berkeley math phd application

Dissertation Filing Requirements

The final requirement in the Math Department’s PhD program is filing the dissertation. The dissertation committee has the responsibility for determining whether a submitted dissertation draft is acceptable for the PhD. It is the responsibility of the student to keep in touch with all members of the committee and communicate their expected timeline to completion with adequate time prior to the filing date for all members of the committee to read and provide comments on the dissertation draft. It is also the responsibility of the student to stay informed about all deadlines associated with dissertation filing. 

There are several steps, outlined below, that must be completed by the student during the term in which they plan to file. PhD candidates should review the checklist carefully and in full no later than the start of the semester in which they intend to file, and follow up with the GSAO with any unanswered questions. Please note that the checklist items detailed below are for Math and Applied Math PhD candidates; Logic students will find a separate checklist linked below under “Dissertation Filing Resources”.

Steps to Filing Your Dissertation (Math & Applied Math)

• Review Graduate Division dissertation filing protocol (beginning of term): Review the guidelines  here . Please read through carefully.
• Check Dissertation Committee on CalCentral (by beginning of term): Ensure that all members listed are correct and show as “active”. If you have a committee member outside of the university, double-check that their email address is correct as well, as the final signature eForm will be routed to this email address for their approval. If your committee is not up to date, you can change it at any time by going to CalCentral > My Dashboard > Submit a Form > Higher Degrees Committee eForm > Committee type “Doctoral”.
• Update Expected Graduation Term to current term (by beginning of term): Expected Graduation Term (EGT) must be set to the term in which you plan to graduate in order for you to be added to the campus degree list. Students can update their EGT by going to CalCentral > My Dashboard > Submit a Form > Change of Expected Graduation Term Request.
• Check Academic Progress Report for incomplete milestones (by beginning of term): The Academic Progress Report (APR) shows all milestones required to complete a degree. Check your APR to ensure that only the dissertation filing milestone is incomplete. If any other milestones show as incomplete, please contact your GSAO. You can view your APR at CalCentral > My Academics > Degree Progress > View Academic Progress Report.
• Inform your committee members of your intent to file (by beginning of term): Check in with all of your committee members (not just your advisor!) and make sure they are aware of your planned timeline to completion. Ensure that all members will be available to read and approve your thesis by the filing date.
• Submit Research Presentation Form to GSAO (at least three weeks prior to filing): Students must give a research presentation, at least 50 minutes in duration, after advancement to candidacy and during a seminar offered by the Math Department; research presentations held at SLMath (formerly MSRI) or LBNL are also acceptable. After your presentation, please download the form template (found below), to be signed by the faculty in whose seminar you presented, and then return it via email to the GSAO once completed.
• Review CalCentral incomplete tasks and complete any outstanding forms or surveys (at least three weeks prior to filing): A list of incomplete tasks can be found on your CalCentral dashboard.
• Submit your final thesis to ProQuest (by the  semester filing deadline ): Upload a PDF of your dissertation to ProQuest as outlined in the procedure for filing your dissertation on Graduate Division's  website . You must review guidelines for submission carefully! Graduate Division has very specific formatting guidelines. You cannot make edits to your dissertation after it has been accepted.
• Submit the Final Signature eForm (by the  semester filing deadline ): The final signature page is now an eForm accessible via CalCentral.  If your EGT is correct, the eForm will show as an incomplete task on your dashboard. Inform your committee prior to submitting the form for their approval so that they can look out for the email notification. Make sure to follow up with all committee members before the filing deadline to ensure that everyone has signed. You will also use this form to select an embargo period for your manuscript, if applicable.
• Complete the AMS Survey of Doctoral Recipients: The Survey of Doctoral Recipients is now housed on AMS' website and completed online. We will email the survey to all graduating students.

Once you have completed all steps of the filing process (including surveys) and have a satisfied APR your certification of conferral will be emailed to you within 2-3 business days.

Dissertation Filing Resources

Research Presentation Form

Current AMS Survey of Doctoral Recipients

Graduate Division Filing Guidelines

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Graduate students in Applied Econometrics, taught by Associate Professor Michael Anderson. Photo: Jim Block

We welcome your application for our Ph.D. program for the FALL term.

Your  online application must be received by the deadline posted on the main Graduate Admissions website .  Your application must be complete to be reviewed for admission, including all supplemental materials, such as transcripts, letters of recommendation, and official test scores.   PLEASE NOTE DEADLINE DATE CUTOFF TIME. ALL APPLICATIONS MUST BE SUBMITTED BY WEDNESDAY DECEMBER 1st, 2023, by 8:59PM  *PACIFIC STANDARD TIME*. The online application site will automatically lockdown at 9:00PM, 12/01/21.

NOTE: We cannot recommend students for fellowships until their applications are complete (including receipt of test scores).

We do not admit students for the Spring term or for a terminal Master's degree.

Please contact ARE's Graduate Student Advisor Carmen Karahalios with questions regarding your application, [email protected] .

Criteria for Graduate Student Admission

Due to the limited number of students we are allowed to recommend for admission, we are not able to recommend admission for all of the applicants who meet all of our qualifications. These guidelines are designed to help potential students strengthen their applications. For example, students who lack strong training in economics or mathematics may want to take additional courses before applying.

Every year, a committee of faculty members and graduate students considers potential applicants for admission to the graduate program in Agricultural and Resource Economics. Since the composition of this committee changes frequently and the pool of applicants changes each year, we do not use rigid rules in determining admissions. Nonetheless, the following guidelines are used as a starting point in our deliberations.

Entire Record

Our objective is to admit an excellent group of individuals who will do well in our program and profession. We undertake a holistic admissions review that takes into account an applicant’s full record. We consider personal circumstances and emphasize the importance of diversity. We want to increase the number of students from groups that have been underrepresented in higher education.  

Formal Records

Formal records are relied upon heavily. We will not consider a student with a grade point average (GPA) below 3.0, which is the University's minimum standard for graduate admission. Typically, we consider the overall GPA, the economics GPA, and the GPA in mathematics, statistics, and other quantitative methods, with particularly heavy weight placed on the economics and mathematics grades.

Many of our applicants have a Master's degree in agricultural economics or economics from other institutions. When grades in graduate courses are available, we put greater weight on those than on undergraduate grades.

Standardized Tests

We encourage applicants to provide as much relevant information as possible. If possible, we recommend that you submit your Graduate Record Examination (GRE) scores. We place relatively more weight on the quantitative score, but we consider the others as well. We understand that due to the coronavirus, taking a GRE may be difficult or impossible. Thus, submitting GRE scores is optional, and you will not be penalized if you do not include them.

Personal Statement

We place substantial weight on the applicant’s essay. We are looking for students who have clear and reasonable objectives. We want to know why the applicant wants to study agricultural and resource economics at Berkeley and how the applicant will use this training after graduation.

Letters of Recommendation

Three letters of recommendation are given considerable attention. We put particular weight on letters from people who have previously recommended students who have done well in our program.

Papers and Publications

Preparing graduate students for a research career is our primary objective. Therefore, we place substantial weight on evidence of research potential. We particularly value sole-authored research such as undergraduate honors papers and masters degree theses. We also heavily weight both sole- and joint-authored papers, especially those that are published or under review.

Coursework and Employment Experience

We only admit students who have prior training in economics and quantitative methods. At the very minimum, a student must have taken intermediate level micro-economics and macro-economics courses and a year of calculus. Successful applicants have usually taken courses in linear algebra, statistics, econometrics and a significant number of undergraduate economics courses at the intermediate and upper level. Students need to have completed a course in real and functional analysis equivalent to Berkeley’s Math 104 before being allowed to take the graduate microeconomics theory sequence at Berkeley. We highly recommend taking such a course before arriving at Berkeley. If you have taken a real analysis course in another university, please upload the syllabus with your application. We may make exceptions for students who do not meet these guidelines if they have other characteristics or experiences that we believe are important, such as professional experience or graduate training in fields relevant to the core research interests of the department, yet academic preparation is an excellent predictor of success in our graduate program.

International Students

We encourage applications from other countries. In previous years, international students have comprised at least one-quarter of the entering class each fall.

International students are required to show competency in English, with a minimum Test of English as a Foreign Language (TOEFL) score of 90 for the internet-based test, 230 in the computer-based test, or 570 for the paper test.

In evaluating an international student's background, we are sometimes unfamiliar with the grading system used, the content of courses, etc. We recommend that foreign students provide an official explanation of the grading system and explain in some detail the content of the courses they have taken.

Preparing for Graduate Admissions

An applicant should hold a degree comparable to a Bachelor's degree at the University of California and must have demonstrated strong scholarship potential. The degree need not be in agricultural or resource economics.

Preparation in Math and Statistics (Equivalent Berkeley course in parentheses)

• Calculus (at Berkeley, Math 1A/B)
• Multivariate Calculus (Math 53)
• Introduction to Linear Algebra and Differential Equations (Math 54)
• An Introduction to Statistics (Stat 131)
• Real and Functional Analysis (Math 104)

Preparation in Economics

• Principles (ECON 1 or EEP 1)
• Intermediate Micro (Econ 100A or EEP 100)
• Intermediate Macro (Econ 100B)
• Recommended: Upper division courses such as public finance, labor, trade, industrial organization and environmental economics

GRE and TOEFL

Institutional code: 4833 Department code for the GRE: 0101 TOEFL code: 31

The GRE scores are part of a standard application. In case of hardship, we will consider applications without GRE scores that are accompanied by an explanation for the missing scores.

All applicants from countries in which the official language is not English are required to take the TOEFL. Please see the Graduate Application forms for more complete information regarding the GRE and TOEFL, and qualification for exemption of the TOEFL test.

International students are required to show competency in English, with a minimum TOEFL score of 90 for the internet-based test, 230 in the computer-based test, or 570 for the paper test.

It is very important that potential applicants arrange to take the GRE and TOEFL examinations early. We recommend that you take them by October or November. Official scores from the December tests may not reach us in time.

Application Management

PhD Program information

The Statistics PhD program is rigorous, yet welcoming to students with interdisciplinary interests and different levels of preparation. Students in the PhD program take core courses on the theory and application of probability and statistics during their first year. The second year typically includes additional course work and a transition to research leading to a dissertation. PhD thesis topics are diverse and varied, reflecting the scope of faculty research interests. Many students are involved in interdisciplinary research. Students may also have the option to pursue a designated emphasis (DE) which is an interdisciplinary specialization:  Designated Emphasis in Computational and Genomic Biology ,  Designated Emphasis in Computational Precision Health ,  Designated Emphasis in Computational and Data Science and Engineering . The program requires four semesters of residence.

Normal progress entails:

Year 1 . Perform satisfactorily in preliminary coursework. In the summer, students are required to embark on a short-term research project, internship, graduate student instructorship, reading course, or on another research activity. Years 2-3 . Continue coursework. Find a thesis advisor and an area for the oral qualifying exam. Formally choose a chair for qualifying exam committee, who will also serve as faculty mentor separate from the thesis advisor.  Pass the oral qualifying exam and advance to candidacy by the end of Year 3. Present research at BSTARS each year. Years 4-5 . Finish the thesis and give a lecture based on it in a department seminar.

Program Requirements

• Qualifying Exam

Course work and evaluation

Preliminary stage: the first year.

Effective Fall 2019, students are expected to take four semester-long courses for a letter grade during their first year which should be selected from the core first-year PhD courses offered in the department: Probability (204/205A, 205B,), Theoretical Statistics (210A, 210B), and Applied Statistics (215A, 215B). These requirements can be altered by a member of the PhD Program Committee (in consultation with the faculty mentor and by submitting a graduate student petition ) in the following cases:

• Students primarily focused on probability will be allowed to substitute one semester of the four required semester-long courses with an appropriate course from outside the department.
• Students may request to postpone one semester of the core PhD courses and complete it in the second year, in which case they must take a relevant graduate course in their first year in its place. In all cases, students must complete the first year requirements in their second year as well as maintain the overall expectations of second year coursework, described below. Some examples in which such a request might be approved are described in the course guidance below.
• Students arriving with advanced standing, having completed equivalent coursework at another institution prior to joining the program, may be allowed to take other relevant graduate courses at UC Berkeley to satisfy some or all of the first year requirements

Requirements on course work beyond the first year

Students entering the program before 2022 are required to take five additional graduate courses beyond the four required in the first year, resulting in a total of nine graduate courses required for completion of their PhD. In their second year, students are required to take three graduate courses, at least two of them from the department offerings, and in their third year, they are required to take at least two graduate courses. Students are allowed to change the timing of these five courses with approval of their faculty mentor. Of the nine required graduate courses, students are required to take for credit a total of 24 semester hours of courses offered by the Statistics department numbered 204-272 inclusive. The Head Graduate Advisor (in consultation with the faculty mentor and after submission of a graduate student petition) may consent to substitute courses at a comparable level in other disciplines for some of these departmental graduate courses. In addition, the HGA may waive part of this unit requirement.

Starting with the cohort entering in the 2022-23 academic year , students are required to take at least three additional graduate courses beyond the four required in the first year, resulting in a total of seven graduate courses required for completion of their PhD. Of the seven required graduate courses, five of these courses must be from courses offered by the Statistics department and numbered 204-272, inclusive. With these reduced requirements, there is an expectation of very few waivers from the HGA. We emphasize that these are minimum requirements, and we expect that students will take additional classes of interest, for example on a S/U basis, to further their breadth of knowledge. 

For courses to count toward the coursework requirements students must receive at least a B+ in the course (courses taken S/U do not count, except for STAT 272 which is only offered S/U).  Courses that are research credits, directed study, reading groups, or departmental seminars do not satisfy coursework requirements (for courses offered by the Statistics department the course should be numbered 204-272 to satisfy the requirements). Upper-division undergraduate courses in other departments can be counted toward course requirements with the permission of the Head Graduate Advisor. This will normally only be approved if the courses provide necessary breadth in an application area relevant to the student’s thesis research.

First year course work: For the purposes of satisfactory progression in the first year, grades in the core PhD courses are evaluated as: A+: Excellent performance in PhD program A: Good performance in PhD program A-: Satisfactory performance B+: Performance marginal, needs improvement B: Unsatisfactory performance First year and beyond: At the end of each year, students must meet with his or her faculty mentor to review their progress and assess whether the student is meeting expected milestones. The result of this meeting should be the completion of the student’s annual review form, signed by the mentor ( available here ). If the student has a thesis advisor, the thesis advisor must also sign the annual review form.

Guidance on choosing course work

Choice of courses in the first year: Students enrolling in the fall of 2019 or later are required to take four semesters of the core PhD courses, at least three of which must be taken in their first year. Students have two options for how to schedule their four core courses:

• Option 1 -- Complete Four Core Courses in 1st year: In this option, students would take four core courses in the first year, usually finishing the complete sequence of two of the three sequences.  Students following this option who are primarily interested in statistics would normally take the 210A,B sequence (Theoretical Statistics) and then one of the 205A,B sequence (Probability) or the 215A,B sequence (Applied Statistics), based on their interests, though students are allowed to mix and match, where feasible. Students who opt for taking the full 210AB sequence in the first year should be aware that 210B requires some graduate-level probability concepts that are normally introduced in 205A (or 204).
• Option 2 -- Postponement of one semester of a core course to the second year: In this option, students would take three of the core courses in the first year plus another graduate course, and take the remaining core course in their second year. An example would be a student who wanted to take courses in each of the three sequences. Such a student could take the full year of one sequence and the first semester of another sequence in the first year, and the first semester of the last sequence in the second year (e.g. 210A, 215AB in the first year, and then 204 or 205A in the second year). This would also be a good option for students who would prefer to take 210A and 215A in their first semester but are concerned about their preparation for 210B in the spring semester.  Similarly, a student with strong interests in another discipline, might postpone one of the spring core PhD courses to the second year in order to take a course in that discipline in the first year.  Students who are less mathematically prepared might also be allowed to take the upper division (under-graduate) courses Math 104 and/or 105 in their first year in preparation for 205A and/or 210B in their second year. Students who wish to take this option should consult with their faculty mentor, and then must submit a graduate student petition to the PhD Committee to request permission for  postponement. Such postponement requests will be generally approved for only one course. At all times, students must take four approved graduate courses for a letter grade in their first year.

After the first year: Students with interests primarily in statistics are expected to take at least one semester of each of the core PhD sequences during their studies. Therefore at least one semester (if not both semesters) of the remaining core sequence would normally be completed during the second year. The remaining curriculum for the second and third years would be filled out with further graduate courses in Statistics and with courses from other departments. Students are expected to acquire some experience and proficiency in computing. Students are also expected to attend at least one departmental seminar per week. The precise program of study will be decided in consultation with the student’s faculty mentor.

Remark. Stat 204 is a graduate level probability course that is an alternative to 205AB series that covers probability concepts most commonly found in the applications of probability. It is not taught all years, but does fulfill the requirements of the first year core PhD courses. Students taking Stat 204, who wish to continue in Stat 205B, can do so (after obtaining the approval of the 205B instructor), by taking an intensive one month reading course over winter break.

Designated Emphasis: Students with a Designated Emphasis in Computational and Genomic Biology or Designated Emphasis in Computational and Data Science and Engineering should, like other statistics students, acquire a firm foundation in statistics and probability, with a program of study similar to those above. These programs have additional requirements as well. Interested students should consult with the graduate advisor of these programs. 

Starting in the Fall of 2019, PhD students are required in their first year to take four semesters of the core PhD courses. Students intending to specialize in Probability, however, have the option to substitute an advanced mathematics class for one of these four courses. Such students will thus be required to take Stat 205A/B in the first year,  at least one of Stat 210A/B or Stat 215A/B in the first year, in addition to an advanced mathematics course. This substitute course will be selected in consultation with their faculty mentor, with some possible courses suggested below. Students arriving with advanced coursework equivalent to that of 205AB can obtain permission to substitute in other advanced probability and mathematics coursework during their first year, and should consult with the PhD committee for such a waiver.

During their second and third years, students with a probability focus are expected to take advanced probability courses (e.g., Stat 206 and Stat 260) to fulfill the coursework requirements that follow the first year. Students are also expected to attend at least one departmental seminar per week, usually the probability seminar. If they are not sufficiently familiar with measure theory and functional analysis, then they should take one or both of Math 202A and Math 202B. Other recommended courses from the department of Mathematics or EECS include:

Math 204, 222 (ODE, PDE) Math 205 (Complex Analysis) Math 258 (Classical harmonic analysis) EE 229 (Information Theory and Coding) CS 271 (Randomness and computation)

The Qualifying Examination 

The oral qualifying examination is meant to determine whether the student is ready to enter the research phase of graduate studies. It consists of a 50-minute lecture by the student on a topic selected jointly by the student and the thesis advisor. The examination committee consists of at least four faculty members to be approved by the department.  At least two members of the committee must consist of faculty from the Statistics and must be members of the Academic Senate. The chair must be a member of the student’s degree-granting program.

Qualifying Exam Chair. For qualifying exam committees formed in the Fall of 2019 or later, the qualifying exam chair will also serve as the student’s departmental mentor, unless a student already has two thesis advisors. The student must select a qualifying exam chair and obtain their agreement to serve as their qualifying exam chair and faculty mentor. The student's prospective thesis advisor cannot chair the examination committee. Selection of the chair can be done well in advance of the qualifying exam and the rest of the qualifying committee, and because the qualifying exam chair also serves as the student’s departmental mentor (unless the student has co-advisors), the chair is expected to be selected by the beginning of the third year or at the beginning of the semester of the qualifying exam, whichever comes earlier. For more details regarding the selection of the Qualifying Exam Chair, see the "Mentoring" tab.  

Paperwork and Application. Students at the point of taking a qualifying exam are assumed to have already found a thesis advisor and to should have already submitted the internal departmental form to the Graduate Student Services Advisor ( found here ).  Selection of a qualifying exam chair requires that the faculty member formally agree by signing the internal department form ( found here ) and the student must submit this form to the Graduate Student Services Advisor.  In order to apply to take the exam, the student must submit the Application for the Qualifying Exam via CalCentral at least three weeks prior to the exam. If the student passes the exam, they can then officially advance to candidacy for the Ph.D. If the student fails the exam, the committee may vote to allow a second attempt. Regulations of the Graduate Division permit at most two attempts to pass the oral qualifying exam. After passing the exam, the student must submit the Application for Candidacy via CalCentral .

The Doctoral Thesis

The Ph.D. degree is granted upon completion of an original thesis acceptable to a committee of at least three faculty members. The majority or at least half of the committee must consist of faculty from Statistics and must be members of the Academic Senate. The thesis should be presented at an appropriate seminar in the department prior to filing with the Dean of the Graduate Division. See Alumni if you would like to view thesis titles of former PhD Students.

Graduate Division offers various resources, including a workshop, on how to write a thesis, from beginning to end. Requirements for the format of the thesis are rather strict. For workshop dates and guidelines for submitting a dissertation, visit the Graduate Division website.

Students who have advanced from candidacy (i.e. have taken their qualifying exam and submitted the advancement to candidacy application) must have a joint meeting with their QE chair and their PhD advisor to discuss their thesis progression; if students are co-advised, this should be a joint meeting with their co-advisors. This annual review is required by Graduate Division.  For more information regarding this requirement, please see  https://grad.berkeley.edu/ policy/degrees-policy/#f35- annual-review-of-doctoral- candidates .

Teaching Requirement

For students enrolled in the graduate program before Fall 2016, students are required to serve as a Graduate Student Instructor (GSI) for a minimum of 20 hours (equivalent to a 50% GSI appointment) during a regular academic semester by the end of their third year in the program.

Effective with the Fall 2016 entering class, students are required to serve as a GSI for a minimum of two 50% GSI appointment during the regular academic semesters prior to graduation (20 hours a week is equivalent to a 50% GSI appointment for a semester) for Statistics courses numbered 150 and above. Exceptions to this policy are routinely made by the department.

Each spring, the department hosts an annual conference called BSTARS . Both students and industry alliance partners present research in the form of posters and lightning talks. All students in their second year and beyond are required to present a poster at BSTARS each year. This requirement is intended to acclimate students to presenting their research and allow the department generally to see the fruits of their research. It is also an opportunity for less advanced students to see examples of research of more senior students. However, any students who do not yet have research to present can be exempted at the request of their thesis advisor (or their faculty mentors if an advisor has not yet been determined).

Mentoring for PhD Students

Initial Mentoring: PhD students will be assigned a faculty mentor in the summer before their first year. This faculty mentor at this stage is not expected to be the student’s PhD advisor nor even have research interests that closely align with the student. The job of this faculty mentor is primarily to advise the student on how to find a thesis advisor and in selecting appropriate courses, as well as other degree-related topics such as applying for fellowships.  Students should meet with their faculty mentors twice a semester. This faculty member will be the designated faculty mentor for the student during roughly their first two years, at which point students will find a qualifying exam chair who will take over the role of mentoring the student.

Research-focused mentoring : Once students have found a thesis advisor, that person will naturally be the faculty member most directly overseeing the student’s progression. However, students will also choose an additional faculty member to serve as a the chair of their qualifying exam and who will also serve as a faculty mentor for the student and as a member of his/her thesis committee. (For students who have two thesis advisors, however, there is not an additional faculty mentor, and the quals chair does NOT serve as the faculty mentor).

The student will be responsible for identifying and asking a faculty member to be the chair of his/her quals committee. Students should determine their qualifying exam chair either at the beginning of the semester of the qualifying exam or in the fall semester of the third year, whichever is earlier. Students are expected to have narrowed in on a thesis advisor and research topic by the fall semester of their third year (and may have already taken qualifying exams), but in the case where this has not happened, such students should find a quals chair as soon as feasible afterward to serve as faculty mentor.

Students are required to meet with their QE chair once a semester during the academic year. In the fall, this meeting will generally be just a meeting with the student and the QE chair, but in the spring it must be a joint meeting with the student, the QE chair, and the PhD advisor. If students are co-advised, this should be a joint meeting with their co-advisors.

If there is a need for a substitute faculty mentor (e.g. existing faculty mentor is on sabbatical or there has been a significant shift in research direction), the student should bring this to the attention of the PhD Committee for assistance.

PhD Student Forms:

Important milestones: .

Each of these milestones is not complete until you have filled out the requisite form and submitted it to the GSAO. If you are not meeting these milestones by the below deadline, you need to meet with the Head Graduate Advisor to ask for an extension. Otherwise, you will be in danger of not being in good academic standing and being ineligible for continued funding (including GSI or GSR appointments, and many fellowships). 

†Students who are considering a co-advisor, should have at least one advisor formally identified by the end of the second year; the co-advisor should be identified by the end of the fall semester of the 3rd year in lieu of finding a Research Mentor/QE Chair.

Expected Progress Reviews: 

* These meetings do not need to be held in the semester that you take your Qualifying Exam, since the relevant people should be members of your exam committee and will discuss your research progress during your qualifying exam

** If you are being co-advised by someone who is not your primary advisor because your primary advisor cannot be your sole advisor, you should be meeting with that person like a research mentor, if not more frequently, to keep them apprised of your progress. However, if both of your co-advisors are leading your research (perhaps independently) and meeting with you frequently throughout the semester, you do not need to give a fall research progress report.

About the Program

The Department of Statistics offers the Master of Arts (MA) and Doctor of Philosophy (PhD) degrees.

Master of Arts (MA)

The Statistics MA program prepares students for careers that require statistical skills. It focuses on tackling statistical challenges encountered by industry rather than preparing for a PhD. The program is for full-time students and is designed to be completed in two semesters (fall and spring).

There is no way to transfer into the PhD program from the MA program. Students must apply to the PhD program.

Doctor of Philosophy (PhD)

The Statistics PhD program is rigorous, yet welcoming to students with interdisciplinary interests and different levels of preparation. The standard PhD program in statistics provides a broad background in probability theory and applied and theoretical statistics.

There are three designated emphasis (DE) tracks available to students in the PhD program who wish to pursue interdisciplinary work formally:  Computational and Data Science and Engineering ,  Computational and Genomic Biology  and Computational Precision Health .

Visit Department Website

Admission to the University

Admission to the program, 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 .

In addition to the minimum requirements listed above, the following materials are required for admission:

• The Online Graduate Application for Admission and Fellowships :
• Statement of Purpose  :  Why are you applying to this program? What are your expectations for this degree? Where do you want this degree to take you, professionally and personally? How will your professional and personal experiences add value to the program?
• Personal History  Statement  :  What past experiences made you decide to go into this field? How will your personal history help you succeed in this program and your future goals? 
• Descriptive List of Upper Division/Graduate Statistics and Math Coursework:  Please include a Descriptive List of Upper Division/Graduate Statistics and Math Coursework. List the department, course number and title, instructor, grade, school, texts used and subject matter covered for all upper division and graduate level statistics and math courses you have taken. You should also include courses outside statistics and math departments that have a significant quantitative component. This list should be uploaded as a PDF document via the online application.
• GPA Worksheet : Please upload a GPA calculation worksheet.
• Resume:  Include a full resume/CV listing your experience and education.

The application process is entirely online. All supplemental materials such as transcripts and the descriptive list of courses must be uploaded as PDF files via the online application by the application deadline. Please do not mail copies of your transcripts, statement of purpose, letters of recommendations, GRE and TOEFL scores, resumes, or any other documents as they will not be included with your application. 

T he GRE is no longer required for applicants applying to the MA or PhD program.  For the PhD program, while it is not required, if you wish to include your GRE Math Subject test you will have the option to do so.

For more information about graduate programs in statistics, including admission information, please visit our graduate programs page .

Doctoral Degree Requirements

Normative time requirements, normative time to advancement.

In the first year, students must perform satisfactorily in preliminary course work. In the summer, students are required to embark on a short-term research project, internship, graduate student instructorship, reading course, or on another research activity.

In the second and third years, students continue to take courses, serve as a graduate student instructor, find an area for the oral qualifying exam, a potential thesis adviser and pass the oral qualifying exam in the spring semester of second year or in the fall semester of third year. With the successful passing of the exam, students then advance to candidacy.

Normative Time in Candidacy

In the third and fourth years, students finalize a thesis topic, continue to conduct research and make satisfactory progress.

By the end of the fifth year, students are expected to finish their thesis and give a lecture based on their work in a department seminar.

Total Normative Time

Total normative time is five years.

Time in Advancement 

During their first year, students are normally expected to take four of the following seven core PhD courses in Probability, Theoretical Statistics, and Applied Statistics:

A member of the PhD program committee may consent to substitute courses at a comparable level in other disciplines for some of these departmental graduate courses. These requirements can also be altered by the PhD program committee.

Students entering the program before 2022  are required to take five additional graduate courses beyond the four required in the first year, resulting in a total of nine graduate courses required for completion of their PhD. In their second year, students are required to take three graduate courses, at least two of them from the department offerings, and in their third year, they are required to take at least two graduate courses. Students are allowed to change the timing of these five courses with approval of their faculty mentor. Of the nine required graduate courses, students are required to take for credit a total of 24 semester hours of courses offered by the Statistics department numbered 204-272 inclusive. The Head Graduate Advisor (in consultation with the faculty mentor and after submission of a graduate student petition) may consent to substitute courses at a comparable level in other disciplines for some of these departmental graduate courses. In addition, the HGA may waive part of this unit requirement.

Starting with the cohort entering in the 2022-23 academic year, students are required to take at least three additional graduate courses beyond the four required in the first year, resulting in a total of seven graduate courses required for completion of their PhD. Of the seven required graduate courses, five of these courses must be from courses offered by the Statistics department and numbered 204-272, inclusive. With these reduced requirements, there is an expectation of very few waivers from the HGA. We emphasize that these are minimum requirements, and we expect that students will take additional classes of interest, for example on a S/U basis, to further their breadth of knowledge. 

For courses to count toward the coursework requirements students must receive at least a B+ in the course (courses taken S/U do not count, except for STAT 272 which is only offered S/U).  Courses that are research credits, directed study, reading groups, or departmental seminars do not satisfy coursework requirements (for courses offered by the Statistics department the course should be numbered 204-272 to satisfy the requirements). Upper-division undergraduate courses in other departments can be counted toward course requirements with the permission of the Head Graduate Advisor. This will normally only be approved if the courses provide necessary breadth in an application area relevant to the student’s thesis research.

Qualifying Examination

The oral qualifying examination is meant to determine whether the student is ready to enter the research phase of graduate studies. It consists of a 50-minute lecture by the student on a topic selected jointly by the student and the thesis advisor. The examination committee consists of at least four faculty members to be approved by the department.  At least two members of the committee must consist of faculty from the Statistics and must be members of the Academic Senate. The chair must be a member of the student’s degree-granting program.

Time in Candidacy 

Advancement .

Advancing to candidacy means a student is ready to write a doctoral dissertation. Students must apply for advancement to candidacy once they have successfully passed the qualifying examination. 

Dissertation Presentation/Finishing Talk

The Ph.D. degree is granted upon completion of an original thesis acceptable to a committee of at least three faculty members. The majority or at least half of the committee must consist of faculty from Statistics and must be members of the Academic Senate. The thesis should be presented at an appropriate seminar in the department prior to filing with the Dean of the Graduate Division. 

Required Professional Development

Students enrolled in the graduate program before fall 2016 are required to serve as a  Graduate Student Instructor  (GSI) for a minimum of 20 hours (equivalent to a 50% GSI appointment) during a regular academic semester by the end of their third year in the program.

Effective with the fall 2016 entering class, students are required to serve as a  Graduate Student Instructor  (GSI) for a minimum of two regular academic semesters and complete at least 40 hours prior to graduation (20 hours is equivalent to a 50% GSI appointment for a semester) for a course numbered 150 and above.  Exceptions to this policy are routinely made by the department.

Master's Degree Requirements

Unit requirements.

In order to obtain the MA in Statistics, admitted MA students must complete a minimum of 24 units of courses and pass a comprehensive examination.

In extremely rare cases, a thesis option may be considered by the MA advisers. Typically, this will be when either the option has been offered to the student at the time of admission, or if the student arrives with substantial progress in research in an area of interest to our faculty. 

The capstone will consist of a team-based learning experience that will give students the opportunity to work on a real-world problem and carry out a substantial data analysis project. It will culminate with a written report and an oral presentation of findings. The elective will depend on the student’s interests and will be decided in consultation with advisers.

Capstone/Thesis (Plan I)

If approved for the thesis option, you must find three faculty to be on your thesis committee. Though not required, it is strongly encouraged that one of the faculty members is from outside the Statistics Department. Both you and the thesis committee chair must agree on the topic of your thesis. Further information on how to file a thesis is available on the MA program web page . 

Capstone/Comprehensive Exam (Plan II) 

On a Saturday shortly after the spring semester begins in January, students will take a comprehensive exam on the theoretical foundations of statistics. There will be a 3-hour exam on the material of   STAT 201A and  STAT 201B . All students taking the exam will receive copies of previous examinations. 

STAT 200A Introduction to Probability and Statistics at an Advanced Level 4 Units

Terms offered: Fall 2018, Fall 2011, Fall 2010 Probability spaces, random variables, distributions in probability and statistics, central limit theorem, Poisson processes, transformations involving random variables, estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory. Introduction to Probability and Statistics at an Advanced Level: Read More [+]

Rules & Requirements

Prerequisites: Multivariable calculus and one semester of linear algebra

Credit Restrictions: Students will receive no credit for Statistics 200A after completing Statistics 201A-201B.

Hours & Format

Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of laboratory per week

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

Additional Details

Subject/Course Level: Statistics/Graduate

Grading: Letter grade.

Introduction to Probability and Statistics at an Advanced Level: Read Less [-]

STAT 200B Introduction to Probability and Statistics at an Advanced Level 4 Units

Terms offered: Spring 2019, Spring 2012, Spring 2011 Probability spaces, random variables, distributions in probability and statistics, central limit theorem, Poisson processes, transformations involving random variables, estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory. Introduction to Probability and Statistics at an Advanced Level: Read More [+]

Credit Restrictions: Students will receive no credit for Statistics 200A-200B after completing Statistics 201A-201B.

STAT C200C Principles and Techniques of Data Science 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023, Spring 2023, Spring 2022, Spring 2021, Spring 2020 Explores the data science lifecycle: question formulation, data collection and cleaning, exploratory, analysis, visualization, statistical inference, prediction, and decision-making. Focuses on quantitative critical thinking and key principles and techniques: languages for transforming, querying and analyzing data; algorithms for machine learning methods: regression, classification and clustering; principles of informative visualization; measurement error and prediction; and techniques for scalable data processing. Research term project. Principles and Techniques of Data Science: Read More [+]

Prerequisites: COMPSCI C8 / INFO C8 / STAT C8 or ENGIN 7 ; and either COMPSCI 61A or COMPSCI 88. Corequisites: MATH 54 or EECS 16A

Credit Restrictions: Students will receive no credit for DATA C200 \ COMPSCI C200A \ STAT C200C after completing DATA C100 .

Fall and/or spring: 8 weeks - 6-6 hours of lecture, 2-2 hours of discussion, and 0-2 hours of laboratory per week 15 weeks - 3-3 hours of lecture, 1-1 hours of discussion, and 0-1 hours of laboratory per week

Summer: 8 weeks - 6-6 hours of lecture, 2-2 hours of discussion, and 0-2 hours of laboratory per week

Additional Format: Three hours of lecture and one hour of discussion and zero to one hours of laboratory per week. Six hours of lecture and two hours of discussion and zero to two hours of laboratory per week for 8 weeks. Six hours of lecture and two hours of discussion and zero to two hours of laboratory per week for 8 weeks.

Formerly known as: Statistics C200C/Computer Science C200A

Also listed as: COMPSCI C200A/DATA C200

Principles and Techniques of Data Science: Read Less [-]

STAT 201A Introduction to Probability at an Advanced Level 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Distributions in probability and statistics, central limit theorem, Poisson processes, modes of convergence, transformations involving random variables. Introduction to Probability at an Advanced Level: Read More [+]

Prerequisites: Undergraduate probability at the level of Statistics 134, multivariable calculus (at the level of Berkeley’s Mathematics 53) and linear algebra (at the level of Berkeley’s Mathematics 54)

Credit Restrictions: Students will receive no credit for STAT 201A after completing STAT 200A .

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

Introduction to Probability at an Advanced Level: Read Less [-]

STAT 201B Introduction to Statistics at an Advanced Level 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Estimation, confidence intervals, hypothesis testing, linear models, large sample theory, categorical models, decision theory. Introduction to Statistics at an Advanced Level: Read More [+]

Credit Restrictions: Students will receive no credit for Statistics 201B after completing Statistics 200B.

Introduction to Statistics at an Advanced Level: Read Less [-]

STAT 204 Probability for Applications 4 Units

Terms offered: Fall 2023, Fall 2019, Spring 2017 A treatment of ideas and techniques most commonly found in the applications of probability: Gaussian and Poisson processes, limit theorems, large deviation principles, information, Markov chains and Markov chain Monte Carlo, martingales, Brownian motion and diffusion. Probability for Applications: Read More [+]

Credit Restrictions: Students will receive no credit for Statistics 204 after completing Statistics 205A-205B.

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

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

Instructor: Evans

Probability for Applications: Read Less [-]

STAT C205A Probability Theory 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 The course is designed as a sequence with Statistics C205B/Mathematics C218B with the following combined syllabus. Measure theory concepts needed for probability. Expection, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations, martingales and martingale convergence theorems. Markov chains. Stationary processes. Brownian motion. Probability Theory: Read More [+]

Also listed as: MATH C218A

Probability Theory: Read Less [-]

STAT C205B Probability Theory 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 The course is designed as a sequence with with Statistics C205A/Mathematics C218A with the following combined syllabus. Measure theory concepts needed for probability. Expection, distributions. Laws of large numbers and central limit theorems for independent random variables. Characteristic function methods. Conditional expectations, martingales and martingale convergence theorems. Markov chains. Stationary processes. Brownian motion. Probability Theory: Read More [+]

Also listed as: MATH C218B

STAT C206A Advanced Topics in Probability and Stochastic Process 3 Units

Terms offered: Fall 2024, Fall 2020, Fall 2016, Fall 2015 The topics of this course change each semester, and multiple sections may be offered. Advanced topics in probability offered according to students demand and faculty availability. Advanced Topics in Probability and Stochastic Process: Read More [+]

Prerequisites: Statistics C205A-C205B or consent of instructor

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

Also listed as: MATH C223A

Advanced Topics in Probability and Stochastic Process: Read Less [-]

STAT C206B Advanced Topics in Probability and Stochastic Processes 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 The topics of this course change each semester, and multiple sections may be offered. Advanced topics in probability offered according to students demand and faculty availability. Advanced Topics in Probability and Stochastic Processes: Read More [+]

Also listed as: MATH C223B

Advanced Topics in Probability and Stochastic Processes: Read Less [-]

STAT 210A Theoretical Statistics 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 An introduction to mathematical statistics, covering both frequentist and Bayesian aspects of modeling, inference, and decision-making. Topics include statistical decision theory; point estimation; minimax and admissibility; Bayesian methods; exponential families; hypothesis testing; confidence intervals; small and large sample theory; and M-estimation. Theoretical Statistics: Read More [+]

Prerequisites: Linear algebra, real analysis, and a year of upper division probability and statistics

Additional Format: Three hours of lecture per week.

Theoretical Statistics: Read Less [-]

STAT 210B Theoretical Statistics 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Introduction to modern theory of statistics; empirical processes, influence functions, M-estimation, U and V statistics and associated stochastic decompositions; non-parametric function estimation and associated minimax theory; semiparametric models; Monte Carlo methods and bootstrap methods; distributionfree and equivariant procedures; topics in machine learning. Topics covered may vary with instructor. Theoretical Statistics: Read More [+]

Prerequisites: Statistics 210A and a graduate level probability course; a good understanding of various notions of stochastic convergence

STAT 212A Topics in Theoretical Statistics 3 Units

Terms offered: Spring 2021, Fall 2015, Fall 2012 This course introduces the student to topics of current research interest in theoretical statistics. Recent topics include information theory, multivariate analysis and random matrix theory, high-dimensional inference. Typical topics have been model selection; empirical and point processes; the bootstrap, stochastic search, and Monte Carlo integration; information theory and statistics; semi- and non-parametric modeling; time series and survival analysis. Topics in Theoretical Statistics: Read More [+]

Prerequisites: 210 or 205 and 215

Formerly known as: 216A-216B and 217A-217B

Topics in Theoretical Statistics: Read Less [-]

STAT 212B Topics in Theoretical Statistics 3 Units

Terms offered: Spring 2016 This course introduces the student to topics of current research interest in theoretical statistics. Recent topics include information theory, multivariate analysis and random matrix theory, high-dimensional inference. Typical topics have been model selection; empirical and point processes; the bootstrap, stochastic search, and Monte Carlo integration; information theory and statistics; semi- and non-parametric modeling; time series and survival analysis. Topics in Theoretical Statistics: Read More [+]

STAT 215A Applied Statistics and Machine Learning 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Applied statistics and machine learning, focusing on answering scientific questions using data, the data science life cycle, critical thinking, reasoning, methodology, and trustworthy and reproducible computational practice. Hands-on-experience in open-ended data labs, using programming languages such as R and Python. Emphasis on understanding and examining the assumptions behind standard statistical models and methods and the match between the assumptions and the scientific question. Exploratory data analysis. Model formulation, fitting, model testing and validation, interpretation, and communication of results. Methods, including linear regression and generalizations, decision trees, random forests, simulation, and randomization methods. Applied Statistics and Machine Learning: Read More [+]

Prerequisites: Linear algebra, calculus, upper division probability and statistics, and familiarity with high-level programming languages. Statistics 133, 134, and 135 recommended

Applied Statistics and Machine Learning: Read Less [-]

STAT 215B Statistical Models: Theory and Application 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Course builds on 215A in developing critical thinking skills and the techniques of advanced applied statistics. Particular topics vary with instructor. Examples of possible topics include planning and design of experiments, ANOVA and random effects models, splines, classification, spatial statistics, categorical data analysis, survival analysis, and multivariate analysis. Statistical Models: Theory and Application: Read More [+]

Prerequisites: Statistics 215A or consent of instructor

Statistical Models: Theory and Application: Read Less [-]

STAT 222 Masters of Statistics Capstone Project 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 The capstone project is part of the masters degree program in statistics. Students engage in professionally-oriented group research under the supervision of a research advisor. The research synthesizes the statistical, computational, economic, and social issues involved in solving complex real-world problems. Masters of Statistics Capstone Project: Read More [+]

Prerequisites: Statistics 201A-201B, 243. Restricted to students who have been admitted to the one-year Masters Program in Statistics beginning fall 2012 or later

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

Additional Format: One hour of laboratory and three hours of seminar per week.

Masters of Statistics Capstone Project: Read Less [-]

STAT 230A Linear Models 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Theory of least squares estimation, interval estimation, and tests under the general linear fixed effects model with normally distributed errors. Large sample theory for non-normal linear models. Two and higher way layouts, residual analysis. Effects of departures from the underlying assumptions. Robust alternatives to least squares. Linear Models: Read More [+]

Prerequisites: Matrix algebra, a year of calculus, two semesters of upper division or graduate probability and statistics

Linear Models: Read Less [-]

STAT 232 Experimental Design 4 Units

Terms offered: Spring 2023, Spring 2022, Fall 2018 This course will review the statistical foundations of randomized experiments and study principles for addressing common setbacks in experimental design and analysis in practice. We will cover the notion of potential outcomes for causal inference and the Fisherian principles for experimentation (randomization, blocking, and replications). We will also cover experiments with complex structures (clustering in units, factorial design, hierarchy in treatments, sequential assignment, etc). We will also address practical complications in experiments, including noncompliance, missing data, and measurement error. Experimental Design: Read More [+]

Prerequisites: Statistics 134 and Statistics 135 and experience with Software R, or consent of instructor

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

Experimental Design: Read Less [-]

STAT 238 Bayesian Statistics 3 Units

Terms offered: Fall 2016 Bayesian methods and concepts: conditional probability, one-parameter and multiparameter models, prior distributions, hierarchical and multi-level models, predictive checking and sensitivity analysis, model selection, linear and generalized linear models, multiple testing and high-dimensional data, mixtures, non-parametric methods. Case studies of applied modeling. In-depth computational implementation using Markov chain Monte Carlo and other techniques. Basic theory for Bayesian methods and decision theory. The selection of topics may vary from year to year. Bayesian Statistics: Read More [+]

Objectives & Outcomes

Course Objectives: develop Bayesian models for new types of data implement Bayesian models and interpret the results read and discuss Bayesian methods in the literature select and build appropriate Bayesian models for data to answer research questions understand and describe the Bayesian perspective and its advantages and disadvantages compared to classical methods

Prerequisites: Probability and mathematical statistics at the level of Stat 134 and Stat 135 or, ideally, Stat 201A and Stat 201B

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.

Bayesian Statistics: Read Less [-]

STAT 239A The Statistics of Causal Inference in the Social Science 4 Units

Terms offered: Fall 2015, Fall 2014 Approaches to causal inference using the potential outcomes framework. Covers observational studies with and without ignorable treatment assignment, randomized experiments with and without noncompliance, instrumental variables, regression discontinuity, sensitivity analysis and randomization inference. Applications are drawn from a variety of fields including political science, economics, sociology, public health and medicine. The Statistics of Causal Inference in the Social Science: Read More [+]

Prerequisites: At least one graduate matrix based multivariate regression course in addition to introductory statistics and probability

Fall and/or spring: 15 weeks - 3-3 hours of lecture and 1-2 hours of discussion per week

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

Grading: Letter grade. This is part one of a year long series course. A provisional grade of IP (in progress) will be applied and later replaced with the final grade after completing part two of the series.

Instructor: Sekhon

The Statistics of Causal Inference in the Social Science: Read Less [-]

STAT 239B Quantitative Methodology in the Social Sciences Seminar 4 Units

Terms offered: Spring 2016, Spring 2015 A seminar on successful research designs and a forum for students to discuss the research methods needed in their own work, supplemented by lectures on relevant statistical and computational topics such as matching methods, instrumental variables, regression discontinuity, and Bayesian, maximum likelihood and robust estimation. Applications are drawn from political science, economics, sociology, and public health. Experience with R is assumed. Quantitative Methodology in the Social Sciences Seminar: Read More [+]

Prerequisites: Statistics 239A or equivalent

Grading: Letter grade. This is part two of a year long series course. Upon completion, the final grade will be applied to both parts of the series.

Quantitative Methodology in the Social Sciences Seminar: Read Less [-]

STAT C239A The Statistics of Causal Inference in the Social Science 4 Units

Terms offered: Fall 2018, Fall 2017, Fall 2016 Approaches to causal inference using the potential outcomes framework. Covers observational studies with and without ignorable treatment assignment, randomized experiments with and without noncompliance, instrumental variables, regression discontinuity, sensitivity analysis and randomization inference. Applications are drawn from a variety of fields including political science, economics, sociology, public health and medicine. The Statistics of Causal Inference in the Social Science: Read More [+]

Fall and/or spring: 15 weeks - 3 hours of lecture and 2 hours of discussion per week

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

Also listed as: POL SCI C236A

STAT C239B Quantitative Methodology in the Social Sciences Seminar 4 Units

Terms offered: Spring 2018, Spring 2017 A seminar on successful research designs and a forum for students to discuss the research methods needed in their own work, supplemented by lectures on relevant statistical and computational topics such as matching methods, instrumental variables, regression discontinuity, and Bayesian, maximum likelihood and robust estimation. Applications are drawn from political science, economics, sociology, and public health. Experience with R is assumed. Quantitative Methodology in the Social Sciences Seminar: Read More [+]

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

Also listed as: POL SCI C236B

STAT 240 Nonparametric and Robust Methods 4 Units

Terms offered: Spring 2023, Spring 2021, Fall 2017 Standard nonparametric tests and confidence intervals for continuous and categorical data; nonparametric estimation of quantiles; robust estimation of location and scale parameters. Efficiency comparison with the classical procedures. Nonparametric and Robust Methods: Read More [+]

Prerequisites: A year of upper division probability and statistics

Nonparametric and Robust Methods: Read Less [-]

STAT C241A Statistical Learning Theory 3 Units

Terms offered: Fall 2023, Fall 2021, Fall 2020 Classification regression, clustering, dimensionality, reduction, and density estimation. Mixture models, hierarchical models, factorial models, hidden Markov, and state space models, Markov properties, and recursive algorithms for general probabilistic inference nonparametric methods including decision trees, kernal methods, neural networks, and wavelets. Ensemble methods. Statistical Learning Theory: Read More [+]

Instructors: Bartlett, Jordan, Wainwright

Also listed as: COMPSCI C281A

Statistical Learning Theory: Read Less [-]

STAT C241B Advanced Topics in Learning and Decision Making 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Recent topics include: Graphical models and approximate inference algorithms. Markov chain Monte Carlo, mean field and probability propagation methods. Model selection and stochastic realization. Bayesian information theoretic and structural risk minimization approaches. Markov decision processes and partially observable Markov decision processes. Reinforcement learning. Advanced Topics in Learning and Decision Making: Read More [+]

Also listed as: COMPSCI C281B

Advanced Topics in Learning and Decision Making: Read Less [-]

STAT 243 Introduction to Statistical Computing 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Concepts in statistical programming and statistical computation, including programming principles, data and text manipulation, parallel processing, simulation, numerical linear algebra, and optimization. Introduction to Statistical Computing: Read More [+]

Student Learning Outcomes: Become familiar with concepts and tools for reproducible research and good scientific computing practices. Operate effectively in a UNIX environment and on remote servers. Program effectively in languages including R and Python with an advanced knowledge of language functionality and an understanding of general programming concepts. Understand in depth and make use of principles of numerical linear algebra, optimization, and simulation for statistics-related research.

Prerequisites: Graduate standing

Introduction to Statistical Computing: Read Less [-]

STAT 244 Statistical Computing 4 Units

Terms offered: Spring 2011, Spring 2010, Spring 2009 Algorithms in statistical computing: random number generation, generating other distributions, random sampling and permutations. Matrix computations in linear models. Non-linear optimization with applications to statistical procedures. Other topics of current interest, such as issues of efficiency, and use of graphics. Statistical Computing: Read More [+]

Prerequisites: Knowledge of a higher level programming language

Statistical Computing: Read Less [-]

STAT C245A Introduction to Modern Biostatistical Theory and Practice 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Course covers major topics in general statistical theory, with a focus on statistical methods in epidemiology. The course provides a broad theoretical framework for understanding the properties of commonly-used and more advanced methods. Emphasis is on estimation in nonparametric models in the context of contingency tables, regression (e.g., linear, logistic), density estimation and more. Topics include maximum likelihood and loss-based estimation , asymptotic linearity/normality, the delta method, bootstrapping, machine learning, targeted maximum likelihood estimation. Comprehension of broad concepts is the main goal, but practical implementation in R is also emphasized. Basic knowledge of probability/statistics and calculus are assume Introduction to Modern Biostatistical Theory and Practice: Read More [+]

Prerequisites: Statistics 200A (may be taken concurrently)

Instructor: Hubbard

Also listed as: PB HLTH C240A

Introduction to Modern Biostatistical Theory and Practice: Read Less [-]

STAT C245B Biostatistical Methods: Survival Analysis and Causality 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Analysis of survival time data using parametric and non-parametric models, hypothesis testing, and methods for analyzing censored (partially observed) data with covariates. Topics include marginal estimation of a survival function, estimation of a generalized multivariate linear regression model (allowing missing covariates and/or outcomes), estimation of a multiplicative intensity model (such as Cox proportional hazards model) and estimation of causal parameters assuming marginal structural models. General theory for developing locally efficient estimators of the parameters of interest in censored data models. Computing techniques, numerical methods, simulation and general implementation of biostatistical analysis techniques with emphasis on data applications. Biostatistical Methods: Survival Analysis and Causality: Read More [+]

Prerequisites: Statistics 200B (may be taken concurrently)

Instructor: van der Laan

Also listed as: PB HLTH C240B

Biostatistical Methods: Survival Analysis and Causality: Read Less [-]

STAT C245C Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine 4 Units

Terms offered: Fall 2023, Fall 2022, Fall 2021 This course provides an introduction to computational statistics, with emphasis on statistical methods and software for addressing high-dimensional inference problems in biology and medicine. Topics include numerical and graphical data summaries, loss-based estimation (regression, classification, density estimation), smoothing, EM algorithm, Markov chain Monte-Carlo, clustering, multiple testing, resampling, hidden Markov models, in silico exp eriments. Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine: Read More [+]

Prerequisites: Statistics 200A or equivalent (may be taken concurrently)

Instructor: Dudoit

Also listed as: PB HLTH C240C

Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine: Read Less [-]

STAT C245D Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine II 4 Units

Terms offered: Fall 2017, Fall 2015, Fall 2013 This course and Pb Hlth C240C/Stat C245C provide an introduction to computational statistics with emphasis on statistical methods and software for addressing high-dimensional inference problems that arise in current biological and medical research. The courses also discusses statistical computing resources, with emphasis on the R language and environment (www.r-project.org). Programming topics to be discussed include: data structures, functions , statistical models, graphical procedures, designing an R package, object-oriented programming, inter-system interfaces. The statistical and computational methods are motivated by and illustrated on data structures that arise in current high-dimensional inference problems in biology and medicine. Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine II: Read More [+]

Prerequisites: Statistics 200A-200B or Statistics 201A-201B (may be taken concurrently) or consent of instructor

Also listed as: PB HLTH C240D

Biostatistical Methods: Computational Statistics with Applications in Biology and Medicine II: Read Less [-]

STAT C245F Statistical Genomics 4 Units

Terms offered: Spring 2022, Spring 2021, Spring 2020, Spring 2018, Spring 2017 Genomics is one of the fundamental areas of research in the biological sciences and is rapidly becoming one of the most important application areas in statistics. The first course in this two-semester sequence is Public Health C240E/Statistics C245E. This is the second course, which focuses on sequence analysis, phylogenetics, and high-throughput microarray and sequencing gene expression experiments. The courses are primarily intended for graduate students and advanced undergraduate students from the mathematical sciences. Statistical Genomics: Read More [+]

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.

Instructors: Dudoit, Huang, Nielsen, Song

Also listed as: PB HLTH C240F

Statistical Genomics: Read Less [-]

STAT C247C Longitudinal Data Analysis 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2021 Course covers statistical issues surrounding estimation of effects using data on units followed through time. Course emphasizes a regression model approach for estimating associations of disease incidence modeling, continuous outcome data/linear models & longitudinal extensions to nonlinear models forms (e.g., logistic). Course emphasizes complexities that repeated measures has on the estimation process & opportunities it provides if data is modeled appropriately. Most time is spent on 2 approaches: mixed models based upon explicit (latent variable) maximum likelihood estimation of the sources of the dependence, versus empirical estimating equation approaches (generalized estimating equations). Primary focus is from the analysis side. Longitudinal Data Analysis: Read More [+]

Course Objectives: After successfully completing the course, you will be able to: • frame data science questions relevant to longitudinal studies as the estimation of statistical parameters generated from regression, • derive consistent statistical inference in the presence of correlated, repeated measures data using likelihood-based mixed models and estimating equation approaches (generalized estimating equations; GEE), • implement the relevant methods using R. • interpret the regression output, including both coefficients and variance components and

Prerequisites: 142, 145, 241 or equivalent courses in basic statistics, linear and logistic regression

Also listed as: PB HLTH C242C

Longitudinal Data Analysis: Read Less [-]

STAT 248 Analysis of Time Series 4 Units

Terms offered: Spring 2022, Spring 2021, Spring 2020 Frequency-based techniques of time series analysis, spectral theory, linear filters, estimation of spectra, estimation of transfer functions, design, system identification, vector-valued stationary processes, model building. Analysis of Time Series: Read More [+]

Prerequisites: 102 or equivalent

Analysis of Time Series: Read Less [-]

STAT 251 Stochastic Analysis with Applications to Mathematical Finance 3 Units

Terms offered: Spring 2008, Spring 2006, Spring 2005 The essentials of stochastic analysis, particularly those most relevant to financial engineering, will be surveyed: Brownian motion, stochastic integrals, Ito's formula, representation of martingales, Girsanov's theorem, stochastic differential equations, and diffusion processes. Examples will be taken from the Black-Scholes-Merton theory of pricing and hedging contingent claims such as options, foreign market derivatives, and interest rate related contracts. Stochastic Analysis with Applications to Mathematical Finance: Read More [+]

Prerequisites: 205A or consent of instructor

Stochastic Analysis with Applications to Mathematical Finance: Read Less [-]

STAT 254 Modern Statistical Prediction and Machine Learning 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This course is about statistical learning methods and their use for data analysis. Upon completion, students will be able to build baseline models for real world data analysis problems, implement models using programming languages and draw conclusions from models. The course will cover principled statistical methodology for basic machine learning tasks such as regression, classification, dimension reduction and clustering. Methods discussed will include linear regression, subset selection, ridge regression, LASSO, logistic regression, kernel smoothing methods, tree based methods, bagging and boosting, neural networks, Bayesian methods, as well as inference techniques based on resampling, cross validation and sample splitting. Modern Statistical Prediction and Machine Learning: Read More [+]

Prerequisites: STAT 135 , the combination of DATA/STAT/COMPSCI C100 and DATA/STAT C140, or equivalent

Modern Statistical Prediction and Machine Learning: Read Less [-]

STAT 256 Causal Inference 4 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 This course will focus on approaches to causal inference using the potential outcomes framework. It will also use causal diagrams at an intuitive level. The main topics are classical randomized experiments, observational studies, instrumental variables, principal stratification and mediation analysis. Applications are drawn from a variety of fields including political science, economics, sociology, public health, and medicine. This course is a mix of statistical theory and data analysis. Students will be exposed to statistical questions that are relevant to decision and policy making. Causal Inference: Read More [+]

Prerequisites: Statistics 201B or Statistics 210A

Causal Inference: Read Less [-]

STAT 259 Reproducible and Collaborative Statistical Data Science 4 Units

Terms offered: Spring 2023, Spring 2022, Spring 2021 A project-based introduction to statistical data analysis. Through case studies, computer laboratories, and a term project, students will learn practical techniques and tools for producing statistically sound and appropriate, reproducible, and verifiable computational answers to scientific questions. Course emphasizes version control, testing, process automation, code review, and collaborative programming. Software tools may include Bash , Git, Python, and LaTeX. Reproducible and Collaborative Statistical Data Science: Read More [+]

Prerequisites: Statistics 133, Statistics 134, and Statistics 135 (or equivalent)

Credit Restrictions: Students will receive no credit for Statistics 259 after taking Statistics 159.

Reproducible and Collaborative Statistical Data Science: Read Less [-]

STAT 260 Topics in Probability and Statistics 3 Units

Terms offered: Fall 2024, Spring 2024, Spring 2023 Special topics in probability and statistics offered according to student demand and faculty availability. Topics in Probability and Statistics: Read More [+]

Topics in Probability and Statistics: Read Less [-]

STAT C261 Quantitative/Statistical Research Methods in Social Sciences 3 Units

Terms offered: Spring 2016, Spring 2015, Spring 2014 Selected topics in quantitative/statistical methods of research in the social sciences and particularly in sociology. Possible topics include: analysis of qualitative/categorical data; loglinear models and latent-structure analysis; the analysis of cross-classified data having ordered and unordered categories; measure, models, and graphical displays in the analysis of cross-classified data; correspondence analysis, association analysis, and related methods of data analysis. Quantitative/Statistical Research Methods in Social Sciences: Read More [+]

Prerequisites: Consent of instructor

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

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

Also listed as: SOCIOL C271D

Quantitative/Statistical Research Methods in Social Sciences: Read Less [-]

STAT 265 Forecasting 3 Units

Terms offered: Spring 2024 Forecasting has been used to predict elections, climate change, and the spread of COVID-19. Poor forecasts led to the 2008 financial crisis. In our daily lives, good forecasting ability can help us plan our work, be on time to events, and make informed career decisions. This practically-oriented class will provide students with tools to make good forecasts, including Fermi estimates, calibration training, base rates, scope sensitivity, and power laws. Forecasting: Read More [+]

Course Objectives: We’ll discuss several historical instances of successful and unsuccessful forecasts, and practice making forecasts about our own lives, about current events, and about scientific progress.

Student Learning Outcomes: Formulate questions that are relevant to their own life or work. Identify well-defined versus poorly-defined forecasting questions. Provide forecasts that are well-calibrated. Understand common forecasting pitfalls, such as improper independence assumptions, and how to identify and guard against them. Understand how forecasts evolve across time in response to new information. Use forecasts to inform decisions. Utilize a variety of forecasting tools, such as base rates, to improve their forecasts. Utilize and filter data across a variety of sources to inform their forecasts. Work in teams to improve forecasts.

Prerequisites: Stat 134, Data/Stat C140, EECS 126 , Math 106, IND ENG 172 , or equivalent; and familiarity with Python; or consent of instructor. Strongly Recommended: Compsci 61A, Data/Compsci C88C, or equivalent

Forecasting: Read Less [-]

STAT 272 Statistical Consulting 3 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 To be taken concurrently with service as a consultant in the department's drop-in consulting service. Participants will work on problems arising in the service and will discuss general ways of handling such problems. There will be working sessions with researchers in substantive fields and occasional lectures on consulting. Statistical Consulting: Read More [+]

Prerequisites: Some course work in applied statistics and permission of instructor

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

Additional Format: Two hours of session per week and individual meetings as necessary.

Grading: Offered for satisfactory/unsatisfactory grade only.

Statistical Consulting: Read Less [-]

STAT 278B Statistics Research Seminar 1 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Special topics, by means of lectures and informational conferences. Statistics Research Seminar: Read More [+]

Fall and/or spring: 15 weeks - 0 hours of seminar per week

Additional Format: Two or more hours of seminar per week.

Statistics Research Seminar: Read Less [-]

STAT 298 Directed Study for Graduate Students 1 - 12 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Special tutorial or seminar on selected topics. Directed Study for Graduate Students: Read More [+]

Fall and/or spring: 15 weeks - 0 hours of independent study per week

Summer: 6 weeks - 1-16 hours of independent study per week 8 weeks - 1-12 hours of independent study per week

Additional Format: Zero hours of Independent study per week for 15 weeks. One to Twelve hour of Independent study per week for 8 weeks. One to Sixteen hour of Independent study per week for 6 weeks.

Directed Study for Graduate Students: Read Less [-]

STAT 299 Individual Study Leading to Higher Degrees 0.5 - 12 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Individual study Individual Study Leading to Higher Degrees: Read More [+]

Fall and/or spring: 15 weeks - 2-36 hours of independent study per week

Summer: 6 weeks - 4-45 hours of independent study per week 8 weeks - 3-36 hours of independent study per week 10 weeks - 2.5-27 hours of independent study per week

Additional Format: Work hours to be arrange based on unit value. Offered for 1-6 units during Summer Session.

Individual Study Leading to Higher Degrees: Read Less [-]

STAT 375 Professional Preparation: Teaching of Probability and Statistics 2 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Discussion, problem review and development, guidance of laboratory classes, course development, supervised practice teaching. Professional Preparation: Teaching of Probability and Statistics: Read More [+]

Prerequisites: Graduate standing and appointment as a graduate student instructor

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

Additional Format: One or two hours of lecture and two to four of laboratory per week.

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

Formerly known as: Statistics 300

Professional Preparation: Teaching of Probability and Statistics: Read Less [-]

STAT 601 Individual Study for Master's Candidates 0.5 - 8 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Individual study in consultation with the graduate adviser, intended to provide an opportunity for qualified students to prepare themselves for the master's comprehensive examinations. Units may not be used to meet either unit or residence requirements for a master's degree. Individual Study for Master's Candidates: Read More [+]

Repeat rules: Course may be repeated for credit up to a total of 16 units.

Fall and/or spring: 15 weeks - 0.5-8 hours of independent study per week

Summer: 6 weeks - 1.5-20 hours of independent study per week 8 weeks - 1-15 hours of independent study per week 10 weeks - 1-12 hours of independent study per week

Additional Format: One-half to eight hours of independent study per week. One to twelve hours of independent study per week for 10 weeks. One to fifteen hours of independent study per week for 8 weeks. One and one-half to twenty hours of independent study per week for 6 weeks.

Subject/Course Level: Statistics/Graduate examination preparation

Individual Study for Master's Candidates: Read Less [-]

STAT 602 Individual Study for Doctoral Candidates 0.5 - 8 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Individual study in consultation with the graduate adviser, intended to provide an opportunity for qualified students to prepare themselves for certain examinations required of candidates for the Ph.D. degree. Individual Study for Doctoral Candidates: Read More [+]

Prerequisites: One year of full-time graduate study and permission of the graduate adviser

Credit Restrictions: Course does not satisfy unit or residence requirements for doctoral degree.

Individual Study for Doctoral Candidates: Read Less [-]

STAT 700 Statistics Colloquium 0.0 Units

Terms offered: Prior to 2007 The Statistics Colloquium is a forum for talks on the theory and applications of Statistics to be given to the faculty and graduate students of the Statistics Department and other interested parties. Statistics Colloquium: Read More [+]

Fall and/or spring: 15 weeks - 1-2 hours of colloquium per week

Additional Format: One to two hours of colloquium per week.

Grading: The grading option will be decided by the instructor when the class is offered.

Formerly known as: Statistics 999

Statistics Colloquium: Read Less [-]

Contact Information

Department of statistics.

367 Evans Hall

Phone: 510-642-2781

Fax: 510-642-7892

Department Chair

Haiyan Huang

[email protected]

PhD Program Coordinator

La Shana Porlaris

373 Evans Hall

Phone: 510-642-5361

[email protected]

Master's Program Coordinator

David Apilado Jr.

375 Evans Hall

Phone: (510) 643-0589

[email protected]

Print Options

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Graduate Group in Science and Mathematics Education (SESAME)

The Graduate Group in Science and Mathematics Education (informally known as SESAME) is an interdisciplinary graduate program leading to a doctoral degree in science, mathematics, or engineering education. The program is designed to give graduates advanced expertise in a scientific discipline as well as in educational theory and research methodologies.

This Graduate Group was established so individuals with training or experience in a mathematical, scientific, or technical discipline can pursue advanced studies focused on educational issues in these disciplines. SESAME produces scholars who can communicate effectively with scientists and engineers as well as with educational researchers and practitioners. SESAME students are expected to obtain at least masters-level competency in their mathematical or scientific discipline.

See the  Online Graduate Application  to apply to the Interdisciplinary PhD in Science and Mathematics Education. We encourage you to read our Admissions Instructions thoroughly and begin your application early.

SESAME also offers a Learning Sciences Certificate in Instructional Design, Learning Technologies, and Education Research.

SESAME Faculty

The SESAME faculty consists of professors from UC Berkeley's School of Education and a variety of Berkeley’s science and engineering departments.

Core faculty

Affiliated faculty.

Lloyd Goldwasser (link is external) , Lecturer, School of Education

Emeritus faculty

Focus of study.

SESAME students work with faculty to gain a better understanding of learning; to design more effective teaching approaches; and to create experiences that enhance the scientific and mathematical literacy of the general public. A major aim of the Group is to identify general theoretical principles that can guide the design of effective instruction.

Many student projects are concerned with college-level teaching in their disciplines. Others are concerned with:

• K-12 curriculum development 
• research into cognitive processes underlying good performance in scientific domains
• investigations of principles for the design of computer-based educational software; or
• studies of informal learning in science museums and other places open to the public.

Program Structure

SESAME includes advanced courses in the student's discipline, science and mathematics education, and psychology; teaching experience; research seminars; colloquia presented by outside speakers; and research into an educational problem connected with engineering, science, mathematics, or computer-science education. 

While SESAME emphasizes research in the processes of learning and teaching, it is not a teacher training unit. Students interested in careers in college-level math/science teaching (along with educational research), science museum program development, or research in the learning and teaching of science are likely to find SESAME suitable. 

The faculty consists of professors from several of Berkeley’s science and engineering departments and the Graduate School of Education, and instructors associated with other campus units such as the Lawrence Hall of Science.

Entrance Requirements

To enter the program, a student must have an excellent academic record with a bachelor's or, preferably, a master's degree in a natural science, mathematics, or engineering/computer science.  Experience teaching, developing instructional materials, or doing educational or psychological research in these areas will also be favorably considered.  Knowledge of psychology, cognitive science, education, or statistics is helpful but not required. See  SESAME Admissions  for more information.

Type of Program (MA/PhD)

This program offers a PhD and students who enter without a master’s degree in their discipline are expected to obtain at least Master's-level competency in their mathematical or scientific discipline.  

The program typically takes 10 to 12 semesters to complete, depending upon whether students are also working toward their master’s degree. Full time enrollment is expected. 

SESAME graduates take leadership roles in promoting educational innovations in academic, industrial, and museum settings including the  Exploratorium  and the  Lawrence Hall of Science . Others teach in two- or four-year colleges or universities, or are directing educational programs of science museums or similar institutions that offer programs for the general public. Still others are active in educational research and curricular development, in industrial training programs, or in their own consulting businesses.

Transfer from within Berkeley 

Application for admission into SEASAME by students already enrolled in a graduate degree program of the Berkeley campus is formally accomplished by submitting a Graduate Petition for Change of Major or Degree Goal .

These petitions are considered along with other applications for admission to the doctoral program. A petition for Change of Degree Goal should be accompanied in all cases by a statement describing the reasons for the proposed change and the nature of the program of studies contemplated. Any applicants previously admitted by the Graduate Division must still submit the standard application form and required letters of recommendation. We may also request a copy of your file from your current department.

Admissions Instructions and FAQ

Lloyd Goldwasser, SESAME Lecturer 2121 Berkeley Way, Room 4321 [email protected]

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Difference between math and applied math phd?

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Four new faculty hires are a quantum leap for experimental physics

New assistant professors cement UC Berkeley's leadership in quantum science and technology

By Robert Sanders

Courtesy of Department of Physics

August 1, 2024

Already known as a leader in quantum science and a testbed for quantum computing, the University of California, Berkeley, is expanding its footprint with the hiring of four early-career experimental physicists who use quantum systems to explore new frontiers in physics.

The new assistant professors of physics will augment a wide range of quantum research already underway in the departments of physics, chemistry and engineering, much of it in collaboration with Lawrence Berkeley National Laboratory. They will arrive within the next year and will leverage the weird quantum properties of atoms and light to make sensitive detectors or improve quantum computing and networking.

Chiara Pancaldo Salemi , for example, employs the quantum properties of superconducting circuits to search for dark matter particles called axions. Aziza Suleymanzade is using entangled photons in optical fibers to network quantum computers. Victoria Xu squeezes light to improve detection of gravitational waves. And Harry Levine entangles trapped neutral atoms to explore new types of qubits and reduce the noise in today’s quantum computers.

“It’s highly unusual to hire four experimentalists in one year, especially considering the high cost of startup research support and lab renovation, so this is a real statement that Berkeley is committed to the emerging field of quantum information science and technology,” said Steven Kahn, dean of mathematical and physical sciences in UC Berkeley’s College of Letters and Science and a professor of physics and astronomy. “These are the best young people in this super-exciting field. Each of them had multiple offers from competing institutions. The new directions their research will take us could be revolutionary.”

“It’s great to have four highly decorated, very talented early-career physicists join the department,” said Irfan Siddiqi , professor and chair of physics and a pioneer in using superconducting quantum circuits as qubits for quantum computing. “They bridge traditional fields of physics with more modern notions of quantum information science. What’s wonderful is, not only are they pushing a particular discipline forward, but they’re also seeing how harnessing the quantum nature of light and matter can push the limits of quantum sensors and the power of quantum computers.”

Keegan Houser, UC Berkeley

The theory of quantum mechanics, which describes how matter and light interact at the smallest scales, has been around for more than 100 years. It explains many aspects of nature that defy common sense, such as the fact that light behaves like both a wave and a particle — at the same time. The phones and computers we use daily employ semiconductor chips that operate because of quantum effects. Quantum mechanics describes why some materials become superconductors at ultracold temperatures, and also how plants absorb light for photosynthesis.

Researchers in labs around the campus have long explored the quantum properties of new materials, clusters of cold atoms, excited single atoms and chemical interactions, to name a few. Others take advantage of the known quantum properties of matter to make sensitive detectors for magnetic fields or gravity or to make precise atomic clocks.

Scientists are also leveraging another weirdness of quantum mechanics called entanglement — first proven in a UC Berkeley physics lab in 1972 — to build quantum computers. Entanglement links the fates of one or more particles such that what happens to one instantly affects what happens to the others, no matter how far apart they are. Quantum computers are based on manipulating entangled quantum bits — qubits — to solve some problems that would take a classical supercomputer an eternity.

As these fields have ramped up, UC Berkeley researchers have entangled themselves in numerous quantum efforts spearheaded by the National Science Foundation and the Department of Energy. UC Berkeley is the lead institution for the NSF-funded Challenge Institute for Quantum Computation , which addresses fundamental challenges to the development of the quantum computer, including the training of new quantum scientists.

UC Berkeley and Berkeley Lab also partner on DOE-funded efforts, such as the Quantum Systems Accelerator to explore different types of qubits and computing algorithms, the Advanced Quantum Testbed , an incubator for innovative quantum computation technologies, and QUANT-NET (Quantum Application Network Testbed for Novel Entanglement Technology), which hopes to build a network to teleport information between quantum computers on the campus and at Berkeley Lab via optical fiber. Another effort, Quantum Algorithms for Chemical Sciences , focuses on developing algorithms that can be used with quantum computers to predict the outcome of chemical reactions.

The four new experimentalists will take campus research in new but complementary directions, Kahn said, focusing on using quantum systems as sensitive detectors to discover new physics.

“Each of them is linked to other research at Berkeley not currently relying on quantum sensing, but which could benefit from quantum links, such as applying qubit technology to sensing,” he said.

Speaking for her and her colleagues, Suleymanzade said, “I think all of our dreams revolve around how to get some sort of enhancements from these quantum mechanical properties that are either very different or advantageous in comparison to classical systems and classical resources.”

“All of these folks have one foot in fundamental science, the other foot in technological applications of quantum technologies,” Siddiqi said. “And that, of course, attracts a lot of local talent, helps build the workforce, and brings quantum a step closer to being more mainstream technology.”

Aziza Suleymanzade: Networking quantum computers

Suleymanzade will join the physics faculty in July 2025 after finishing up her postdoctoral work at Harvard University, her undergraduate alma mater.

Courtesy of Aziza Suleymanzade.

Her main interest is using photons of light to interconnect quantum computers, networking computers in dispersed labs the way that digital computers are linked today through the internet. Doing this is complicated, in part because there are different types of quantum computers that use different quantum systems — trapped ions, superconducting circuits, even photons — as qubits. Just about anything that can be entangled can be used as a qubit for quantum computing.

The other challenge is linking the quantum information in these computers via entangled photons through a fiber optic cable without losing the entanglement that allows computation.

“In the future we will have distributed quantum sensing or distributed quantum computing, where you can imagine having a network of quantum nodes located in different places, even across tens of kilometers of distances, and being able to distribute entanglement and exotic quantum resources and being able to actually do computation distributed over larger distances, similar to how we do currently in classical computers,” Suleymanzade said.

She plans to tackle this challenge in her own lab in the basement of Birge Hall, where rooms are windowless to avoid extraneous light and vibrations are dampened to reduce the shaking of mirrors and lenses that direct the laser beams that provide the photons. She hopes to combine two types of quantum qubit systems already well developed — superconducting circuits and highly excited cold atoms called Rydberg atoms — into a hybrid system.

“I’m an experimentalist, so I’m really excited about bridging these platforms together to create new quantum systems with capabilities that are not just the sum of the two,” she said. “My motivation is to get entanglement out of these systems into the world.”

A native of Azerbaijan, she grew up in Russia but finished high school in Islamabad, Pakistan, where she learned enough English to apply and be accepted at Harvard. After earning a master’s degree from the University of Cambridge in the United Kingdom, she completed her Ph.D. at the University of Chicago and returned to Harvard to work in the quantum optics lab of Mikhail Lukin.

Chiara Salemi: Dark matter axions

The search for dark matter — the mysterious missing mass in the universe — has moved into the laboratory, with a focus on finding evidence for a theoretical dark matter particle called the axion. As a doctoral student at the Massachusetts Institute of Technology, Salemi helped build a table-top experiment called ABRACADABRA (A Broadband/Resonant Approach to Cosmic Axion Detection with an Amplifying B-field Ring Apparatus) to try to detect these particles, which should be all around us. Currently in a joint postdoctoral position at Stanford University and the SLAC National Accelerator Laboratory in Menlo Park, she’s working on a low-mass axion detector called DMRadio, a scaled up version of ABRACADABRA, and a high-mass axion detector called BREAD (Broadband Reflector Experiment for Axion Detection).

She plans to continue her search for axions at Berkeley employing superconducting circuits — qubits and superconducting quantum interference devices (SQUIDS) — as detectors.

Courtesy of Chiara Salemi

“If axions themselves are very light, that means there have to be a lot of them, so instead of treating the axions as individual particles, we look at the coherent effect of all of the axions acting together as a classical field,” she said. “You can do classical electromagnetism to study how they interact, but quantum sensors are a perfect way to detect these very tiny electromagnetic effects.”

The superconducting circuits used as qubits in quantum computers are ideal for quantum sensors, since they have been studied extensively.

“You can’t really build a quantum computer to do practical things right now. But the level that quantum technology is at right now is basically perfect for developing these quantum sensors,” she said.

Qubit sensors would allow her to look for high-mass axions in a range so-far unexplored. SQUIDs, another type of quantum circuit, are better suited for detecting low-mass axions.

As result of her current joint position at Stanford and SLAC, she’s become accustomed to collaborating with colleagues at DOE labs. At Berkeley, she will be affiliated with Berkeley Lab and is eager to interact with theorists and experimentalists there and on campus. She is already collaborating on the DMRadio experiment with Karl van Bibber, professor of nuclear engineering.

A native of Chapel Hill, North Carolina, Salemi first delved into dark matter detection — looking for weakly interacting massive particles (WIMPS) as well as axions — while an undergraduate physics and math major at the University of North Carolina at Chapel Hill. After earning her PhD from MIT, she accepted a postdoctoral fellowship at Stanford’s Kavli Institute for Particle Astrophysics and Cosmology (KIPAC) and SLAC. She will join the physics department on Jan. 1, 2025.

Victoria Xu: Squeezed light

During its first three runs between 2015 and 2020, the Advanced Laser Interferometer Gravitational-Wave Observatory (LIGO) detected ripples in spacetime from about 90 mergers of black holes and neutron stars, all from relatively near events. Expanding LIGO’s cosmic reach for the current run, which began in May 2023, required squeezing more sensitivity from the detectors, and to Xu, that meant squeezing light.

Gravitational waves are ripples in the fabric of spacetime. LIGO works by detecting the tiny fluctuations these waves produce in the distance two laser beams travel along perpendicular arms more than a kilometer long. If their paths are not exactly equal, as when a gravitational wave lengthens one arm and shortens the other, the beams interfere proportionate to the intensity of the gravitational wave.

Xu said that squeezing the laser light is important because the LIGO team is continuously optimizing all other aspects of the detectors, reducing the classical noise so that quantum noise prevails.

“Laser light is fundamentally composed of photons, and at some point quantization of light into photons limits you. That’s quantum noise,” Xu said.

She and the LIGO detector team, including colleagues at MIT, where she has been a postdoc for the past three years, upgraded the squeezing subsystem — first employed in 2019 for the third run — for the fourth round of observations. Within eight months, they detected nearly as many gravitational wave triggers from mergers in real-time as during the previous three runs combined. The reach of the detectors has increased by 65%, she said, extending the reach of gravitational-wave detectors far deeper into the universe.

Further reducing quantum noise with squeezing meant installing a special 300-meter cavity along the laser beamline that optimizes the entanglement between “squeezed” photon pairs to enhance detector sensitivity. Squeezing allows complementary properties of the vacuum field — phase and amplitude — to be manipulated. For some frequencies of gravitational waves, reducing noise in the phase produces a more sensitive detection, even though the amplitude noise increases. For other frequencies the opposite is true. Her focus at Berkeley will be in further applying quantum technologies to maximize detections of merging massive objects in the universe.

“We are really pursuing the best possible measurement that can be made,” she said. “It’s pretty cool because, for me, I get to work with this great team of people. I get to learn from scientists all over the world and build a new detector that’s going to discover new things about the universe. It’s kind of the best of all worlds. It checks all the boxes I like.”

A Bay Area native, Xu attended UC Santa Barbara, majoring in physics, and came to UC Berkeley as a graduate student, where she worked on atom interferometry with Holger Müller. After receiving her Ph.D., she moved to MIT to work on laser interferometry with the large LIGO team. In January 2025, she will join the Berkeley faculty.

“It’s like a homecoming to me,” she said.

Harry Levine: Fun with qubits

Levine will come to UC Berkeley in July 2025 after a four-year stint at the AWS Center for Quantum Computing, a partnership between Amazon Web Services and the California Institute of Technology based on the university’s campus in Pasadena. There he has been working on one of the most popular types of qubits for quantum computing, solid state circuits cooled to cryogenic temperatures.

Courtesy of Harry Levine

At Berkeley, he hopes to turn to a type of qubit he worked on for his Ph.D. at Harvard University: single atoms levitated in vacuum chambers. He plans to build vacuum chambers to study and control these at the level of each individual atom. An interesting challenge, he said, is to learn how to control and entangle the quantum motion of clusters of atoms.

“Now that we know how to control atoms with a very rich toolbox these days, we can think about taking multiple atoms and delocalizing all of them in an entangled way,” he said. “I think what’s so exciting about it is, it can offer us a new way to try to make the most macroscopic, crazy quantum states that you can imagine, to try to push the bounds on how massive of an object can you put in a superposition state. That’s kind of always a frontier in which you want to observe quantum effects in larger and larger systems and over more and more macroscopic length scales.”

He’s also interested in ways to suppress the noise in quantum systems, which causes them to lose their entanglement, a process called decoherence. One possible way to reduce decoherence, that is, increase the lifetime of entangled qubits and decrease the errors in quantum computing, is to group qubits together into “logical qubits” that may be less susceptible to decoherence. He plans to explore new strategies for doing so, leveraging and expanding on some of the very exciting progress in the field over the last few years, he said.

Upon hearing that he had been offered a position in the physics department, “I was super excited,” he said. “Berkeley is an incredible institution. I have so much respect for the community here and feel so honored to be offered the chance to join. I think it’ll be an amazing place to start a research group and to contribute to the educational mission of the university.”

A native of Los Angeles, Levine obtained his undergraduate degree in physics and math from Stanford and his Ph.D. from Harvard. Now, he said, he’s taking the unusual leap back into academia after a stint as an industry scientist.

Quick Links +

Applied ph.d. program requirements (fall 2024).

This plan requires a total of 60 units. Students will enroll for 12 units per quarter including research, academic, and seminar units. Before advancing to Candidacy for a doctoral degree, a student must have satisfied all requirements set by the graduate program, maintained a minimum GPA of 3.0 in all course work undertaken (except those courses graded S or U), passed the PhD Preliminary Examination, and passed the Qualifying Examination. A dissertation and an exit seminar are required.

Course Requirements

• Area A (12 units in one of the series) : (i) Analysis Series MAT 201AB/MAT 201C/MAT 205/MAT206 (Analysis) or (ii) Methods of Applied Mathematics MAT 207ABC.  
• Area B (8 units in one of the series): (i) Data Science series: Numerical Optimization (MAT 258A), Discrete and Mixed-Integer Optimization (MAT258B), Mathematics of Data Science (MAT 270); or (ii) Numerical Methods series: Numerical Methods: Fundamentals (MAT 226A), Numerical Methods: Large-Scale Matrix Computations (MAT 226B), Numerical Methods: Ordinary Differential Equations (MAT226C), Numerical Solutions of Differential Equations A, B and C (MAT 228ABC); or (iii) Probability series: Probability Theory A and B (MAT235AB), Mathematical Statistics III (STA231C); or (iv) Theoretical Computer Science series: Theoretical Computer Science (ECS220), Design and Analysis of Algorithms (ECS222).  
• Graduate Advanced Mathematics: 12 units of 200-level Mathematics courses whose course numbers are 279 or less. These include courses from areas A or B not selected as core courses. One mathematics-oriented graduate course outside of Mathematics, could be substituted but only with prior approval by GGAM Chair.  
• Field of Specialization (FOS): Minimum of 15 units in a field of specialization, such as, any courses in Area A or B not taken to satisfy the core requirements, optimization, and control, differential equations, probability and statistics, discrete mathematics, mathematical physics, mathematical biology, harmonic analysis and signal processing, etc. Out of this minimum of 15 units, at least one course of 3 or more units must be outside of Mathematics. Courses outside of the mathematics department have to be approved by the Chair of the graduate group. For a list of preapproved courses, see the GGAM webpage and consult with the potential thesis advisor. An updated list of courses can be found in https://appliedmath.ucdavis.edu/graduate/sample-depth-courses  
• Advanced Mathematics: Maximum of 12 units, in three 100 level (i.e.upper division) math courses.  
• GGAM miniconference: Attendance at the annual GGAM mini-conference, which takes place during winter quarter graduate welcome and consists of faculty talks and a student poster session showcasing the research being done at GGAM, is required in the first or second year, for which 1 unit of 290 will be given in order to document compliance.  
• MAT 290 seminars  
• MAT 390 and Teaching: Each student who accepts a TAship in the Department of Mathematics is required to complete MAT390, which is taught every Fall quarter. Most students take this in their first year, even if they are supported by a fellowship. MAT 390 does not count toward degree units (but does count for the 12-unit minimum required for registration each quarter). All PhD students are required to be teaching assistants for at least one quarter. Exceptions require approval of the GGAMEXEC. Students beyond their first year are encouraged to apply for positions as Associate-Instructors to develop and improve their lecturing skills.  
• Research Units: Students can register for research units but these units cannot substitute course requirements unless previously reviewed and approved by the chair of the graduate group.

Ph.D. Preliminary Examination

The Ph.D. Preliminary Examination is a written examination covering materials from the student’s chosen tracks Area A (MAT 201AB or MAT 207ABC) and Area B: Data Science: (MAT 168, MAT170); Numerical Methods (MAT128ABC); Probability (MAT 135AB); and Theoretical Computer Science (ECS220, ECS222). The exam is offered in June and September every year. PhD students are required to pass this examination before the end of their second year.  They may take the examination multiple times; what matters is when they pass, not how many attempts.

Qualifying Examination

Students must complete the course requirements before taking their Qualifying Examination (QE). The QE will consist of a written research proposal, a syllabus of materials relevant to the research proposal covered in the chosen tracks in Areas A and B, and oral examination. Approximately six weeks before the date of the proposed QE, the research proposal, along with the QE Application, is submitted to GGAMEXEC for approval. Once approved and required signatures obtained, the QE Application will be forwarded to the Office of Graduate Studies for final approval. The QE should be taken by the sixth quarter and no later than the end of the ninth quarter after admission to the Ph.D. program. Passing the QE makes the student eligible for advancement to candidacy.

Dissertation

The doctoral dissertation is an essential part of the Ph.D. program. A topic will be selected by the student, under the guidance of the Dissertation Advisor. Students are encouraged to begin their research activity as early as possible. The dissertation must contain an original contribution of publishable quality to the knowledge of applied mathematics. Acceptance of the dissertation by the dissertation committee must follow Graduate Studies guidelines (Plan B). The program does not have any program-specific requirements, such as length or presentation format. Instructions on preparation of the dissertation and a schedule of dates for filing the thesis in final form are available from Graduate Studies; the dates are also printed in the UC Davis General Catalog.

Exit Seminar

Ph.D. students are required to give a 60-minute seminar presentation, open to the public, on their dissertation subject.

Optional Final Oral Examination (at the discretion of the Dissertation Committee)

After the exit seminar, the student’s dissertation committee may meet privately with the student to discuss the contents of the dissertation and ask additional questions. Satisfaction of this requirement must be verified by the Dissertation Committee Chair.