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Ph.D. Course work Guidelines, Structure and Syllabus (Revised August – 2021)

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Ph.D. Course work Guidelines, Structure, and Syllabus (Revised August – 2021)

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Doctoral Program - Coursework

PhD students register for 10 units in each of the Autumn, Winter and Spring quarters. Most courses offered by the department for PhD students are three units, including the core courses of the first-year program. In addition to regular lecture courses on advanced topics, reading courses in the literature of probability and the literature of statistics are available each quarter. Students working on their dissertation may register for up to 10 units of directed research in each quarter. Students should also register for selected courses outside the Statistics Department to fulfill the breadth requirement .

Prerequisites

Equivalents of Math 113, Math 115; Stats 116, Stats 200; CS 106A. (Descriptions of these courses may be viewed on Stanford's ExploreCourses course listings pages.

Previous experience has shown that before starting the core courses students need to have mastered the material in the prerequisite courses (or their equivalents at other universities), as demonstrated by very strong and relatively recent grades. Where this background is missing or not recent, admission to the PhD program will involve working with the Director of Graduate Studies to design an individual program to make up the necessary courses.

Core Courses

Statistics 300A, 300B and 300C systematically survey the ideas of estimation and of hypothesis testing for parametric and nonparametric models involving small and large samples.

Statistics 305A is concerned with linear regression and the analysis of variance. Statistics 305B and 305C survey a large number of modeling techniques, related to but going significantly beyond the linear models of 305A.

Statistics 310A, 310B and 310C are measure-theoretic courses in probability theory, beginning with basic concepts of the law of large numbers, and martingale theory.

Although the content of the first-year core courses is specified by the department, the order in which topics are studied and details of the presentation are left to the instructor and will vary from year to year. Unusually well-prepared students may place out of Statistics 305A. Students who do not have a sufficient mathematics background can, with approval from the Graduate Director, take the 310 series after the first year. All core courses must be taken for a letter grade.

Literature Course

Stats 319 is a literature course in statistics and probability that is offered each quarter. The course is generally taken by students in the second and third years and may be taken repeatedly. It serves two connected purposes:

• to expose students to a variety of topics of current research interest, for example, to help identify dissertation topics. Students are expected to read several articles and to write a short paper related to the reading that is presented to the class. The paper can be a synthesis of the reading material, or it may mark the beginning of research in the area. Reading assignments are made in consultation with any faculty member, especially the course instructor.
• to allow students the opportunity to practice giving and receiving feedback on talk techniques. The talk can be on dissertation work in progress, on an ancillary project (consulting, RA work), or on a selection of papers that the student has recently read. The instructor of the literature course, along with the student's course peers, provides feedback on the talk, and can also provide guidance in topic choice where needed.

All students who have passed the qualifying exams but have not yet passed the Dissertation Proposal Meeting must take Stats 319 Literature of Statistics at least once per year.

Advanced Courses (Depth Requirement)

Students are required to complete a depth requirement consisting of a minimum of three courses (nine units) of advanced topics courses offered by the department. Courses for the depth and breadth (see below) requirements must equal a combined minimum of 24 units. Recommended advanced topics courses include the following:

• Introduction to Time Series Analysis (Stats 307)
• Information Theory and Statistics (Stats 311)
• Advanced Statistical Methods (Stats 314A)
• Modern Applied Statistics: Learning (Stats 315A)
• Modern Applied Statistics: Learning II (Stats 315B)
• Stochastic Processes (Stats 317)
• Modern Markov Chains (Stats 318)
• Machine Learning Methods for Neural Data Analysis (Stats 320)
• Function Estimation in White Noise (Stats 322)
• Multivariate Analysis (Stats 325)
• Causal Inference (Stats 361)
• Monte Carlo (Stats 362)
• Design of Experiments (Stats 363)
• Bayesian Statistics (Stats 370)
• Convex Optimization I (EE 364A)
• Convex Optimization II (EE 364B)

In any given year only some of these courses will be offered. These courses are normally taken after the first year and may help students to find dissertation topics.

Consulting Workshop

Students taking the Consulting Workshop, STATS 390, provide a free consulting service to the Stanford community. Researchers from all areas of the university community are free to request appointments to discuss their research or analysis problems. This course allows students to assimilate material from their first-year courses, especially STATS 305A/B/C.

The consulting is executed by teams of students, in which inexperienced students are matched with those more proficient. The course is offered each quarter and may be taken repeatedly. Students are encouraged to participate in the formulation of the consulting problems and in any data analysis which may be involved.

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Syllabus for VTU – ETR Ph.D./M.Sc. ( Engineering ) by Research 2019-20 

Ph.D. 2018 Batch Syllabus

PhD Requirements

The first year.

The time to degree (normative time) of the Computational Biology PhD is five years. The first year of the program emphasizes gaining competency in computational biology, the biological sciences, and the computational sciences (broadly construed). Since student backgrounds will vary widely, each student will work with faculty and student advisory committees to develop a program of study tailored to their background and interests. Specifically, all first-year students must:

• Perform three rotations with Core faculty (one rotation with a non-Core faculty is acceptable with advance approval)
• Complete course work requirements (see below)
• Complete a course in the Responsible Conduct of Research
• Attend the computational biology seminar series
• Complete experimental training (see below)

Laboratory Rotations

Entering students are required to complete three laboratory rotations during their first year in the program to seek out a Dissertation Advisor under whose supervision dissertation research will be conducted. Students should rotate with at least one computational Core faculty member and one experimental Core faculty member.

Click here to view the rotation policy.

Course Work & Additional Requirements

Students must complete the following coursework in the first three (up to four) semesters. Courses must be taken for a grade and a grade of B or higher is required for a course to count towards degree progress:

• Fall and Spring semester of  CMPBIO 293, Doctoral Seminar in Computational Biology
• A Responsible Conduct of Research course, most likely through the Department of Molecular and Cell Biology.
• STAT 201A & STAT 201B: Intro to Probability and Statistics at an Advanced Level. Note: Students who are offered admission and are not prepared to complete STAT 201A and 201B will be required to complete STAT 134 or PH 142 first.
• CS61A: The Structure and Interpretation of Computer Programs.  Note: students with the equivalent background can replace this requirement with a more advanced CS course of their choosing.
• 3 elective courses relevant to the field of Computational Biology, one of which must be at the graduate level (see below for details).
• Attend the computational biology invited speaker seminar series. A schedule is circulated to all students by email and is available on the Center website. Starting with the 2023 entering class, CCB PhD students must enroll in CMPBIO 275:  Computational Biology Seminar , which provides credit for this seminar series.
• 1) completion of a laboratory course at Berkeley with a minimum grade of B,
• 2) completion of a rotation in an experimental lab (w/ an experimental project), with a positive evaluation from the PI,
• a biological sciences undergraduate major with at least two upper division laboratory-based courses,
• a semester or equivalent of supervised undergraduate experimental laboratory-based research at a university,
• or previous paid or volunteer/internship work in an industry-based experimental laboratory.

Students are expected to develop a course plan for their program requirements and to consult with the Head Graduate Advisor before the Spring semester of their first year for formal approval (signature required). The course plan will take into account the student’s undergraduate training areas and goals for PhD research areas.

Satisfactory completion of first year requirements will be evaluated at the end of the spring semester of the first year. If requirements are satisfied, students will formally choose a Dissertation advisor from among the core faculty with whom they rotated and begin dissertation research.

Waivers:  Students may request waivers for the specific courses STAT 201A, STAT 201B, and CS61A. In all cases of waivers, the student must take alternative courses in related areas so as to have six additional courses, as described above. For waiving out of STAT 201A/B, students can demonstrate they have completed the equivalent by passing a proctored assessment exam on Campus. For waiving out CS61A, the Head Graduate Advisor will evaluate student’s previous coursework based on the previous course’s syllabus and other course materials to determine equivalency.

Electives:  Of the three electives, students are required to choose one course in each of the two following cluster areas:

• Cluster A (Biological Science): These courses are defined as those for which the learning goals are primarily related to biology. This includes courses covering topics in molecular biology, genetics, evolution, environmental science, experimental methods, and human health. This category may also cover courses whose focus is on learning how to use bioinformatic tools to understand experimental data.
• Cluster B (Computational Sciences): These courses are defined as those for which the learning goals involve computing, inference, or mathematical modeling, broadly defined. This includes courses on algorithms, computing languages or structures, mathematical or probabilistic concepts, and statistics. This category would include courses whose focus is on biological applications of such topics.

In the below link we give some relevant such courses, but students can take courses beyond this list; for courses not on this list, the Head Graduate Advisor will determine to which cluster a course can be credited. For classes that have significant overlap between these two clusters, the department which offers the course may influence the decision of the HGA as to whether the course should be assigned to cluster A or B.

See below for some suggested courses in these categories:

Suggested Coursework Options  (link is external)

Second Year & Beyond

At the beginning of the fall of the second year, students begin full-time dissertation research in earnest under the supervision of their Dissertation advisor. It is anticipated that it will take students three (up to four) semesters to complete the 6 course requirement. Students are required to continue to participate annually in the computational biology seminar series.

Qualifying Examination

Students are expected to take and pass an oral Qualifying Examination (QE) by the end of the spring semester (June 15th) of their second year of graduate study. Students must present a written dissertation proposal to the QE committee no fewer than four weeks prior to the oral QE. The write-up should follow the format of an NIH-style grant proposal (i.e., it should include an abstract, background and significance, specific aims to be addressed (~3), and a research plan for addressing the aims) and must thoroughly discuss plans for research to be conducted in the dissertation lab.

Click here for more details on the guidelines and format for the QE.

Advancement to Candidacy

After successfully completing the QE, students will Advance to Candidacy. At this time, students select the members of their dissertation committee and submit this committee for approval to the Graduate Division. Students should endeavor to include a member whose research represents a complementary yet distinct area from that of the dissertation advisor (ie, biological vs computational, experimental vs theoretical) and that will be integrated in the student’s dissertation research.

Click here to view the rules for the composition of the committee and the form for declaring your committee.

Meetings with the Dissertation Committee

After Advancing to Candidacy, students are expected to meet with their Dissertation Committee at least once each year.

Teaching Requirements

Computational Biology PhD students are required to teach at least two semesters (starting with Fall 2019 class), but may teach more. The requirement can be modified if the student has funding that does not allow teaching. Starting with the Fall 2019 class: At least one of those courses should require that you teach a section. Berkeley Connect or CMPBIO 293 can count towards one of the required semesters.

The Dissertation

Dissertation projects will represent scholarly, independent and novel research that contributes new knowledge to Computational Biology by integrating knowledge and methodologies from both the biological and computational sciences. Students must submit their dissertation by the May Graduate Division filing deadline (see Graduate Division for date) of their fifth–and final–year.

Special Requirements

Students will be required to present their research either orally or via a poster at the annual retreat beginning in their second year.