, such as, multi-view image clustering, image captioning, image generation from text, and visual question and answering. Usually offered every second year.

MATH 15a Linear Algebra
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Prerequisites: MATH 5a and permission of the instructor, placement by examination, or any mathematics course numbered 10 or above. Students may take MATH 15a or 22a for credit, but not both.

Matrices, determinants, linear equations, vector spaces, eigenvalues, quadratic forms, linear programming. Emphasis on techniques and applications. Usually offered every semester.

MATH 22a Honors Linear Algebra
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Prerequisite: MATH 22a placement exam and permission of the instructor. Students may take MATH 15a or MATH 22a for credit, but not both.

MATH 22a covers linear algebra. The material is similar to MATH 15a but with some additional content, a more theoretical emphasis, and more attention to proofs. Usually offered every fall.

MATH 122a Numerical Methods and Big Data
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Prerequisites: MATH 15a or MATH 22a and MATH 20a or MATH 22b, and basic proficiency with a programming language such as Python or Matlab.

Introduces fundamental techniques of numerical linear algebra widely used for data science and scientific computing. The purpose of this course is to introduce methods that are useful in applications and research. Usually offered every year.

MATH 124a Optimization
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Prerequisites: MATH 15a or MATH 22a, MATH 20a or MATH 22b, MATH 23b, and basic proficiency with a programming language such as Python or Matlab, or permission of the instructor.

Explores the theory of mathematical optimization and its fundamental algorithms, emphasizing problems arising in machine learning, economics, and operations research. Topics include linear and integer programming, convex analysis, and duality. Usually offered every second year.

MATH 125a Mathematics for Machine Learning
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Prerequisites: MATH 15a (or MATH 22a), MATH 20a (or MATH 22b), and MATH 36a.

Serves as a first course in machine learning and general data science, with a focus on the mathematics underlying the various modern machine learning algorithms. The course covers the fundamental concepts of statistical distribution, information theory, statistical learning, optimization and matrix factorizations, as well as classic algorithms such as tree methods, kernel methods and various neural network models. A few important real world examples of current interest will be considered such as computer vision, natural language processing, search engine, recommendation systems, finance, and biology. Usually offered every second year.

PHIL 106b Mathematical Logic
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We continue our rigorous investigation of logic that we began in Phil6A by studying the metatheory of formal systems. We begin with an introduction to sets, relations, and functions, after which we prove the Soundness, Completeness, and Löwenheim-Skolem Theorems for First-Order Logic. We end by examining Turing machines in order to introduce students to the notions of computability and undecidability, and to prepare them for the more advanced study of Gödel's Incompleteness Theorems. Usually offered every second year.