phd berkeley

Information Science: PhD

About the Program

The doctoral program.

The doctoral program in Information Science is a research-oriented program in which the student chooses specific fields of specialization, prepares sufficiently in the literature and the research of those fields to pass a qualifying examination, and completes original research culminating in the written dissertation. The degree of Doctor of Philosophy is conferred in recognition of a candidate's grasp of a broad field of learning and distinguished accomplishment in that field through the contribution of an original piece of research revealing high critical ability and powers of imagination and synthesis.

The I School also offers a master's in Information Management and Systems (MIMS), a master's in  Information and Data Science  (MIDS), and a master's in  Information and Cybersecurity (MICS).

Visit School Website

Admission to the PhD Program

We welcome students from a diverse set of backgrounds; some will be technically educated, some educated in the humanities and social sciences.

The I School typically accepts 3-7 PhD students each year from more than 100 applications. Applications are reviewed by a committee of faculty.

Applicants are evaluated holistically on a number of factors. A strong academic record is important, but not sufficient. A critical factor is the ability to demonstrate a research record and agenda that fit well with specific I School faculty. In a small, interdisciplinary program, it is important that applicants clearly indicate in their Statement of Purpose which faculty member(s) they are interested in researching with, and why.

To be eligible to apply to the PhD in Information Management and Systems program, applicants must meet the following requirements:

A bachelor's degree or its recognized equivalent from an accredited institution.

Superior scholastic record, normally well above a 3.0 GPA.

Indication of appropriate research goals, described in the Statement of Purpose.

For applicants whose academic work has been in a language other than English, the Test of English as a Foreign Language (TOEFL) or International English Language Testing System (IELTS).

Not required: GRE/GMAT. Starting Fall 2021, we no longer require the GRE or GMAT. We recommend you put your time and effort towards the required application materials.

Further  information about I School Ph.D. Admissions  can be found on the I School website. 

Applying for Graduate Admission

Thank you for considering UC Berkeley for graduate study! UC Berkeley offers more than 120 graduate programs representing the breadth and depth of interdisciplinary scholarship. The Graduate Division hosts a complete list of graduate academic programs, departments, degrees offered, and application deadlines can be found on the Graduate Division website.

Prospective students must submit an online application to be considered for admission, in addition to any supplemental materials specific to the program for which they are applying. The online application and steps to take to apply can be found on the Graduate Division website .

Admission Requirements

The minimum graduate admission requirements are:

A bachelor’s degree or recognized equivalent from an accredited institution;

A satisfactory scholastic average, usually a minimum grade-point average (GPA) of 3.0 (B) on a 4.0 scale; and

Enough undergraduate training to do graduate work in your chosen field.

For a list of requirements to complete your graduate application, please see the Graduate Division’s Admissions Requirements page . It is also important to check with the program or department of interest, as they may have additional requirements specific to their program of study and degree. Department contact information can be found here .

Where to apply?

Visit the Berkeley Graduate Division application page .

Doctoral Degree Requirements

Program design.

The School of Information is an interdisciplinary school examining the design, organization, and management of information and information systems. The School of Information draws on the expertise not only of its own faculty but of the full Berkeley campus. We encourage students to take full advantage of being at this world-class University and not feel bound by disciplinary boundaries.

The PhD degree program at the School of Information is a research program. Each student is expected to work with his or her adviser to ensure that the program of study includes:

• A thorough understanding of research methods and research design.
• The ability to review current research critically.
• The ability to understand emerging trends from an interdisciplinary perspective.

Expected PhD Timeline:

• Semester 1:  Identify a faculty adviser
• Semesters 1–4:  Complete breadth courses; complete major and minor requirements
• Semester 4:  Complete the preliminary research paper
• Semester 5:  Complete preliminary exam
• Semester 6–8:  Complete qualifying exam; advance to candidacy
• Four semesters after qualifying exam:  Complete dissertation and give presentation

Please refer to  the School of Information website  for more information.

Breadth Courses

Major/Minor Areas

Related Courses

Info 201 research design and applications for data and analysis 3 units.

Terms offered: Fall 2024, Spring 2024, Fall 2023 Introduces the data sciences landscape, with a particular focus on learning data science techniques to uncover and answer the questions students will encounter in industry. Lectures, readings, discussions, and assignments will teach how to apply disciplined, creative methods to ask better questions, gather data, interpret results, and convey findings to various audiences. The emphasis throughout is on making practical contributions to real decisions that organizations will and should make. Course must be taken for a letter grade to fulfill degree requirements. Research Design and Applications for Data and Analysis: Read More [+]

Hours & Format

Fall and/or spring: 15 weeks - 1.5 hours of lecture per week

Additional Format: One and one-half hours of lecture per week.

Additional Details

Subject/Course Level: Information/Graduate

Grading: Letter grade.

Research Design and Applications for Data and Analysis: Read Less [-]

INFO 202 Information Organization and Retrieval 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course introduces the intellectual foundations of information organization and retrieval: conceptual modeling, semantic representation, vocabulary and metadata design, classification, and standardization, as well as information retrieval practices, technology, and applications, including computational processes for analyzing information in both textual and non-textual formats. Information Organization and Retrieval: Read More [+]

Rules & Requirements

Prerequisites: Students should have a working knowledge of the Python programming language

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Format: Three hours of lecture per week.

Information Organization and Retrieval: Read Less [-]

INFO 203 Social Issues of Information 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course is designed to be an introduction to the topics and issues associated with information and information technology and its role in society. Throughout the semester we will consider both the consequence and impact of technologies on social groups and on social interaction and how society defines and shapes the technologies that are produced. Students will be exposed to a broad range of applied and practical problems, theoretical issues, as well as methods used in social scientific analysis. The four sections of the course are: 1) theories of technology in society, 2) information technology in workplaces 3) automation vs. humans, and 4) networked sociability. Social Issues of Information: Read More [+]

Social Issues of Information: Read Less [-]

INFO 205 Information Law and Policy 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course uses examples from various commercial domains—retail, health, credit, entertainment, social media, and biosensing/quantified self—to explore legal and ethical issues including freedom of expression, privacy, research ethics, consumer protection, information and cybersecurity, and copyright. The class emphasizes how existing legal and policy frameworks constrain, inform, and enable the architecture, interfaces, data practices, and consumer facing policies and documentation of such offerings; and, fosters reflection on the ethical impact of information and communication technologies and the role of information professionals in legal and ethical work. Information Law and Policy: Read More [+]

Prerequisites: Consent of instructor required for nonmajors

Instructor: Mulligan

Information Law and Policy: Read Less [-]

INFO 206A Introduction to Programming and Computation 2 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course introduces the basics of computer programming that are essential for those interested in computer science, data science, and information management. Students will write their own interactive programs (in Python) to analyze data, process text, draw graphics, manipulate images, and simulate physical systems. Problem decomposition, program efficiency, and good programming style are emphasized throughout the course. Introduction to Programming and Computation: Read More [+]

Fall and/or spring: 7.5 weeks - 4 hours of lecture per week

Additional Format: Four hours of lecture per week for seven and one-half weeks.

Instructor: Farid

Introduction to Programming and Computation: Read Less [-]

INFO 206B Introduction to Data Structures and Analytics 2 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 The ability to represent, manipulate, and analyze structured data sets is foundational to the modern practice of data science. This course introduces students to the fundamentals of data structures and data analysis (in Python). Best practices for writing code are emphasized throughout the course. This course forms the second half of a sequence that begins with INFO 106. It may also be taken as a stand-alone course by any student that has sufficient Python experience. Introduction to Data Structures and Analytics: Read More [+]

Prerequisites: INFO 206A or equivalent, or permission of instructor

Credit Restrictions: Course must be completed for a letter grade to fulfill degree requirements.

Formerly known as: Information 206

Introduction to Data Structures and Analytics: Read Less [-]

INFO 213 Introduction to User Experience Design 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course will provide an introduction to the field of Human-Computer Interaction (HCI). Students will learn to apply design thinking to User Experience (UX) design, prototyping, & evaluation. The course will also cover special topic areas within HCI. Introduction to User Experience Design: Read More [+]

Introduction to User Experience Design: Read Less [-]

INFO 214 User Experience Research 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course addresses concepts and methods of user experience research, from understanding and identifying needs, to evaluating concepts and designs, to assessing the usability of products and solutions. We emphasize methods of collecting and interpreting qualitative data about user activities, working both individually and in teams, and translating them into design decisions. Students gain hands-on practice with observation, interview, survey , focus groups, and expert review. Team activities and group work are required during class and for most assignments. Additional topics include research in enterprise, consulting, and startup organizations, lean/agile techniques, mobile research approaches, and strategies for communicating findings. User Experience Research: Read More [+]

Additional Format: Three hours of Lecture per week for 15 weeks.

User Experience Research: Read Less [-]

INFO 215 Product Design Studio 3 Units

Terms offered: Fall 2024, Fall 2023 This course will give participants hands-on digital product design experience oriented around current industry practice. The course will be project-based with an emphasis on iteration, practice, and critique. During the course, participants will work on a series of design projects through a full design process, including developing appropriate design deliverables, gathering feedback, and iterating on designs. Product Design Studio: Read More [+]

Objectives & Outcomes

Course Objectives: The course objective is to provide students interested in web and mobile Product Design with skills, practice, and experience that will prepare them for careers in product design and design-related roles.

Prerequisites: DES INV 15 or COMPSCI 160 or INFO 213 AND INFO 214; or permission of the instructor. Students can take INFO 214 and INFO 215 concurrently, but students may not drop INFO 214 and remain in INFO 215

Formerly known as: Information Systems and Management 215

Product Design Studio: Read Less [-]

INFO 217A Human-Computer Interaction (HCI) Research 3 Units

Terms offered: Spring 2024, Fall 2021, Fall 2020 This course is a graduate-level introduction to HCI research. Students will learn to conduct original HCI research by reading and discussing research papers while collaborating on a semester-long research project. Each week the class will focus on a theme of HCI research and review foundational and cutting-edge research relevant to that theme. The class will focus on the following areas of HCI research: ubiquitous computing , social computing, critical theory, and human-AI interaction. In addition to these research topics the class will introduce common qualitative and quantitative methodologies in HCI research. Human-Computer Interaction (HCI) Research: Read More [+]

Instructor: Salehi

Human-Computer Interaction (HCI) Research: Read Less [-]

INFO 218 Concepts of Information 3 Units

Terms offered: Spring 2024, Spring 2022, Spring 2020 As it's generally used, "information" is a collection of notions, rather than a single coherent concept. In this course, we'll examine conceptions of information based in information theory, philosophy, social science, economics, and history. Issues include: How compatible are these conceptions; can we talk about "information" in the abstract? What work do these various notions play in discussions of literacy, intellectual property, advertising, and the political process? And where does this leave "information studies" and "the information society"? Concepts of Information: Read More [+]

Prerequisites: Graduate standing

Instructors: Duguid, Nunberg

Concepts of Information: Read Less [-]

INFO 225 Leadership and Management 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2021 This course focuses on the practice of leadership, collaboration, and people management in contemporary, distributed, information and technology-rich organizations. Not just for potential people managers, this course is derived from the premise that a foundation in leadership, management, and collaboration is essential for individuals in all roles, at any stage of their career. To build this foundation we will take a hybrid approach, engaging literature from disciplines such as social psychology, management, and organizational behavior, as well as leveraging case studies and practical exercises. The course will place a special emphasis on understanding and reacting to social dynamics in workplace hierarchies and teams. Leadership and Management: Read More [+]

Leadership and Management: Read Less [-]

INFO 231 Decisions and Algorithms 3 Units

Terms offered: Fall 2024, Spring 2013, Spring 2011 This class is for graduate students interested in getting an advanced understanding of judgments and decisions made with predictive algorithms. The course will survey the vast literature on the psychology of how people arrive at judgments and make decisions with the help of statistical information, focused mostly on experimental lab evidence from cognitive and social psychology. Then study the burgeoning evidence on how people use statistical algorithms in practice, exploring field evidence from a range of settings from criminal justice and healthcare to housing and labor markets. Special attention is paid to psychological principles that impact the effectiveness and fairness of algorithms deployed at scale. Decisions and Algorithms: Read More [+]

Course Objectives: Help students understand systematic human errors and explore potential algorithmic solutions.

Decisions and Algorithms: Read Less [-]

INFO 233 Social Psychology and Information Technology 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Discusses application of social psychological theory and research to information technologies and systems; we focus on sociological social psychology, which largely focuses on group processes, networks, and interpersonal relationships. Information technologies considered include software systems used on the internet such as social networks, email, and social games, as well as specific hardware technologies such as mobile devices, computers , wearables, and virtual/augmented reality devices. We examine human communication practices, through the lens of different social psychology theories, including: symbolic interaction, identity theories, social exchange theory, status construction theory, and social networks and social structure theory. Social Psychology and Information Technology: Read More [+]

Instructor: Cheshire

Social Psychology and Information Technology: Read Less [-]

INFO 234 Information Technology Economics, Strategy, and Policy 3 Units

Terms offered: Spring 2024, Spring 2022, Spring 2021 This course applies economic tools and principles, including game theory, industrial organization, information economics, and behavioral economics, to analyze business strategies and public policy issues surrounding information technologies and IT industries. Topics include: economics of information goods, services, and platforms; economics of information and asymmetric information; economics of artificial intelligence, cybersecurity, data privacy, and peer production; strategic pricing; strategic complements and substitutes; competition and antitrust; Internet industry structure and regulation; network cascades, network formation, and network structure. Information Technology Economics, Strategy, and Policy: Read More [+]

Course Objectives: INFO234 is a graduate level course in the school's topical area of Information Economics and Policy, and can be taken by the masters and doctoral students to satisfy their respective degree requirements.

Student Learning Outcomes: Students will learn to identify, describe, and analyze business strategies and public policy issues of particular relevance to the information industry. Students will learn and apply economic tools and principles to analyze phenomena such as platform competition, social epidemics, and peer production, and current policy issues such as network neutrality and information privacy. Through integrated assignments and project work, the students will apply the theoretical concepts and analytic tools learned in lectures and readings to develop and evaluate a business model, product, or service of their choosing, e.g., a start-up idea they are pursuing.

Instructor: Chuang

Information Technology Economics, Strategy, and Policy: Read Less [-]

INFO 239 Technology and Delegation 3 Units

Terms offered: Fall 2021, Fall 2019, Fall 2018 The introduction of technology increasingly delegates responsibility to technical actors, often reducing traditional forms of transparency and challenging traditional methods for accountability. This course explores the interaction between technical design and values including: privacy, accessibility, fairness, and freedom of expression. We will draw on literature from design, science and technology studies, computer science, law, and ethics, as well as primary sources in policy, standards and source code. We will investigate approaches to identifying the value implications of technical designs and use methods and tools for intentionally building in values at the outset. Technology and Delegation: Read More [+]

Technology and Delegation: Read Less [-]

INFO 241 Experiments and Causal Inference 3 Units

Terms offered: Fall 2024, Spring 2024, Fall 2022 This course introduces students to experimentation in data science. Particular attention is paid to the formation of causal questions, and the design and analysis of experiments to provide answers to these questions. This topic has increased considerably in importance since 1995, as researchers have learned to think creatively about how to generate data in more scientific ways, and developments in information technology has facilitated the development of better data gathering. Experiments and Causal Inference: Read More [+]

Experiments and Causal Inference: Read Less [-]

INFO 247 Information Visualization and Presentation 4 Units

Terms offered: Spring 2023, Spring 2022, Spring 2021 The design and presentation of digital information. Use of graphics, animation, sound, visualization software, and hypermedia in presenting information to the user. Methods of presenting complex information to enhance comprehension and analysis. Incorporation of visualization techniques into human-computer interfaces. Course must be completed for a letter grade to fulfill degree requirements. Information Visualization and Presentation: Read More [+]

Prerequisites: INFO 206B or knowledge of programming and data structures with consent of instructor

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of laboratory per week

Additional Format: Three hours of lecture and one hour of laboratory per week.

Instructor: Hearst

Information Visualization and Presentation: Read Less [-]

INFO 251 Applied Machine Learning 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Provides a theoretical and practical introduction to modern techniques in applied machine learning. Covers key concepts in supervised and unsupervised machine learning, including the design of machine learning experiments, algorithms for prediction and inference, optimization, and evaluation. Students will learn functional, procedural, and statistical programming techniques for working with real-world data. Applied Machine Learning: Read More [+]

Student Learning Outcomes: • Effectively design, execute, and critique experimental and non-experimental methods from statistics, machine learning, and econometrics. • Implement basic algorithms on structured and unstructured data, and evaluate the performance of these algorithms on a variety of real-world datasets. • Understand the difference between causal and non-causal relationships, and which situations and methods are appropriate for both forms of analysis. • Understand the principles, advantages, and disadvantages of different algorithms for supervised and unsupervised machine learning.

Prerequisites: INFO 206B , or equivalent course in Python programming; INFO 271B , or equivalent graduate-level course in statistics or econometrics; or permission of instructor

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Format: Three hours of lecture and one hour of discussion per week.

Instructor: Blumenstock

Applied Machine Learning: Read Less [-]

INFO 253A Front-End Web Architecture 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course is a survey of technologies that power the user interfaces of web applications on a variety of devices today, including desktop, mobile, and tablet devices. This course will delve into some of the core Front-End languages and frameworks (HTML/CSS/JS/React/Redux), as well as the underlying technologies enable web applications (HTTP, URI, JSON). The goal of this course is to provide an overview of the technical issues surrounding user interfaces powered by the web today, and to provide a solid and comprehensive perspective of the Web's constantly evolving landscape. Front-End Web Architecture: Read More [+]

Prerequisites: Introductory programming

Fall and/or spring: 15 weeks - 2 hours of lecture and 1 hour of laboratory per week

Additional Format: Two hours of lecture and one hour of laboratory per week.

Formerly known as: Information 253

Front-End Web Architecture: Read Less [-]

INFO 253B Back-End Web Architecture 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course is a survey of web technologies that are used to build back-end systems that enable rich web applications. Utilizing technologies such as Python, Flask, Docker, RDBMS/NoSQL databases, and Spark, this class aims to cover the foundational concepts that drive the web today. This class focuses on building APIs using micro-services that power everything from content management systems to data engineering pipelines that provide insights by processing large amounts of data. The goal of this course is to provide an overview of the technical issues surrounding back-end systems today, and to provide a solid and comprehensive perspective of the web's constantly evolving landscape. Back-End Web Architecture: Read More [+]

Back-End Web Architecture: Read Less [-]

INFO 255 Privacy Engineering 3 Units

Terms offered: Spring 2024, Spring 2023 The course overviews a broad number of paradigms of privacy from a technical point of view. The course is designed to assist system engineers and information systems professionals in getting familiar with the subject of privacy engineering and train them in implementing those mechanisms. In addition, the course is designed to coach those professionals to critically think about the strengths and weaknesses of the different privacy paradigms. These skills are important for cybersecurity professionals and enable them to effectively incorporate privacy-awareness in the design phase of their products. Privacy Engineering: Read More [+]

Course Objectives: Critique the strengths and weaknesses of the different privacy paradigms Describe the different technical paradigms of privacy that are applicable for systems engineering Implement such privacy paradigms, and embed them in information systems during the design process and the implementation phase Stay updated about the state of the art in the field of privacy engineering

Credit Restrictions: Students will receive no credit for INFO 255 after completing INFO 255 . A deficient grade in INFO 255 may be removed by taking INFO 255 .

Privacy Engineering: Read Less [-]

INFO 256 Applied Natural Language Processing 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2021 This course examines the use of natural language processing as a set of methods for exploring and reasoning about text as data, focusing especially on the applied side of NLP — using existing NLP methods and libraries in Python in new and creative ways. Topics include part-of-speech tagging, shallow parsing, text classification, information extraction, incorporation of lexicons and ontologies into text analysis, and question answering. Students will apply and extend existing software tools to text-processing problems. Applied Natural Language Processing: Read More [+]

Prerequisites: INFO 206A and INFO 206B or proficient programming in Python (programs of at least 200 lines of code). Proficient with basic statistics and probabilities

Instructor: Bamman

Applied Natural Language Processing: Read Less [-]

INFO 258 Data Engineering 4 Units

Terms offered: Spring 2024, Fall 2022 This course will cover the principles and practices of managing data at scale, with a focus on use cases in data analysis and machine learning. We will cover the entire life cycle of data management and science, ranging from data preparation to exploration, visualization and analysis, to machine learning and collaboration, with a focus on ensuring reliable, scalable operationalization. ensuring reliable, scalable operationalization. Data Engineering: Read More [+]

Prerequisites: INFO 206B or equivalent college-level course in computer science in Python with a C- or better AND COMPSCI C100/ DATA C100 / STAT C100 or COMPSCI 189 or INFO 251 or DATA 144 or equivalent college-level course in data science with a C- or better

Instructors: Hellerstein, Parameswaran, Jain

Data Engineering: Read Less [-]

INFO 259 Natural Language Processing 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course introduces students to natural language processing and exposes them to the variety of methods available for reasoning about text in computational systems. NLP is deeply interdisciplinary, drawing on both linguistics and computer science, and helps drive much contemporary work in text analysis (as used in computational social science, the digital humanities, and computational journalism). We will focus on major algorithms used in NLP for various applications (part-of-speech tagging, parsing, coreference resolution, machine translation) and on the linguistic phenomena those algorithms attempt to model. Students will implement algorithms and create linguistically annotated data on which those algorithms depend. Natural Language Processing: Read More [+]

Prerequisites: Familiarity with data structures, algorithms, linear algebra, and probability

Natural Language Processing: Read Less [-]

INFO C262 Theory and Practice of Tangible User Interfaces 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course explores the theory and practice of Tangible User Interfaces, a new approach to Human Computer Interaction that focuses on the physical interaction with computational media. The topics covered in the course include theoretical framework, design examples, enabling technologies, and evaluation of Tangible User Interfaces. Students will design and develop experimental Tangible User Interfaces using physical computing prototyping tools and write a final project report. Theory and Practice of Tangible User Interfaces: Read More [+]

Instructor: Ryokai

Also listed as: NWMEDIA C262

Theory and Practice of Tangible User Interfaces: Read Less [-]

INFO C265 Interface Aesthetics 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course will cover new interface metaphors beyond desktops (e.g., for mobile devices, computationally enhanced environments, tangible user interfaces) but will also cover visual design basics (e.g., color, layout, typography, iconography) so that we have systematic and critical understanding of aesthetically engaging interfaces. Students will get a hands-on learning experience on these topics through course projects, design critiques , and discussions, in addition to lectures and readings. Interface Aesthetics: Read More [+]

Also listed as: NWMEDIA C265

Interface Aesthetics: Read Less [-]

INFO 271B Quantitative Research Methods for Information Systems and Management 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Introduction to many different types of quantitative research methods, with an emphasis on linking quantitative statistical techniques to real-world research methods. Introductory and intermediate topics include: defining research problems, theory testing, casual inference, probability, and univariate statistics. Research design and methodology topics include: primary/secondary survey data analysis, experimental designs, and coding qualitative data for quantitative analysis. Quantitative Research Methods for Information Systems and Management: Read More [+]

Prerequisites: Introductory statistics recommended

Quantitative Research Methods for Information Systems and Management: Read Less [-]

INFO 272 Qualitative Research Methods for Information Systems and Management 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Theory and practice of naturalistic inquiry. Grounded theory. Ethnographic methods including interviews, focus groups, naturalistic observation. Case studies. Analysis of qualitative data. Issues of validity and generalizability in qualitative research. Qualitative Research Methods for Information Systems and Management: Read More [+]

Instructor: Burrell

Qualitative Research Methods for Information Systems and Management: Read Less [-]

INFO 283 Information and Communications Technology for Development 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This seminar reviews current literature and debates regarding Information and Communication Technologies and Development (ICTD). This is an interdisciplinary and practice-oriented field that draws on insights from economics, sociology, engineering, computer science, management, public health, etc. Information and Communications Technology for Development: Read More [+]

Fall and/or spring: 15 weeks - 3 hours of seminar per week

Additional Format: Three hours of seminar per week.

Instructor: Saxenian

Formerly known as: Information C283

Information and Communications Technology for Development: Read Less [-]

INFO 288 Big Data and Development 3 Units

Terms offered: Spring 2024, Spring 2021, Spring 2019 As new sources of digital data proliferate in developing economies, there is the exciting possibility that such data could be used to benefit the world’s poor. Through a careful reading of recent research and through hands-on analysis of large-scale datasets, this course introduces students to the opportunities and challenges for data-intensive approaches to international development. Students should be prepared to dissect, discuss, and replicate academic publications from several fields including development economics, machine learning, information science, and computational social science. Students will also conduct original statistical and computational analysis of real-world data. Big Data and Development: Read More [+]

Prerequisites: Students are expected to have prior graduate training in machine learning, econometrics, or a related field

Big Data and Development: Read Less [-]

INFO 289 Public Interest Cybersecurity: The Citizen Clinic Practicum 3 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This course provides students with real-world experience assisting politically vulnerable organizations and persons around the world to develop and implement sound cybersecurity practices. In the classroom, students study basic theories and practices of digital security, intricacies of protecting largely under-resourced organizations, and tools needed to manage risk in complex political, sociological, legal, and ethical contexts. In the clinic , students work in teams supervised by Clinic staff to provide direct cybersecurity assistance to civil society organizations. We emphasize pragmatic, workable solutions that take into account the unique needs of each partner organization. Public Interest Cybersecurity: The Citizen Clinic Practicum: Read More [+]

Repeat rules: Course may be repeated for credit with instructor consent.

Public Interest Cybersecurity: The Citizen Clinic Practicum: Read Less [-]

INFO 290 Special Topics in Information 1 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Specific topics, hours, and credit may vary from section to section, year to year. Special Topics in Information: Read More [+]

Repeat rules: Course may be repeated for credit when topic changes. Students may enroll in multiple sections of this course within the same semester.

Fall and/or spring: 8 weeks - 2-8 hours of lecture per week 15 weeks - 1-4 hours of lecture per week

Summer: 10 weeks - 1.5-6 hours of lecture per week

Additional Format: One to four hours of lecture per week. One and one-half to six hours of lecture per week for 10 weeks. Two to eight hours of lecture per week for 8 weeks.

Special Topics in Information: Read Less [-]

INFO 290M Special Topics in Management 1 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Specific topics, hours, and credit may vary from section to section and year to year. Special Topics in Management: Read More [+]

Additional Format: One to four hours of lecture per week. Two to eight hours of lecture per week for 8 weeks.

Special Topics in Management: Read Less [-]

INFO 290S Special Topics in Social Science and Policy 2 - 4 Units

Terms offered: Fall 2024, Fall 2023, Spring 2023 Specific topics, hours, and credit may vary from section to section and year to year. Special Topics in Social Science and Policy: Read More [+]

Fall and/or spring: 8 weeks - 4-8 hours of lecture per week 15 weeks - 2-4 hours of lecture per week

Additional Format: Two to four hours of lecture per week. Four to eight hours of lecture per week for 8 weeks.

Special Topics in Social Science and Policy: Read Less [-]

INFO 290T Special Topics in Technology 2 - 4 Units

Terms offered: Spring 2024, Fall 2023, Spring 2023 Specific topics, hours, and credit may vary from section to section and year to year. Special Topics in Technology: Read More [+]

Special Topics in Technology: Read Less [-]

INFO 291 Special Topics in Information 1 - 4 Units

Terms offered: Prior to 2007 Specific topics, hours, and credit may vary from section to section, year to year. Special Topics in Information: Read More [+]

Repeat rules: Course may be repeated for credit when topic changes.

Fall and/or spring: 15 weeks - 1-4 hours of lecture per week

Additional Format: One to four hours of lecture per week.

Grading: Offered for satisfactory/unsatisfactory grade only.

Instructor: Hoofnagle

INFO 293 Information Management Practicum 0.5 Units

Terms offered: Fall 2016, Summer 2016 10 Week Session, Spring 2016 This course is designed to help School of Information graduate students maximize their internship, practicum, or independent research experiences. Information Management Practicum: Read More [+]

Course Objectives: Experience the practical application of your academic knowledge to real-world professional contexts; Gain insight into an organization and how one might make a valuable contribution; Reflect on the information the experience has provided, to see if it fits within one’s personal value set and work/life manifestos. Try out various professional activities to see when you are in ‘flow’;

Student Learning Outcomes: Assess the organizational culture of a company, governmental body, or non-governmental organization Connect academic knowledge about information management to real-world professional contexts Evaluate the effectiveness of a variety of information science techniques when deployed in organizational situations Integrate the student's own individual professional goals with the organization's needs relevant to the internship or practicum Reflect critically on the internship or practicum experience

Prerequisites: Consent of a Head Graduate Adviser for the School of Information

Repeat rules: Course may be repeated for credit without restriction.

Fall and/or spring: 15 weeks - 1 hour of internship per week

Summer: 10 weeks - 1.5 hours of internship per week

Additional Format: One hour of internship per week. One and one-half hours of internship per week for 10 weeks.

Information Management Practicum: Read Less [-]

INFO 294 Doctoral Research and Theory Workshop 2 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 An intensive weekly discussion of current and ongoing research by Ph.D. students with a research interest in issues of information (social, legal, technical, theoretical, etc.). Our goal is to focus on critiquing research problems, theories, and methodologies from multiple perspectives so that we can produce high-quality, publishable work in the interdisciplinary area of information research. Circulated material may include dissertation chapters , qualifying papers, article drafts, and/or new project ideas. We want to have critical and productive discussion, but above all else we want to make our work better: more interesting, more accessible, more rigorous, more theoretically grounded, and more like the stuff we enjoy reading. Doctoral Research and Theory Workshop: Read More [+]

Prerequisites: PhD students only

Fall and/or spring: 15 weeks - 2 hours of workshop per week

Additional Format: Two hours of workshop per week.

Doctoral Research and Theory Workshop: Read Less [-]

INFO 295 Doctoral Colloquium 1 Unit

Terms offered: Fall 2024, Fall 2023, Spring 2023 Colloquia, discussion and readings designed to introduce students to the range of interests of the school. Doctoral Colloquium: Read More [+]

Prerequisites: Ph.D. standing in the School of Information

Fall and/or spring: 15 weeks - 1 hour of colloquium per week

Additional Format: One hour of colloquium per week.

Doctoral Colloquium: Read Less [-]

INFO 296A Seminar 2 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Topics in information management and systems and related fields. Specific topics vary from year to year. Seminar: Read More [+]

Prerequisites: Consent of instructor

Fall and/or spring: 15 weeks - 2-4 hours of seminar per week

Additional Format: Two to Four hours of Seminar per week for 15 weeks.

Seminar: Read Less [-]

INFO 298 Directed Group Study 1 - 4 Units

Terms offered: Fall 2019, Spring 2016, Fall 2015 Group projects on special topics in information management and systems. Directed Group Study: Read More [+]

Credit Restrictions: Students will receive no credit for INFO 298 after completing INFOSYS 298.

Fall and/or spring: 15 weeks - 1-4 hours of directed group study per week

Summer: 8 weeks - 1.5-7.5 hours of directed group study per week

Additional Format: One to four hours of directed group study per week. One and one-half to seven and one-half hours of directed group study per week for 8 weeks.

Directed Group Study: Read Less [-]

INFO 298A Directed Group Work on Final Project 1 - 4 Units

Terms offered: Spring 2022, Spring 2016, Spring 2015 The final project is designed to integrate the skills and concepts learned during the Information School Master's program and helps prepare students to compete in the job market. It provides experience in formulating and carrying out a sustained, coherent, and significant course of work resulting in a tangible work product; in project management, in presenting work in both written and oral form; and, when appropriate, in working in a multidisciplinary team. Projects may take the form of research papers or professionally-oriented applied work. Directed Group Work on Final Project: Read More [+]

Prerequisites: Consent of instructor. Course must be taken for a letter grade to fulfill degree requirements

Additional Format: One to four hours of directed group study per week.

Directed Group Work on Final Project: Read Less [-]

INFO 299 Individual Study 1 - 12 Units

Terms offered: Fall 2023, Summer 2016 8 Week Session, Spring 2016 Individual study of topics in information management and systems under faculty supervision. Individual Study: Read More [+]

Fall and/or spring: 15 weeks - 1-12 hours of independent study per week

Summer: 8 weeks - 2-22.5 hours of independent study per week

Additional Format: Format varies.

Individual Study: Read Less [-]

INFO 375 Teaching Assistance Practicum 2 Units

Terms offered: Spring 2024, Fall 2021, Fall 2020 Discussion, reading, preparation, and practical experience under faculty supervision in the teaching of specific topics within information management and systems. Does not count toward a degree. Teaching Assistance Practicum: Read More [+]

Fall and/or spring: 15 weeks - 2 hours of lecture per week

Additional Format: Two hours of lecture per week.

Subject/Course Level: Information/Professional course for teachers or prospective teachers

Instructor: Duguid

Teaching Assistance Practicum: Read Less [-]

INFO 602 Individual Study for Doctoral Students 1 - 5 Units

Terms offered: Spring 2016, Fall 2015, Spring 2015 Individual study in consultation with the major field adviser, intended to provide an opportunity for qualified students to prepare themselves for the various examinations required of candidates for the Ph.D. degree. Individual Study for Doctoral Students: Read More [+]

Fall and/or spring: 15 weeks - 1-5 hours of independent study per week

Additional Format: One to Five hour of Independent study per week for 15 weeks.

Subject/Course Level: Information/Graduate examination preparation

Individual Study for Doctoral Students: Read Less [-]

Contact Information

School of information.

102 South Hall

Phone: 510-642-1464

Senior Director of Student Affairs

Siu Yung Wong

[email protected]

Senior Director of Admissions

Julia Sprague

[email protected]

Print Options

When you print this page, you are actually printing everything within the tabs on the page you are on: this may include all the Related Courses and Faculty, in addition to the Requirements or Overview. If you just want to print information on specific tabs, you're better off downloading a PDF of the page, opening it, and then selecting the pages you really want to print.

The PDF will include all information unique to this page.

• Skip to right header navigation
• Skip to primary navigation
• Skip to secondary navigation
• Skip to main content
• Skip to primary sidebar

University of California, Berkeley Mechanical Engineering

• From the Chair
• Honors and Rankings
• Visitor Information
• Equity and Inclusion
• 150 Years of Women in ME
• Make a Gift
• Faculty by Research Area
• Faculty Books
• Administrative
• Information Technology
• Student Services
• Student Machine Shops
• Community Spotlight
• External Advisory Board
• Lecturer Positions
• Visiting Scholars
• Curriculum Flowchart
• Degree Requirements
• ME + Business
• ME/MSE Joint Major
• ME/NE Joint Major
• Aerospace Engineering Minor
• Fifth Year B.S./M.S. Program
• Simultaneous Degrees
• Semesterly Advising
• Faculty Adviser Assignments
• Faculty Office Hours
• Career Planning Maps
• Applying to Engineering
• The Application
• Junior Transfers
• Credit from Exams
• Drake Scholarship
• Financial Resources
• Tentative ME Course Schedule
• Technical Electives
• Design Course Transition
• Quantitative Science
• Humanities & Social Sciences Courses
• Undergrad Course Syllabi
• ME DeCal Courses
• Credit for Research
• Research Samples
• Past Prize Winners
• Student Life Resources
• Jobs and Internships
• Program Objectives and Outcomes (ABET)

Ph.D. & D.Eng.

• Master of Science
• Master of Engineering
• 5th Year Masters Program Handbook
• Special Programs
• M.S., Ph.D., D.Eng. & CWO Application
• MEng Application
• 5th Year Masters Admissions
• Application Tips
• Fees and Financial Support
• Readmission / Change of Major
• Graduate Handbook
• Graduate Forms
• Prelim Exams
• Research Areas and Major Fields
• Major Field Advisors
• Grant Writing
• Grad Division Resources
• COE Graduate Guide
• GSI/Reader Forms
• Tentative ME course schedule
• Graduate Course Syllabi
• Laboratories
• Research Centers
• Student Academic Resources
• Graduate Resources
• ME Student Groups
• Virtual Career Panel Series
• Room Reservations
• Safety Information
• Key Requests
• Sexual Violence & Sexual Harassment Prevention
• Mail & Office Administration
• Faculty & Staff Resources
• Financial Services
• IT Services
• Shop Training
• Services Provided
• Shop Equipment
• Shop Safety
• Alumni Newsletter
• Mechanical Engineering Seminars
• Search for: Search Button

The Doctor of Philosophy in Engineering can be done in conjunction with a Ph.D. (for the M.S./Ph.D. option) or alone. Degrees are granted after completion of programs of study that emphasize the application of the natural sciences to the analysis and solution of engineering problems. Advanced courses in mathematics, chemistry, physics, and the life sciences are normally included in a program that incorporates the engineering systems approach for analysis of problems. Students must have a bachelors degree in one of the accredited engineering curricula or satisfy the equivalent of a bachelors degree in engineering as determined by the department concerned for admission to this program.

For more information, please see Graduate Handbook 7.1 .  A printable version of the curriculum can be found here .

Though the vast majority of students in our program earn the Ph.D., the D.Eng can be earned in rare cases. Degrees are granted after completion of programs of study in professional engineering emphasizing technical, sociological, environmental, and economic problems involved in the design, construction, and operation of engineering structures, processes, and equipment. Studies include courses in the engineering sciences necessary to the engineering interpretation of the latest scientific developments, as well as courses in design, operation, humanities, and economics to provide bases for the analysis and solution of problems in professional engineering. Students must have a BS degree in one of the accredited engineering curricula or satisfy the equivalent of a BS degree in engineering as determined by the department concerned.

Ph.D. Program

Click  here for the Handbook for Graduate Study in English .  This document includes departmental policies and procedures concerned with graduate study.

The Berkeley English Department offers a wide-ranging Ph.D. program, engaging in all historical periods of British and American literature, Anglophone literature, and critical and cultural theory. The program aims to assure that students gain a broad knowledge of literature in English as well as the highly-developed skills in scholarship and criticism necessary to do solid and innovative work in their chosen specialized fields.

Please note that the department does not offer a Master’s Degree program or a degree program in Creative Writing. Students can, however, petition for an M.A. in English with an emphasis in Creative Writing upon completion of the Ph.D. course requirements (one of which must be a graduate writing workshop) and submission of a body of creative work.

Students interested in combining a Ph.D. in English with studies in another discipline may pursue Designated Emphases or Concurrent Degrees in a number of different fields

Normative time to complete the program is six years. The first two years are devoted to fulfilling the course and language requirements. The third year is spent preparing for and taking the Ph.D. oral qualifying examination. The fourth through sixth years are devoted to researching and writing the prospectus and dissertation.

The general goal of the first two years is to assure that the students have a broad and varied knowledge of the fields of British and American literature in their historical dimensions, and are also familiar with a wide range of literary forms, critical approaches, and scholarly methods. Students will complete twelve courses distributed as follows:

• 1) English 200, “Problems in the Study of Literature”
• 2) Medieval through 16 th -Century
• 3) 17 th - through 18 th -Century
• 4) 19 th -Century
• 5) 20 th -Century
• 6) a course organized in terms other than chronological coverage.
• 7-12) Elective courses.

(A thirteenth required course in pedagogy can be taken later.) Students who have done prior graduate course work may transfer up to three courses for credit toward the 12-course requirement. Up to five of the 12 courses may be taken in other departments.

Students must demonstrate either proficiency in two foreign languages or advanced knowledge in one foreign language before the qualifying examination. There are no "canonical languages" in the department. Rather, each specifies which languages are to count, how they relate to the student's intellectual interests, and on which level knowledge is to be demonstrated. "Proficiency" is understood as the ability to translate (with a dictionary) a passage of about 300 words into idiomatic English prose in ninety minutes. The proficiency requirement may also be satisfied by completing one upper-division or graduate literature course in a foreign language. The advanced knowledge requirement is satisfied by completing two or three literature courses in the language with a grade of "B" or better.

At the end of the second year each student’s record is reviewed in its entirety to determine whether or not he or she is able and ready to proceed to the qualifying exam and the more specialized phase of the program.

The Qualifying Examination

Students are expected to take the qualifying examination within one year after completing course and language requirements. The qualifying exam is oral and is conducted by a committee of five faculty members. The exam lasts approximately two hours and consists of three parts: two comprehensive historical fields and a third field which explores a topic in preparation for the dissertation. The exam is meant both as a culmination of course work and as a test of readiness for the dissertation.

The Prospectus and Dissertation

The prospectus consists of an essay and bibliography setting forth the nature of the research project, its relation to existing scholarship and criticism on the subject, and its anticipated value. Each candidate must have a prospectus conference with the members of their committee and the Graduate Chair to discuss the issues outlined in the proposal and to give final approval to the project. The prospectus should be approved within one or two semesters following the qualifying exam.

The dissertation is the culmination of the student's graduate career and is expected to be a substantial and original work of scholarship or criticism. Students within normative time complete the dissertation in their fourth through sixth years.

• Meet CED Alums
• Work at CED
• Undergraduate Majors + Minors
• Graduate Programs
• Concurrent Degrees
• Certificates
• Summer Programs
• Study Abroad
• Undergraduate Admissions
• Graduate Admissions
• Fees + Financial Aid
• CED Undergraduate Advising
• Graduate Advising
• Centers & Institutes
• Climate Solutions
• Equity + Social Justice
• Technology + Material Innovations
• Publications
• Research Collaborations
• Environmental Design Archives
• Student Work
• Student Organizations
• Student Support
• Building Safety
• Student Fees and Waivers
• Fabrication + Materials
• IT + Computing
• Environmental Design Library
• Facility Services
• Awards, Scholarships and Fellowships
• Careers & Work Opportunities
• Accreditation and Licensure
• Bachelor of Arts
• Minor in Environmental Design and Urbanism in Developing Countries
• Minor in the History of the Built Environment
• Minor in Social and Cultural Factors in Environmental Design
• Minor in Sustainable Design
• Master of Architecture (MArch)
• Master of Advanced Architectural Design (MAAD)
• Master of Science
• Concurrent Programs
• 2024 Summer Courses
• 2024 Fall Courses
• + About LAEP
• Minor in Landscape Architecture
• Master of Landscape Architecture
• + About DCRP
• Master of City Planning
• Bachelor of Arts in Urban Studies
• Faculty Work
• Studio Work
• + About IURD
• About MRED+D
• + BA in Sustainable Environmental Design
• For Students

Ph.D. in Architecture

• About Architecture
• Building Science, Technology, and Sustainability

History, Theory, and Society

The Ph.D. in architecture is a research degree appropriate for those seeking careers in teaching and scholarship in architecture and its related areas, or in roles in government or professional consultation that require depth in specialization and experience in research.

The Program

Berkeley’s Ph.D. program in architecture is interdisciplinary in outlook, reaching into the various disciplines related to architecture and incorporating substantial knowledge from outside fields. Students admitted to this program carry out a program of advanced study and research, both on the basis of formal class work and of individual investigation. Work centers on three related fields of study, the major field (the basis for the dissertation), and one-to-two minor fields, at least one of which must be from a discipline outside architecture.

Fields of Study

The Ph.D. degree emphasizes course work and supervised independent research in one of the following areas of study:

• Building Science, Technology and Sustainability (BSTS)
• History, Theory and Society (HTS)

Major fields outside these fields or combinations thereof may also be proposed at the time of admission.

Course work is individually developed through consultation with an academic adviser. Outside fields of study may take advantage of the University’s varied resources. Recent graduates have completed outside fields in anthropology, art history, business administration, city and regional planning, computer science, various engineering fields, psychology, women’s studies, geography and sociology.

The following are members of the Ph.D faculty, broken into one of two offered areas of study. Please also review the current list of all faculty in the Architecture Department for other faculty and specialities. A sampling of faculty research is described on the faculty research projects page.

Building Science, Technology and Sustainability

Requirements

The Ph.D. program in architecture is governed by the regulations of the University Graduate Division and administered by the departmental Ph.D. committee. Specific degree requirements include:

• A minimum of two years in residence.
• Completion of a one-semester course in research methods.
• Satisfaction of a foreign language requirement for those in the History, Theory and Society.
• Completion of one-to-two outside fields of study.
• A written qualifying examination, followed by an oral qualifying examination.
• A dissertation.

Course requirements for the degree include:

BSTS Master of Science and PhD Handbook for 2023-2024 and 2024-2025

For previous years' handbooks, please contact graduate advising .

Ph.D. Alumni List

• Ph.D. Alumni — Building Science, Technology and Sustainability
• Ph.D. Alumni — History, Theory, and Society

• Current Students

Infectious Diseases and Immunity PhD

The IDI is a laboratory-based research program where students study infectious diseases and immunology through a public health lens.

The IDI Program intends to create opportunities for students to gain new and advanced knowledge about infectious disease agents and how they interact with host cells, human populations, and the environment. Our goal is to improve public health by increasing our understanding of infectious diseases and human immunology through basic and translational research that contributes to developing new diagnostics, treatments, and methods to prevent or control diseases. Because the IDI Graduate Group is administratively managed by the School of Public Health, we follow the School of Public Health application process and deadlines . GRE test scores are not required for the Infectious Diseases and Immunology PhD program.

Program Objectives

The objective of this program is to provide students with research-oriented training that will enable them to design and implement independent investigations and advance the fundamental knowledge of infectious disease agents and their interactions with the human host and the environment. The IDI PhD program is a five to six-year program. Students in the program are fully funded throughout their time in the program, and are provided with a competitive monthly stipend.

We strive to promote health by integrating basic research and applied technologies to develop new approaches for the diagnosis, treatment, prevention, and control of infectious disease in humans. This program combines clinical, epidemiological, and basic laboratory research strategies in order to apply these methods to specific infectious disease problems affecting human populations. In addition, students have opportunities to interact with faculty members from multidisciplinary centers involved in global infectious disease research. These include the Center for Global Public Health and the Center for Emerging and Neglected Diseases. Students matriculating through this program will acquire expertise in not only fundamental infectious disease research, but also learn how their research relates to other disciplines. Students matriculating through this degree program will acquire expertise in fundamental infectious diseases research for which there is demand from academic institutions, local and national government agencies, and biotechnology companies.

Our Commitment to Belonging

The graduate students and faculty of the Infectious Disease and Immunity Program stand with Black Lives Matter. We believe that racism and police violence is a public health crisis that deserves the same amount of attention and work as any other disease.

As students of Public Health and Biology, we understand the ways in which our field and institutions have been complicit and contributed to the harm perpetuated against Black bodies. The popularized pseudoscience of phrenology, the grotesque distortion of evolution into eugenics, and the irreparable harm of the Tuskegee Syphilis study are just a few examples of how science and public health have perpetuated white supremacy in both mind and body.

We are dedicated to creating an academic environment free of anti-Blackness and making our program and community places where Black, Indigenous, and other URM students can thrive. This is a lofty goal and we recognize that it will take more than a passing attempt to root out anti-Blackness from the academy and ourselves. We pledge to make this effort a sustained one, creating accountability by updating publicly the progress we have made on our goals and setting new goals year after year.

• DEI Resources on campus Includes campus initiatives, affinity groups, STEM outreach in Bay Area
• Grants/fellowships and funding A consolidated list of of funding and fellowship opportunities

Program Snapshot

• First year: Coursework and three lab rotations
• End of first year: Join dissertation research lab
• Second year: Complete coursework requirement, GSI for a semester and take qualifying examination (QE)
• Third year: Advance to candidacy after passing the QE
• 3rd-5th/6th years: Hold dissertation committee meetings at least once a year, complete the annual Doctoral Progress review, complete dissertation research, complete GSI one additional time. Present dissertation at IDI Monday Seminar. Notify program their graduating semester. File dissertation.

IDI students spend their first year taking an interdisciplinary set of classes and completing three lab rotations. The curriculum aims to introduce students to the breadth of infectious disease and immunology research while sharpening statistical skills and building a strong foundation of basic science knowledge. These courses are supplemented by a small faculty led seminar in the first and second years that focuses on developing and improving specific skills like grant writing, qualifying exam preparation and critical research paper analyses.

For additional details on program requirements, see curriculum information and resources for current students page.

Qualifications

The following subjects are normally required as undergraduate preparation for all candidates. Deficiencies must be made up early during the graduate program.

• Mathematics: Calculus; one course in probability or statistics.
• Physics: General physics.
• Chemistry and biochemistry: Inorganic chemistry; organic chemistry; biochemistry; and associated laboratories.
• Biology: General biology lecture and laboratory; genetics; and a basic course(s) in molecular biology.
• Common undergraduate majors for admitted applicants: Biology, integrative biology, microbiology, biological sciences, biology and communications
• Common work experience for admitted applicants: Work experience is not required for admission, but relevant work experience related to infectious diseases, e.g. wet laboratory and/or surveillance work is important for the IDI PhD program. Most of the admitted applicants have strong lab and research experience.

GRE scores are not required, it is optional for the fall 2023 admissions cycle. We recommend submitting a GRE if you have no other evidence of quantitative, verbal, or analytical abilities in your application.

Recruitment Information

The Infectious Diseases and Immunity PhD program admits students only in the Fall semester. Applications are available for submission online in mid-September. The deadline for applications is December 4, 2023, for Fall 2024 admission. Late applications are not accepted. Students should complete their applications as early as possible and be sure to hit the submit button before the deadline. Admission review will be conducted in mid January 2024. Shortlisted applicants will be invited to attend the required interview on Feb 9, 2024.

IDI Graduate Group faculty come from multiple departments including Infectious Diseases and Vaccinology, Plant and Microbial Biology, and Molecular and Cell Biology. In addition, a unique aspect of the IDI program is our affiliation with UCSF faculty who conduct work in the area of global infectious disease. IDI students may choose to join any IDI affiliated lab for their thesis research.

UCSF Affiliated Faculty

Below is a list of UCSF professors who are currently affiliated with IDI but who are only a small selection of potential UCSF mentors.

Margaret Feeney, MD

Bryan Greenhouse, MD, MA

Phil Rosenthal, MD

Rachel Rutishauser, MD, PhD

Current IDI PhD Students

Student Name – Faculty Advisor; Lab

• Cuong Joseph Tran – Dr. Matthew Welch ; The Welch Lab
• Joanna Vinden – Dr. Bryan Greenhouse ; Greenhouse Lab (UCSF)
• Marcus Wong – Dr. Eva Harris ; The Harris Research Program
• Eric Jedel – Dr. Suzanne Fleiszig ; Fleiszig – Evans Lab
• Kishen Patel – Dr. Kim Seed ; Seed Lab
• Reinaldo Mercado-Hernandez – Dr. Eva Harris ; The Harris Research Program
• Elias Michael Duarte – Dr. Eva Harris ; The Harris Research Program
• Abigail Kane – Dr. Rachel Rutishauser ; Rutishauser Lab, UCSF
• Claire Mastrangelo – Dr. Lee Riley ; Riley Lab
• Carolina Agudelo – Dr. Ashley R. Wolf, co-mentor Dr. Sarah Stanley ; Stanley Lab
• Isabel Lamb-Echegaray – Dr. Sarah Stanley ; Stanley Lab
• Jaime Cardona Ospina – Dr. Eva Harris ; The Harris Research Program
• Scott Espich
• Felix Pahmeier – Dr. Eva Harris ; The Harris Research Program
• Marize Rizkalla
• Zahra Zubair-Nizami

Go to “Curriculum Information and Resources for Current IDI Students” page

We would like to thank the following funding donors for their generous fellowship support to support our students and our work:

• Hillel and Rose Levine Fellowship
• The Albert and Mildred Krueger Memorial Fellowship
• Sally Anne Bradley Presser and Steven A. Presser Fellowships

Prospective donors: please visit give.berkeley.edu .

Steps to a PhD

Some of our graduates at spring 2023 commencement,along with professors Friedrich Sommer, Dan Feldman, Michael Silver, Joni Wallis, Marla Feller, and Ehud Isacoff. Photo by  GradImages (link is external) .

Neuroscience is a broad field that requires multidisciplinary training as well as intensive study of specific concepts and techniques related to each student’s primary research focus. The Neuroscience PhD Program is designed to provide highly individualized, flexible training that fulfills both these needs. Our PhD training program has a standard completion time of 5.5 to 6 years.  The program is PhD-granting only, there is no master’s degree program.  The following is a general overview of the steps to a Neuroscience PhD at UC Berkeley. For detailed policies, see  Resources For Current Students

First-year students begin the program with an intensive, 10-day “Neuro Camp” course held just prior to the official start of fall semester classes. The course features lectures on key neuroscience concepts and on classical and emerging experimental techniques and research seminars by Berkeley Neuroscience faculty. In addition, hands-on research projects in faculty laboratories cover techniques ranging from molecular neuroscience to neurophysiology and optogenetics to fMRI. The goal is to provide an immersive introduction to multiple disciplines and experimental approaches within neuroscience. Our Neuro Camp unites neuroscience-oriented students from multiple PhD programs.

Laboratory Rotations

During Year 1, each student spends three 10-week periods performing research projects in different faculty laboratories. The choice of laboratories is based on student preference. The goal is to expose students to different techniques and approaches in neuroscience and to provide training in experimental design, critical analysis of data, and presentation of research findings. Performance in rotations is evaluated and graded. Rotations also allow students to identify the laboratory in which their thesis research will be performed. Students formally present results from the laboratory rotations in a dedicated course designed to instruct students in clear, effective presentation of scientific findings.

The program has highly flexible course requirements. These are designed to provide students with sufficiently broad training in all areas of neuroscience, while allowing focus in the area of primary research interest.

During the first two years of the program, each student is required to take one course in each of three broad areas: (A) Cellular, Molecular & Developmental Neuroscience; (B) Systems and Computational Neuroscience; and (C) Cognitive and Behavioral Neuroscience. Each student consults with faculty advisers to determine the most appropriate individual courses within these areas.

Students must also complete a one-semester course in Applied Statistics in Neuroscience, or an equivalent approved course in statistics or quantitative analysis methods, as well as one elective course. 

For additional details, see the Neuroscience Course Curriculum.

Training in Teaching 

Effective teaching is a critical skill required in most academic and research careers. Students are required to serve as Graduate Student Instructors (GSIs; also knows as Teaching Assistants) for two semesters. GSI teaching occurs during Years 2 and 3 and provides supervised teaching experience in laboratory and discussion settings. Teaching is evaluated, and outstanding teaching is rewarded with annual Outstanding Graduate Student Instructor Awards. One to three of our students typically win this award each year.

Qualifying Examination

Students complete an Oral Qualifying Exam during the spring semester of Year 2. This exam is structured around a written thesis proposal and oral examination on this proposal, related research areas, and foundational questions in neuroscience. During the exam, a faculty committee tests the student’s knowledge of these areas and general neuroscience. Students must demonstrate the ability to recognize important research problems, propose relevant experimental approaches, and display comprehensive knowledge of relevant subjects. Students must pass the qualifying examination before advancing to doctoral candidacy.

Thesis Research

Thesis research begins after the completion of rotations in spring or summer of Year 1. During Year 2, students conduct thesis research while completing required coursework and GSI teaching. Years 3 to 5 are spent primarily on thesis research. Progress on thesis research is evaluated by the student, the thesis advisor, and a Thesis Committee of three additional faculty members. Thesis research is expected to lead to publication in top-ranked, refereed scientific journals. Students are strongly encouraged to present posters and speak at scientific meetings and conferences. During Year 4, they make a formal presentation of their research progress to their peers. Completion of thesis research is determined by the Thesis Committee. While there is no formal thesis defense, students present a formal thesis seminar to the neuroscience community in their last semester of candidacy.

Other Program Activities

During training, students are expected to participate in a range of activities to increase their exposure to neuroscience research within and outside their specialty areas. These include the annual Neuroscience Conference, the Neuroscience Seminar Series, as well as other affiliated seminar series and lectures. Students also participate in journal clubs, lab meetings, and multi-laboratory special interest group meetings focused on specific scientific topics. See Program Activities for a comprehensive list.

News & Events

Phd student emily ruppel has a a new article in social science & medicine, titled "therapeutic management in the low-wage workplace".

Please click the link below!

Here's the link:  https://www. sciencedirect.com/science/ article/pii/S0277953624004775 .

Congratulations Emily!

Congratulations to the 2024 Jengyee Prize Winner

We are delighted to announce that Emily Meng is this year’s Jengyee Prize – Leadership for a Better World winner. Established in 2010, the Jengyee Prize recognizes undergraduate students who demonstrate outstanding leadership, service, and commitment to advancing a better world. Emily exemplifies these qualities through her outstanding leadership and dedication for improving our community, society, and culture.

About this year’s winner:

Emily Meng’s journey in leadership and volunteering is driven by her love for science, puzzles, and helping others. In high school, she engaged in various engineering projects, such as designing equipment for healthcare workers and 3D-printing tennis shock absorbers. These experiences highlighted her potential to make a difference and ignited her passion for teaching.

At Berkeley, Emily serves as an ambassador for the Santa Clara Valley Society of Women Engineers, where she leads engineering workshops for elementary school girls. Her efforts have cultivated a supportive environment, resulting in several of her female students taking first place at robotics competitions. Emily also volunteers at UCSF Benioff Children’s Hospital, teaching art and science activities to patients and their families, which has deepened her sense of empathy, compassion, and advocacy.

As a student-teacher for CREATE, Emily runs an after-school program at Berkely Maynard Academy in Oakland, integrating social and emotional learning activities to help her students develop goal-setting, empathy, and gratitude skills. Additionally, she leads accessible introductory swimming lessons for children with physical disabilities through SNAPkids. By using her engineering skills, she designed 3D equipment to assist her students’ motor skills and provided them a sense of freedom in and outside of the water.

Emily Meng hopes to combine her passions for engineering and mentoring to inspire children to pursue careers in STEM. Congratulations, Emily, for being this year’s Jengyee Prize winner!

About the Award

The Jengyee prize is given in memory of Jengyee Liang, who earned a B.S. in 2005 from the Industrial Engineering and Operations Research (IEOR) Department in the College of Engineering, and who tragically passed away on November 10, 2008. Jengyee is remembered for her leadership, kindness, and commitment to building a better world.

Learn more about Jengyee Liang

Jump to navigation

• Course Catalog
• Class Schedule
• Undergraduate

2024 Fall PHDBA 259A 001 SEM 001

D-Lab's workshops and consulting services are paused for the summer.  Our core staff will be focusing on special projects and other endeavors. We look forward to seeing you in the fall and hope you have a great summer.

Minding the Gaps: Pay Equity in California

Pay equity between women and men has improved in the United States from about 60 percent in 1960 to 82 percent in 2018 ( Semega et al. 2021 , see Figure 5). California is among the states with the smallest pay gaps and outpaces the national number at 13 percent ( Wisniewski 2022 ). California is unique in that it enacted legislation aimed at eliminating pay gaps by sex and race categories.  For example, the California Fair Pay Act of 2015 ( Senate Bill 358 ), effective January 1, 2016, shrinks loopholes employers used to justify unequal wages. It does so by changing the definition of equal work to “substantially similar work” and specifying that people doing similar work at different establishments also must be paid equally. Beyond the gender pay gap, California also amended the Equal Pay Act (1949) with the Fair Pay Act in 2016 ( SB 1063  and  AB 1676 , effective January 1, 2017) and 2017 ( AB 168  and  AB 46 , effective January 1, 2018) to prohibit unequal pay for employees of different race or ethnicities, prohibit employers from asking for a potential employee’s salary history, and expand coverage to include public and private employees, respectively. Given that legislation assumes potential differences in pay for similar work by establishment necessitates controlling for establishment, Firm size is also a potentially important and untested control variable in gender pay gap studies (see  Blau and Kahn 2017 ).

The California Commission on the Status of Women and Girls (CCSWG) led the  Pay Equity Task Force  to implement SB 358 and create guidance about the law for  employees ,  employers , and  unions . I served as CCSWG staff senior research consultant on the task force where I, among other things, organized task force subcommittees by performing a content analysis of web documents advising readers how to address pay equity in the workplace.

For a social statistics course I teach, we use the U.S. Census American Community Survey (ACS) to analyze the gender pay gap in California. Students deploy the ACS 2014-2018 five-year file to learn about how sex and race categories operate via the institutional pay and occupation structure. The five-year file consists of a weighted sample of 12,581,405 full-time, year-round working people. They test variables indicating three theoretical frameworks, 1) human capital, 2) occupational segregation, and 3) discrimination. While human capital refers to indicators such as education and experience, the latter two are indicated by occupation and demographic characteristics such as race, ethnicity, and gender. Variables sometimes indicate more than one theoretical framework.

At the beginning of term, students learn that less than half of full-time California workers are women (40.7%) who outpace men in each income category except those paying over $100,000 annually, though the two groups appear to break even in the income category just below this, a glass income ceiling of sorts. Women working full-time, year-round in California are overrepresented in the lowest income categories, and men are overrepresented in the highest income categories. The California gender pay gap in this dataset, including outliers, is about 21%.

By midterm, they learn occupation alone is weakly related to gender categories, education category alone is strongly related to income category, and that, once age is controlled for, education remains a strong predictor of income while age is moderately so. Knowing someone’s gender does little to reduce errors in the prediction of occupation category (Lambda = .033), while education category on the other hand is a strong predictor of income category (Gamma = .535). In a multivariate analysis of income as a continuous measure, knowing someone’s age reduces error about 21% (R = .211) of the time and knowing someone’s education level does so about 32% (R = .321) of the time.

Two interesting side points emerge. First, the modal occupation of full-time year-round California workers is a manager once managers and supervisors are recoded into a single group across occupation categories. Second, older workers in these cross-sectional analyses tend to have lower education levels; younger workers are more educated.

In the final analysis, students perform a regression analysis of gender and race categories in addition to continuous age and education (i.e., an ordinal measure treated as interval/ratio) measures to better understand how each variable impacts income.

The scenarios they analyze compare a 25-year-old, white, male with 12 years of education to a 40-year-old, nonwhite, female with 16 years of education. Plugging in the model coefficients yields $18,510 and $34,481, respectively. For the more engaged students, it is eye-opening that being a man indicates someone earns almost $22,000 more a year on average and that being white does so to the tune of about $9,000 a year. Education remains the strongest predictor of income with a standardized coefficient of .337. Significance levels also indicate we can generalize these sample findings to the population. Note: outliers are not removed and income is self-reported. Additional procedures should be done to refine the model.

While the work students do is rigorous using a representative sample of full-time year-round California workers, there remains work to be done. Not only is there a need for firm-level analysis, as pointed out in the introduction to this blog piece, but the shifts in law offer a good starting point for a longitudinal or cross-sectional time-series analysis. The shifts in California law allow a unique opportunity for a natural experiment. Testing the statistical significance of observed associations before and after a California law change will allow us to generalize results should other states follow suit and for researchers interested in how changes in law impact outcomes. That California’s entire working population was exposed to these changes at the same time creates large organic control and treatment groups and further increases the likelihood of being able to generalize results. And, comparing California's pre- and post-changes in law to states in the continental United States will control for larger trends that might otherwise go unmeasured.

Further, local California county women’s commissions are interested in knowing how their counties are doing. County-level analyses may pinpoint what can be done at the local level to eliminate pay gaps favoring one demographic category over others. However, publicly available data yield large margins of error as demographic variables are added to a model, making those data less generalizable when doing a county-level analysis. Restricted-use disclosure avoidance rules also prohibit providing detailed statistical output by county or otherwise— limiting an understanding of the role of local efforts in closing pay gaps unless robust authoritative individual-level data can be accessed and analyzed.

Semega, Jessica, Melissa Kollar, John Creamer, and Abinash Mohanty. 2021 (updated). Income and Poverty in the United States: 2018. U.S Census Bureau.  https://www.census.gov/content/dam/Census/library/publications/2019/demo/p60-266.pdf  Accessed on November 18, 2022.

Wisniewski, Megan. 2022. In Puerto Rico no gap in median earnings between men and women. U.S. Census Bureau.  https://www.census.gov/library/stories/2022/03/what-is-the-gender-wage-gap-in-your-state.html  Accessed November 18, 2022.

Blau, Francine D. and Lawrence Kahn. 2017. “The Gender Wage Gap: Extent Trends and Explanations.” Journal of Economic Literature v.55. Accessed on August 9, 2023.  https://www.nber.org/system/files/working_papers/w21913/w21913.pdf

Tonya D. Lindsey, Ph.D.