Applied Mathematics and Statistics
201819 General Catalog
Baskin School of Engineering
(831) 4592158
https://www.soe.ucsc.edu
Program Description
Applied mathematics and statistics are disciplines devoted to the use of mathematical methods and reasoning to solve realworld problems of a scientific or decisionmaking nature in a wide variety of subjects, principally (but not exclusively) in engineering, medicine, the physical and biological sciences, and the social sciences. Applied mathematical modeling often involves the use of systems of (partial) differential equations to describe and predict the behavior of complex realworld systems that unfold dynamically in time. Statistics, construed broadly, is the study of uncertainty: how to measure it (using ideas and methods in probability theory), and what to do about it (using concepts from statistical inference and decision theory).
The Applied Mathematics and Statistics Department at UCSC offers master’s and doctoral programs in Statistics and Applied Mathematics, or Applied Mathematics and Statistics, depending on chosen emphasis, and a master's program in Scientific Computing and Applied Mathematics (SciCAM). Effective fall quarter 2018, the department will offer a M.S./Ph.D. program in statistical science, intended to replace the statistics track of the statistics and applied mathematics M.S./Ph.D. program offered by the department. Discontinuance of the statistics track will be proposed in fall 2018. The goal of these programs is to help students develop into independent scholars who are prepared for productive careers in research, teaching, and industry. The department also offers a designated emphasis in statistics, a minor in statistics, and a minor in applied mathematics.
Additional information on these programs can be found on the department’s website.
Undergraduate Programs
Requirements of the Minor in Statistics
The statistics minor is available for students who wish to gain a quantitative understanding of how to (a) measure uncertainty and (b) make good decisions on the basis of incomplete or imperfect information, and to apply these skills to their interests in another field. This minor could also be combined with a major in mathematics as preparation for a graduate degree in statistics or biostatistics.
Students are required to take the following courses:

Basic calculus sequence: Applied Mathematics and Statistics 11AB or Economics 11AB or Applied Mathematics and Statistics 15AB or Mathematics 11AB or Mathematics 19AB or Mathematics 20AB

Plus one course from each of the following seven categories:

Statistical Concepts: Applied Mathematics and Statistics 5 or 7/L

Computer Programming: Biomolecular Engineering 160/L, Computer Science 12A/L or 5C or 5J or 5P or Computer Engineering 13/L or Earth Sciences 119 or Astronomy and Astrophysics 119

Linear Algebra: Applied Mathematics and Statistics 10 or Mathematics 21 (also recommended that students take Applied Mathematics and Statistics 20 or Mathematics 24)

Multivariate Calculus: Mathematics 22 or both Mathematics 23A and Mathematics 23B

Probability: Applied Mathematics and Statistics 131 Applied Mathematics and Statistics 203 or Computer Engineering 107

Statistical Inference: Applied Mathematics and Statistics 132

Computational Methods: Applied Mathematics and Statistics 147


Plus two electives from the following list of courses:
 Applied Mathematics and Statistics 156; Applied Mathematics and Statistics 198; Applied Mathematics and Statistics 202; Applied Mathematics and Statistics 205B; Applied Mathematics and Statistics 206B; Applied Mathematics and Statistics 207; Applied Mathematics and Statistics 256; Biomolecular Engineering 205; Computer Engineering 108; 145; Computer Science 142; Economics 104; 113; 114; 120; 161B; and 190; Electrical Engineering 151; Mathematics 114; Psychology 181; Sociology 103A; Technology and Information Management 230.
Note: Students planning graduate work in statistics are recommended to choose Mathematics 23AB, Applied Mathematics and Statistics 205B, and Mathematics 105AB.
Requirements of the Minor in Applied Mathematics
The applied mathematics minor is available for students who wish to develop (1) proficiency in modeling reallife problems using mathematics and (2) knowledge of standard, practical analytical and numerical methods for the solution of these models. This minor could be combined with a major in any of the physical, biological, mathematical, or engineering sciences as preparation for a graduate degree in that field or in applied mathematics.
Students are required to take the following courses:

Basic calculus sequence: Mathematics 19AB or Mathematics 20AB, and Mathematics 23AB

Plus one of the following sequences:

Applied Mathematics and Statistics 10 and 20

Mathematics 21 and 24

Physics 116A and 116B

Note: Students who complete Mathematics 21 and 24 or Physics 116A and 116B, are strongly recommended to complete the MATLAB selfpaced tutorial.

Plus one course from each of the following categories:

Probability Theory: Applied Mathematics and Statistics 131 or Computer Engineering 107

Dynamical Systems: Applied Mathematics and Statistics 114 or Applied Mathematics and Statistics 214

Introduction to Numerical Methods: Applied Mathematics and Statistics 147, Physics 115, or Earth Sciences 119

Partial Differential Equations: Applied Mathematics and Statistics 212A, Physics 116C, or Mathematics 107


Plus one appliedmathematics elective from the following list:

Applied Mathematics elective: Applied Mathematics and Statistics 107/217, 115/215, 132, 198, 212B, 213A, 213B, 216, 231, 232, 250; Electrical Engineering 103, 154; Computer Engineering 115; Mathematics 103A, 117, 121A; Physics 105, 139A, 139B, 171.

Students may also propose other electives which use applied mathematical methods, subject to approval by the department.
Graduate Programs (M.S., Ph.D.)
The department of Applied Mathematics and Statistics at UCSC offers both masters and doctoral degrees within the Statistics and Applied Mathematics (SAM) graduate program, with respective emphasis in statistics (for a degree title in “Statistics and Applied Mathematics”) or in applied mathematics (for a degree title in “Applied Mathematics and Statistics”). In addition, the department also offers an independent masters program in “Scientific Computing and Applied Mathematics (SciCAM).”
The goal of the two tracks of the SAM graduate program is to help students develop into independent scholars specialized in statistics or applied mathematics respectively, who are prepared for productive careers in research, teaching, and industry. The Master of Science (M.S.) degree may be used as a terminal degree or as the first step toward the associated Doctor of Philosophy degree (Ph.D.).
Effective fall quarter 2018, the department will offer master's and doctoral degrees in statistical science. The statistical science M.S./Ph.D. program is administered by the statistics faculty in Applied Mathematics and Statistics, and grows out of the statistics track in the SAM program which it is intended to replace. (Discontinuance of the statistics track of SAM will be proposed in Fall 2018.)
Students in the statistical science program learn to develop and use statistical methods to provide a probabilistic assessment of the variability in different data structures. This knowledge is applied to the quantification of the uncertainties inherent in the discoveries, summaries and conclusions that are drawn from the data analysis. The Ph.D. program provides mastery of fundamental concepts in statistical theory and methods, as well as analytical and computational skills to build modern statistical models, implement them, and effectively communicate their results. Through the process of learning these skills, the students develop the ability to conduct independent research. The M.S. program has its own identity. It places emphasis on the application of statistical methods to the solution of relevant scientific, technological and engineering problems, with the goal of preparing students for professional careers.
The goal of the SciCAM M.S. program is primarily to prepare students interested in scientific computing for productive careers in industry. However, it also serves as an excellent further preparation for students who prefer to pursue an academic career and wish to transfer into a Ph.D. program later, either in scientific computing/applied mathematics, or in the field related to their undergraduate degree.
Graduate Program in Statistical Science
Undergraduate preparation for admission
We will accept students with undergraduate degrees in fields that include computer science, engineering, mathematics, natural sciences, physics, and statistics, subject to appropriate course requirements in statistics and mathematics. Undergraduate preparation in mathematics and statistics should include: single variable and multivariate differential and integral calculus (UCSC equivalent AMS 11A,B or MATH 19A,B, and MATH 23A,B); linear algebra (UCSC equivalent AMS 10 or MATH 21); introductory statistics (UCSC equivalent AMS 5 or AMS 7); and introductory calculusbased probability and statistical inference (UCSC equivalent AMS 131 and AMS 132).
Program of study
Students will obtain a graduate degree (M.S. or Ph.D.) in statistical science. More specifically, students will develop background on statistical theory, methods, computing and applications through the program coursework, with research emphasis on novel methods and applications of Bayesian statistics.
Ph.D. students must complete 9 core courses: 7 5credit courses listed below; a 3credit course on research and teaching (AMS 200); and a 2credit research seminar (AMS 280B). Ph.D. students must complete 4 additional 5credit courses from the approved list of elective courses, bringing the total nonseminar credit requirements to 58 credits. M.S. students must complete 8 core courses: 6 5credit courses listed below; a 3credit course on research and teaching (AMS 200); and a 2credit research seminar (AMS 280B). M.S. students must complete 2 additional 5credit courses from the approved list of elective courses, bringing the total nonseminar credit requirement to 43 credits. None of the additional elective courses required to satisfy the unit requirements for the M.S./Ph.D. program can be substituted by independent study courses (“M.S. Project”, “Independent Study/Research” or “Thesis Research”).
Students in the Ph.D. program must take the following nine core courses:
AMS 203: Introduction to Probability Theory
AMS 204: Introduction to Statistical Data Analysis
AMS 205B: Intermediate Classical Inference
AMS 206B: Intermediate Bayesian Inference
AMS 256: Linear Statistical Models
AMS 207: Intermediate Bayesian Statistical Modeling
AMS 274: Generalized Linear Models
AMS 200: Research and Teaching in AMS
AMS 280B: Seminars in Statistical and Applied Mathematical Modeling
Students in the M.S. program must take the following eight core courses:
AMS 203: Introduction to Probability Theory
AMS 204: Introduction to Statistical Data Analysis
AMS 205: Introduction to Classical Statistical Learning
AMS 206: Applied Bayesian Statistics
AMS 256: Linear Statistical Models
AMS 207: Intermediate Bayesian Statistical Modeling
AMS 200: Research and Teaching in AMS
AMS 280B: Seminars in Statistical and Applied Mathematical Modeling
All core courses are 5credit courses, except for AMS 200 and AMS 280B. AMS 200 is a 3credit course which covers basic teaching techniques for teaching assistants, and examines research and professional training items, as well as ethical issues relating to research in science and engineering. AMS 280B is a 2credit seminar course, which involves attending the AMS Department colloquia and participating in the discussion session after the seminar presentation. The strict requirement for AMS 280B is for students to take it once in their first year in the program. However, students are strongly recommended to take AMS 280B each quarter throughout their graduate studies.
All core courses must be taken for a letter grade (except for AMS 200 and AMS 280B, which are given on a Satisfactory/Unsatisfactory basis). In order to maintain a full load for graduate standing after their first year, students take additional courses, including independent study courses, from the approved list of elective courses, appropriate to their research interests and selected in consultation with their advisers.
Ph.D. students are required to serve as teaching assistants for at least one quarter during their graduate study. Certain exceptions may be permitted for those with extensive prior teaching experience, for those who are not allowed to be employed due to visa regulations, or for other reasons approved by the director of graduate studies.
Master’s capstone project
For the M.S. degree, students conduct a capstone research project in their second year (up to three quarters), and in the spring of that year participate in a seminar in which results from their project are presented. Examples of capstone research projects include: review and synthesis of the literature on a topical area of statistical science; application and comparison of different models and/or computational techniques from a particular area of study in statistics; comprehensive analysis of a data set from a particular application area.
Students must submit a proposal to the potential faculty sponsor no later than the end of the fourth academic quarter. If the proposal is accepted, the faculty member becomes the sponsor and supervises the research and writing of the project. When the project is completed and written, it must be submitted to and accepted by a committee of two individuals, consisting of the faculty adviser and one additional reader. The additional reader will be chosen appropriately from within the graduate program faculty or outside of it. Either the adviser or the additional reader must be from within the graduate program faculty.
Ph.D. examinations
At the end of the first year, Ph.D. students take a prequalifying examination covering 6 5credit core courses: AMS 203, 204, 205B, 206B, 207 and 256. This examination comprises two parts: an inclass written examination, followed by a takehome project involving data analysis. Students who do not pass this examination can retake it before the start of the following fall quarter; if they fail the second examination they are dismissed from the Ph.D. program, but have the option to continue in the M.S. program.
Ph.D. students must complete the qualifying examination (advancement to candidacy) requirement by the end of the spring quarter of their third year. Ph.D. students must select a research adviser by the end of their second year in the program. A written dissertation proposal must be submitted to the adviser, and filed with the graduate advising office. A qualifying examination committee will be formed, consisting of the adviser and at least three additional members, approved by the director of graduate studies and the dean of the Graduate Division. The following conditions must be met for the examination committee:
 The chair of the qualifying examination committee must be a tenured faculty from within the graduate program faculty. The committee chair can not be the student’s adviser or one of her/his coadvisers.
 For students with a single adviser, or two coadvisers one of which is from outside the graduate program faculty, the committee must include at least two members from within the graduate program faculty other than the adviser or coadviser. For students with two coadvisers that are both members of the graduate program faculty, the committee must include at least one additional member from within the graduate program faculty.
 The committee must include at least one member from outside the graduate program faculty, for which the Senate Regulations for committee membership apply. The outside member can be the student’s adviser or coadviser.
The student submits the written dissertation proposal to all members of the committee no less than one month in advance of the qualifying examination. The dissertation proposal is formally presented in a public oral qualifying examination with the committee, followed by a private examination. Students will advance to candidacy after they have completed all course requirements (including removal of any incompletes), passed the qualifying examination, and paid the filing fee. Under normal progress, a student will advance to candidacy by the end of the spring quarter of her/his third year. A student who has not advanced to candidacy by the start of the fourth year will be subject to academic probation.
Upon advancement to candidacy, a dissertation reading committee is formed, consisting of the dissertation adviser (coadvisers) who serve as the chair (cochairs) of the committee, and at least two additional readers. Therefore, the minimum number of dissertation reading committee members is 3 (4) for students with a single adviser (two coadvisers). For students with a single adviser, or two coadvisers one of which is from outside the graduate program faculty, the committee must include at least two additional readers from within the graduate program faculty. For students with two coadvisers that are both members of the graduate program faculty, the committee must include at least one additional reader from within the graduate program faculty. The committee is subject to the approval of the director of graduate studies and of the Graduate Division. The Ph.D. dissertation should consist of a minimum of three chapters composed of material suitable for publication in major professional journals in statistics and journals in relevant scientific fields where the statistical methodology is applied. The completed dissertation must be submitted to the reading committee at least one month before the dissertation defense, which consists of a public presentation of the research followed by a private examination by the reading committee. Successful completion of the dissertation defense is the final requirement for the Ph.D. degree.
Relationship of M.S. and Ph.D. programs
The M.S. and Ph.D. programs are freestanding and independent, so that students can be admitted to either. Students completing the M.S. program may proceed into the Ph.D. program upon successful completion of the prequalifying examination, and application to the graduate committee and acceptance. Students in the Ph.D. program have the option of receiving the M.S. degree upon completion of the M.S. program requirements, including the capstone research project.
Normative time to degree
The normative time to the M.S. degree (for students enrolled fulltime) is two academic years. The normative precandidacy period for Ph.D. students (enrolled fulltime) is three years and the normative candidacy period is two years, for a total of five years to the Ph.D. degree.
Review of progress
Students will be admitted to the graduate (M.S. or Ph.D.) program, not to the research group of any individual faculty member. However, each student will be matched with a firstyear mentor, to ensure that adequate guidance is provided in the crucial first year of graduate school. In later years, the role of the mentor will be played by the Ph.D. thesis or M.S. project adviser. Faculty advisers will be responsible for charting the progress of their students on a regular basis, and for making necessary adjustments to their plan of study and research.
The graduate program faculty will meet in the spring quarter of each academic year to review the performance of all students in the program. Based on the results from the faculty review, a written report will be provided to each student with an assessment of her/his performance and description of specific program objectives for the following academic year.
Transfer credit
Up to three School of Engineering courses fulfilling the degree requirements of either the M.S. or Ph.D. degrees may be taken before beginning the graduate program through the concurrent enrollment program. Ph.D. students who have previously earned a M.S. degree in a related field at another institution may substitute courses from their previous university with approval of the adviser and the graduate committee. Courses from other institutions may not be applied to the M.S. degree course requirements. Petitions should be submitted along with the transcript from the other institution or UCSC Extension. For courses taken at other institutions, copies of the syllabi, examinations, and other course work should accompany the petition. Such petitions are not considered until the completion of at least one quarter at UCSC. At most, a total of three courses may be transferred from concurrent enrollment and other institutions.
Students who complete the M.S. degree in statistical science and continue onto the Ph.D. program in statistical science can transfer all applicable courses taken during the M.S. to the Ph.D. program, provided that such students meet the minimum residency requirement for Ph.D. programs at UCSC, as specified by the UCSC Graduate Division.
Graduate Program in Statistics and Applied Mathematics
Requirements for a Graduate Degree in Statistics and Applied Mathematics
This track is for students emphasizing statistics. Ph.D. students must complete the core courses described below.
Required core courses:
200 Research and Teaching in Applied Mathematics and Statistics
203 Introduction to Probability Theory
204 Introduction to Statistical Data Analysis
205B Intermediate Classical Inference
206B Intermediate Bayesian Inference
207 Intermediate Bayesian Statistical Modeling
256 Linear Statistical Models
280B Seminar in Statistics and Applied Mathematical Modeling
The required core courses for M.S. students are the same with the ones for Ph.D. students with one exception: course AMS 206 (Applied Bayesian Statistics) replaces course AMS 206B in the core for the statistics track of the M.S. program.
In addition to these 35 credits, master of science (M.S.) students must complete two additional 5credit courses from the approved list, for a total requirement of 45 credits; doctor of philosophy (Ph.D.) students must complete four additional 5credit courses from the approved list, for a total requirement of 55 credits.
Requirements for a Graduate Degree in Applied Mathematics and Statistics
This track is for students emphasizing Applied Mathematics. All students must complete the core courses described below.
Required core courses:
200 Research and Teaching in Applied Mathematics and Statistics
211 Foundations of Applied Mathematics
212A Applied Mathematical Methods I
213A Numerical Linear Algebra
213B Numerical Solutions of Differential Equations
214 Applied Dynamical Systems
280B Seminar in Statistical and Applied Mathematical Modeling
In addition to these 30 credits, master of science (M.S.) students must complete three additional 5credit courses, including a firstyear elective (see below), for a total requirement of 45 credits; doctor of philosophy (Ph.D.) students must complete five additional 5credit courses, including a firstyear elective (see below), for a total requirement of 55 credits. All elective courses must be approved by the student’s official adviser.
Firstyear electives in the applied mathematics track are designed to prepare students for their ultimate research emphasis within the applied mathematics track. They must be taken during the first year, and must be selected from the following list: course 203, 209, 216, 217, 227, 230, 231, 232, 238, 250, 260, and 275.
For both emphasis tracks, M.S. students will be allowed to substitute up to two elective courses with their required research project in which they conduct a research program in one or two of the quarters of their second year. The project will consist of solving a problem or problems from the selected area of application and will be presented to the sponsoring faculty member as a written document.
For the applied mathematics emphasis track, Ph.D. students who already have an M.S. degree (or equivalent) will be allowed to substitute up to two elective courses with corresponding numbers of credits of independent study (i.e., 5 or 10), during which they conduct research with their adviser toward their advancement to candidacy.
Ph.D. students will be required to serve as teaching assistants for at least two quarters during their graduate study. Certain exceptions may be permitted for those with extensive prior teaching experience, for those who are not allowed to be employed due to visa regulations, or for other reasons approved by the graduate director.
Qualifying Examinations
At the end of the first year, all Ph.D. students will take a prequalifying examination covering the (nonseminar) core courses. This examination will have two parts: an inclass written examination, followed by a takehome project. Ph.D. students who do not pass this examination will be allowed to retake it before the start of the following fall quarter; if they fail the second examination they will not be allowed to continue in the Ph.D. program, but will have the option to continue into the M.S. program and exit with the M.S. as the terminal degree.
Ph.D. students must complete the oral proposal defense, through which they advance to candidacy, by the end of the spring quarter of their third year. The proposal defense is a public seminar as part of an oral qualifying examination given by the qualifying committee. For the applied mathematics emphasis track, the student’s oral presentation must be approximately 45 minutes in length. Applied mathematics students will also be required to submit a substantial written document describing their research to date as well as their Ph.D. proposal ahead of time to the qualifying examination committee.
Thesis and/or Dissertation Requirements
A capstone project is required for the M.S. degree and a dissertation for the Ph.D. degree.
For the M.S. degree, students will conduct a capstone research project in their second year (up to three quarters). Students must submit a proposal to the potential faculty sponsor by the start of the fourth academic quarter. If the proposal is accepted, the faculty member will become the sponsor and will supervise the research and writing of the project. The project will involve the solution of a problem or problems from the selected area of application. When the project is completed and written, it will be submitted to and must be accepted by a committee of two individuals, consisting of the faculty adviser and one additional reader. Additional readers will be chosen appropriately from within the Applied Mathematics and Statistics Department or outside of it. Either the adviser or the additional reader must be from within the Applied Mathematics and Statistics Department.
A dissertation is required for the Ph.D. degree. Ph.D. students must select a faculty research adviser by the end of the second year. A written dissertation proposal will be submitted to the adviser, and filed with the graduate secretary. A qualifying examination committee will be formed, consisting of the adviser and three additional members, approved by the Chair of the Graduate Program and the Dean of the Graduate Division. The student will submit the written dissertation proposal to all members of the committee and the graduate secretary no less than one month in advance of the qualifying examination. The dissertation proposal will be formally presented in a public oral qualifying examination with the committee, followed by a private examination.
Students will advance to candidacy after they have completed all course requirements (including removal of all incompletes), passed the qualifying examination, and paid the filing fee. Under normal progress, a student will advance to candidacy by the end of the spring quarter of her/his third year. A student who has not advanced to candidacy by the start of the fourth year will be subject to academic probation. Upon advancement to candidacy, a dissertation reading committee will be formed, consisting of the dissertation supervisor and at least two additional readers appointed by the Graduate Program chair upon recommendation of the dissertation supervisor. At least one of these additional readers must be in the Applied Mathematics and Statistics Department. The committee is subject to the approval of the Graduate Division.
The dissertation will consist of a minimum of three chapters composed of material suitable for submission and publication in major professional journals in statistics or applied mathematics (or related subject areas of application). The completed dissertation will be submitted to the reading committee at least one month before the dissertation defense, which consists of a public presentation of the research followed by a private examination by the reading committee. Successful completion of the dissertation defense is the final requirement for the Ph.D. degree.
Relationship of Master's and Doctoral Programs
The M.S. and Ph.D. programs are freestanding and independent, so that students can be admitted to either. Students completing the M.S. program may proceed into the Ph.D. program (provided they pass the prequalifying examination), and students in the Ph.D. program can receive a M.S. degree upon completion of M.S. requirements, including the capstone research project. Each Ph.D. student will be required to have knowledge of statistics and applied mathematics equivalent to that required for the M.S. degree. In addition, Ph.D. candidates will be required to complete coursework beyond the M.S. level.
Transfer Credit
Up to three School of Engineering courses fulfilling the degree requirements of either the M.S. or Ph.D. degrees may be taken before beginning the graduate program through the concurrent enrollment program. Ph.D. students who have previously earned a master’s degree in a related field at another institution may substitute courses from their previous university with approval of the adviser and the graduate committee. Courses from other institutions may not be applied to the M.S. degree course requirements.
Petitions should be submitted along with the transcript from the other institution or UCSC Extension. For courses taken at other institutions, copies of the syllabi, exams, and other course work should accompany the petition. Such petitions are not considered until the completion of at least one quarter at UCSC. At most, a total of three courses may be transferred from concurrent enrollment and other institutions.
Students who complete an M.S. degree in applied mathematics and statistics at UC Santa Cruz and continue on to a Ph.D. program in AMS at UCSC can transfer all applicable courses taken during the M.S. to the Ph.D. program, provided that such students meet the minimum residency requirement for Ph.D. programs at UCSC, as specified by the UCSC Graduate Division.
Requirements for a Designated Emphasis in Statistics to an External Degree Program
Students from another degree program who meet the following requirements can have the designated emphasis of “statistics” annotated to their degree title. For example, a Ph.D. student in electrical engineering who meets the requirements would get a certification that read “Ph.D. Electrical Engineering (Statistics).” The course requirements are:
Required core courses:
203 Introduction to Probability Theory
206 Applied Bayesian Statistics (or 206B Intermediate Bayesian Inference)
207 Intermediate Bayesian Statistical Modeling
and one other statistics course from a list of approved courses in AMS (currently 202, 205B, 221, 223, 225, 241, 245, 256, 261, 263, 268, 274, and 291).
Upon electing to pursue a designated emphasis (DE) in statistics, students must choose a DE faculty adviser in the AMS Department. A list of eligible DE advisers is maintained online. The student must organize a preliminary meeting with the DE adviser, and agree on a plan for completion of the requirements. Once this plan has been designated, the student and the DE adviser must complete the Application for a Designated Emphasis in Statistics form. The completed application form should be signed by the student's home department adviser, the DE adviser, and the statistics graduate director, and then filed with the BSOE Graduate Advising Office (bsoega@rt.ucsc.edu). This should be done before the student's advancement to candidacy (for Ph.D. students).
Requirements for a Designated Emphasis in Scientific Computing to an External Degree Program
Students from another degree program who meet certain requirements (see below) can have the designated emphasis of “Scientific Computing” annotated to their degree title. For example, a M.S. or Ph.D. student in physics who meets the requirements would get a certification that read “M.S. Physics (Scientific Computing) or “Ph.D. Physics (Scientific Computing).”
Upon electing to pursue a designated emphasis (DE) in scientific computing, students must choose a DE faculty adviser in the AMS Department. A list of eligible DE advisers is maintained online. The student must organize a preliminary meeting with the DE adviser, and agree on a plan for completion of the requirements. Once this plan has been designed, the student and the adviser must complete the DE application form available online. The completed application form should then be signed by the AMS graduate director, and filed in the BSOE graduate advising office. This should be done before the student’s advancement to candidacy (for Ph.D. students), and no later than three months before the planned date for the oral presentation completion requirement (see below).
Course and Writing Requirements for the DE in Scientific Computing

Course requirements: AMS 213A (Numerical Linear Algebra), AMS 213B (Numerical Solutions of Differential Equations), AMS 250 (Introduction to HighPerformance Computing), as well as one additional course from the following list of approved electives: AMS 260, CMPS 201, CMPS 211, CMPS 242, CMPS 261, and AST 235.

Writing requirements: a substantial and original written body of work related to scientific computing, associated with substantial code development. or substantial modification of existing code, or development of significant computational tools for data analysis. The writeup could take the form of a paper (at least submitted), or an M.S., M.A. or Ph.D. thesis chapter, for instance.

Oral presentation requirement: a presentation of no less than 30 minutes during which the student must demonstrate mastery of the scientific computing component of the submitted written piece of work. This presentation could be the student’s qualifying exam, or the Ph.D. defense, or a separate presentation. The DE adviser must be invited to attend this presentation.
Graduate program in Scientific Computing and Applied Mathematics (SciCAM)
Requirements for a Graduate Degree in Scientific Computing and Applied Mathematics
Required core courses and foundational requirements:
212A Applied Mathematical Methods I
213A Numerical Linear Algebra
213B Numerical Solutions of Differential Equations
214 Applied Dynamical Systems
250 Introduction to parallel computing
Students in the SciCAM program must also demonstrate mastery in the foundations of Scientific Computing and Applied Mathematics, either by producing evidence through undergraduate transcripts, or by taking some or all of the following foundational courses upon entry to the M.S. program: AMS 147 (Computational Methods and Applications), AMS 209 (Foundations of Scientific Computing) and AMS 211 (Foundations of Applied Mathematics).
Electives and capstone requirements:
Approved elective courses: Any regular graduate AMS course not already in the core except AMS 200 and supervised research courses. Elective courses outside of AMS must be approved by the SciCAM graduate director. Note that some upperdivision electives are allowed, bearing in mind that no more than a total of 15 credits of upperdivision courses may be used to satisfy the degree requirements.
Students in the SciCAM program may pursue a Plan I (thesis capstone) or a Plan II (comprehensive examination capstone) curriculum.
Candidates for a Plan I capstone must, in addition to the 25 credits required from core courses, (1) complete one additional 5credit course from the approved elective list, (2) complete 10 credits of supervised research (in the form of AMS 297 or AMS 298 with one of the program faculty), and (3) write a thesis.
The thesis requirements are as follows. Students must submit a thesis proposal to the potential faculty sponsor after completion of all core courses. If the proposal is accepted, the faculty member will become the sponsor and will supervise the research and writing of the thesis project. The project will involve the solution of a problem or problems from the selected area of application. The thesis must consist of at least 30 pages and no more than 60 pages of printed written work and accompanying pertinent figures, consisting of a coherent introduction and presentation of the current state of the field, a clear presentation of the questions raised, of the methodology used to solve them, and a discussion of the results obtained. The thesis will be read by at least two faculty from the AMS Department, one of which must be the student’s adviser. The student will then be required to give a short (20minute) public oral presentation of their thesis, which will be evaluated by the reading committee. The reading committee will assess the quality of both written work and oral presentation in making their recommendation for awarding the M.S. degree to the student.
Candidates for a Plan II capstone must, in addition to the 25 credits required from core courses, (1) complete three additional 5credit courses from the approved elective list, and (2) successfully pass the SciCAM comprehensive examination. The latter takes place in June at the end of the academic year. Students may only take this exam following completion of the last core course. The exam will take the form of a takehome project covering all core and foundational courses.
Accelerated 1Year Program Plan
The expected time to completion of the SciCAM degree program is two years. However, AMS also offers a oneyear accelerated track for interested students who can demonstrate sufficient proficiency in the foundational subjects. Information on the minimum requirements to join the accelerated track can be found on the program website. Requests to join the accelerated track must be made to the graduate director by email no later than August 31 of each year.
4+1 Contiguous Pathway leading to the SciCAM degree program
The 4+1 pathway into SciCAM is an option that allows undergraduates at UC Santa Cruz to (1) take the SciCAM foundational courses during their undergraduate degree in preparation to join the 1year track of the SciCAM program, and (2) apply to SciCAM through a streamlined application process. Undergraduate students currently enrolled in approved programs (currently, the B.A. in mathematics, or the B.S. in computer science, the B.S. in robotics engineering, the B.S. in physics, the B.S. in applied physics and the B.S. in physics [astrophysics]) have the opportunity, any time after the start of their junior year and the end of the fall quarter of their senior year, to join the 4+1 contiguous pathway leading to the SciCAM degree program. Qualified undergraduates from other undergraduate majors may also apply to the pathway and their applications will be considered on a case by case basis.
The requirements for admission into the 4+1 pathway are (1) a GPA in the major of 3.5 or more, and (2) to have taken, or to have a plan to take, at least two of the three SciCAM foundational courses before the end of their senior year. Note that some of the foundational courses are waived depending on the student’s major. Interested students should set up a meeting with the SciCAM adviser to discuss their curriculum plan and fill the application forms. The ultimate deadline for application to the pathway is December 1st of the senior year, although students are encouraged to apply significantly earlier, ideally at the same time as their major declaration.
Students in the pathway who apply to SciCAM through the streamlined application process are not guaranteed admission, although we do expect to admit anyone who has passed all the foundational courses and has maintained a GPA in the major of 3.5 or more. Once accepted into the SciCAM program, students from the pathway will follow the same requirements as any other students in the oneyear track with anticipated graduation in June of their 5th year for Plan II (comprehensive exam track) students, and the end of the summer of their fifth year for Plan I (thesis track) students.
Relationship of SciCAM Masters program and AMS Doctoral Program
Students in the SciCAM M.S. program interested in an academic career will be strongly encouraged to apply to the SAM Ph.D. program. Applications are reviewed in the standard academic cycle, so that students interested in applying to the SAM program are encouraged to discuss this option with the graduate director in the fall of each year.
Transfer Credit
Up to three UCSC courses fulfilling the degree requirements of the SciCAM degree may be taken before beginning the graduate program. However, students will still need to take courses totalling 35 credits as graduate students to satisfy university requirements. Note that this limit does not apply to the foundational requirements, which may all be taken prior to the start of the SciCAM program without penalty.
Up to one course from other institutions may be applied to the M.S. degree course requirements. Petitions should be submitted along with the transcript from the other institution or UCSC Extension. For courses taken at other institutions, copies of the syllabi, exams, and other course work should accompany the petition. Such petitions are not considered until the completion of at least one quarter at UCSC.
Review of Progress
Each year, the faculty reviews the progress of every student in all programs and tracks. Students not making adequate progress toward completion of degree requirements are subject to dismissal from the program (see the Graduate Handbook for the policy on satisfactory academic progress). Also, please refer to specific guidelines on the annual student reviews.
Revised: 07/15/18