Graduate Program Overview

The department offers Ph.D. degrees in both Mathematics and Statistics as well as M.S. degrees in Applied Mathematics and Statistics. We are committed to excellence in research and teaching in a friendly and diverse academic environment.

Our Faculty and Graduate Students

Life in the Department

Options and requirements

Alumni Testimonials

 

Our Faculty and Graduate Students

The permanent faculty teach most of the graduate courses and provide formal and informal supervision of graduate students' careers and progress through their degree program. Students also learn a great deal from interacting with each other both in and out of class. The department has at any time a number of temporary postdoctoral visiting assistant professors. They are usually from one to three years past their Ph.D., and provide a useful bridge between students and areas of current research.

The Department currently has 128 graduate students across 4 degree programs, of whom approximately 27% are women and 52% are from outside the U.S. We embrace the diversity of our department and community, see our statement on equity, inclusion and diversity. The UMass chapter of Association for Women in Mathematics provides resources and networking opportunities for women and women-identifying mathematicians. We actively seek to increase the proportion of women and of minority students in our programs, including those from backgrounds that have been historically under-represented in STEM fields. Foreign students with strong mathematical preparation and a good command of spoken English are encouraged to apply.

 

Life in the Department

The early part of a graduate student's time will be spent on coursework. These courses provide the background necessary for further study in mathematics, and prepare students for the qualifying exams. A diverse group of "topics" courses are offered every year to introduce students to areas in which our faculty are currently working. Students can also take directed reading classes with faculty.

Outside of formal class instruction, there are other ways students can participate in the mathematical life of the department. There are a wide variety of seminars covering the range of pure and applied mathematics and statistics, and students are encouraged to attend them to become more familiar with current research. Talks in the department colloquium are meant for a general mathematical audience, and so are generally more accessible to graduate students, and seminars such as GRASS and Reading Seminar in Algebraic Geometry are specifically aimed at graduate students.

Our graduate students are funded as Teaching Assistants. The Teaching Seminar helps students to become excellent instructors. Graduate students run a Math Club for undergraduate students.

 

Options and requirements

Mathematics and statistics form separate programs within the department; students are admitted either to one program or the other, and permission is required to change programs. The M.S. in pure mathematics is normally only offered to students on the Ph.D. track; we do not admit students whose objective is a masters' in pure mathematics. The applied mathematics M.S. program is formally a separate program, with its own requirements and admissions process. It is possible to apply for both the Ph.D. and M.S. in statistics with the same application.

 

A complete description of the requirements for each of these degrees can be found in the Graduate Handbook.

More information about graduate options in statistics can be found here.

Information on a fifth year M.S. in Statistics for UMass Amherst and other Five College students can be found here.

For more information about the applied math master's program, click here.

 

Alumni Testimonials

Zoi Rapti is currently an Associate Professor in the Department of Mathematics at the University of Illinois at Urbana-Champaign. Her main research focus is on mathematical biology with applications to infectious diseases. She mostly uses differential equations, both ordinary and partial, and sometimes stochastic models to describe these disease systems. Recently she has become interested in the analysis of epidemic time-series from a data-analytical point of view. She still remembers fondly her time at UMass Amherst.

The applied mathematics and analysis professors and, in particular, my advisor Panos Kevrekidis made those five years at UMass both enjoyable and productive. After visiting other graduate programs in the US, I appreciate even more the small size of the graduate program and the attention graduate students were receiving at the UMass program. Having a private office as a graduate student now seems like a true luxury! Having professors that constantly encourage their students to talk to visitors, attend national conferences, write manuscripts and be willing to write recommendation letters and notes to colleagues on their behalf, had long-lasting effects on my career.

Julie Rana enrolled into our PhD program after graduating from Marlboro College, a small liberal arts college in Vermont. Her thesis "Boundary divisors in the moduli space of stable quintic surfaces", written under supervision of Jenia Tevelev, won the department's distinguished thesis award. Dr. Rana's research interests are in algebraic geometry, which studies shapes defined by polynomial equations using an array of algebraic, topological and analytic techniques. After graduating from UMass and teaching for two years as the Math Fellow at Marlboro College, Dr. Rana held a postdoctoral position at the University of Minnesota in Minneapolis. She is now a tenure-track Assistant Professor at Lawrence University in Appleton WI, where she lives with her husband and their twins Isha and Akash, who were born while she was a graduate student.

The best part of grad school at UMass was the community of women graduate students and lecturers in the math department. I honestly don't think I would have finished without their support. I also loved the Valley Geometry Seminar and the Geometry Reading Seminar. Although I was lost most of the time (especially for the first couple of years), I was inspired by the talks at VGS, and really appreciated the opportunity to give talks at the reading seminar.

Bright Antwi Boasiakoinfo-icon enrolled into our regular MS statistics program in 2016 after completing a BS in Actuarial Science in 2015 from the Kwame Nkrumah University of Science and Technology (KNUST), Ghana. He graduated in 2018 and his current position is Actuarial Assistant in New York Life Insurance Company.

One of the things that surprised me the most about the Statistics program at UMass was the welcoming attitude of the faculty, this made it easier to tap into their broad knowledge base and experience. I think it is a thing with the department, everyone I encountered was nice! The program itself has a very strong theoretical foundation of statistics with an opportunity to practice through Statistical Consulting. The consulting bit was extremely helpful. It provided an opportunity to work on real-world problems, and at the same time give back to our community. It was also a great way to learn important soft skills such as communication, professionalism and teamwork through client and team meetings. We were always motivated and empowered to take lead roles in such deliberations. Working in a Life Actuarial role, my training at UMass helps me take a data driven perspective of my day-to-day, in connection with actuarial models, to better understand the mechanics of my job and gain more insight into findings. I feel like I'm my own little consultant at my desk and I owe that to the great work by the faculty and staff at the department!

Holley Friedlander is an Assistant Professor in the Department of Mathematics and Computer Science at Dickinson College. She came to UMass after completing a Bachelor of Arts in Mathematics at the University of Vermont. Her research is in number theory, arithmetic geometry, and combinatorial representation theory.

As a prospective student, I wanted a supportive program with close faculty-student interaction. The wide range of research offerings at UMass relative to the size of the graduate program allowed me to get to know faculty and explore areas through topics courses and seminars prior to choosing a research focus. I continue to reap the benefits of the many opportunities I had as a student to build my professional network, including the entire semester I spent at the Institute for Computational and Experimental Research in Mathematics (ICERM). The teaching assistantship program gave me experience without overloading me with work, and I have no doubt that my work as instructor of record at UMass and involvement with the Undergraduate Math Club (for which I was awarded the Department?s Distinguished Teaching Award) helped me land my first position as a Visiting Assistant Professor at Williams College. In the years since graduation, the number theory group, especially my advisor Paul Gunnells, has continued to provide mentorship and support my career.

Boxuan Cuiinfo-icon enrolled into our Fifth Year MS statistics program in 2010 after graduating from UMass Amherst with an BS in Mathematics. In 2012, he graduated statistics program with an MS and he is currently Data Science Manager at TripAdvisor. Boxuan has a grateful memory of the two years in the MS Statistics program at UMass Amherst.

The MS Statistics program offers a great blend of theoretical and applicable coursework. Throughout the program, students not only get to understand complex theories, but are also able to apply the knowledge to real-world problems. In addition to the standard statistics courses, I find the cross disciplinary research, independent study and the statistical consulting service extremely helpful in preparing me for my future career. From data analytics to presentation/communication skills, from coding to prototyping solutions, I have acquired most essential skills to succeed as my career progresses. As a fresh graduate, these also opened doors for various interview and networking opportunities. During my study in the program, there were also regular colloquium and seminars. Students were encouraged to attend and participate, so that they can stay up-to-date with the latest research. These meetings also broadened my views and field of interests, so that I could better understand my personal aspiration, and choose relevant courses to further my learnings. To conclude, I am grateful for everything the MS Statistics program had offered me. It was a key to my next journey after academia, and I couldn't feel more fulfilled having graduated from UMass Amherst, and from this program.

Dr. Isabelle Beaudryinfo-icon graduated from UMass Amherst with a PhD in Statistics in 2017 and currently she is an Assistant professor, Statistics Department at Pontificia Universidad Católica de Chile.

My journey as a PhD statistics students began in 2011 after practicing as an actuary for a number of years. The Department of Mathematics and Statistics at UMass Amherst was among my top choices of program because of one of the research areas, that is, statistical methodological for Social Sciences. In addition to the research interests match, selecting this Department to pursue doctoral studies turned out to be a very satisfying experience for a wide variety of unexpected reasons.

In addition to the solid, modern and engaging curriculum designed to help students prepare for academic and professional careers, the Department provided me with various opportunities to develop the skills needed to become an academic. Examples of these opportunities included teaching my own courses, participating in the writing of a number of grant proposals, mentoring students, peer reviewing papers, etc. These skills have helped me secure an academic position in one of the highest ranked universities in Latin America.

The Department also promotes interdisciplinary research. For instance, the Computational Social Science Institute is one of the initiatives in which various professors and students from the Department participate. This Institute was of particular interest to me due to its research focus, but more importantly, it provides students exposure to multidisciplinary research which highly contributes to the training of well-rounded academics and professionals.

One of the main reasons for my positive experience is the incredible dedication of the Faculty members and Personnel from the Department. The professors are available to the students and really go the extra miles to make sure optimal conditions are in place for the students to learn. For instance, regular working groups are organized where students may present their work and hear about others’ work in a friendly environment. Also, I will be forever grateful for the extraordinary support I received from everyone in the Department when I suffered a huge loss in my life.

In summary, I am really appreciative of all the amazing individuals in the Department I have met and who all contributed to the successful completion of my studies as well as the quality of the education I received.

Dr. Xiangdong Guinfo-icon enrolled into our MS statistics program in 2010 and graduated with an MS in 2012. Currently he is working as Lead Data Scientist at MassMutual Financial Group. Dr. Gu has a very fruitful experience in the MS Statistics program at UMass Amherst.

The best thing about this program is that it prepares students not only with extensive practical skills that can be directly applied to real world projects but also with solid theoretical background to understand how things work and solve problems in greater depth. The department is very supportive for students to gain practical experience through industry internship, research assistantship and teaching assistantship (I want to highlight that there are many RA/TA opportunities available even for MS students). Professors here are knowledgeable, helpful and approachable. Students out of this program can easily find industry jobs or enter PhD programs for further study.

Ngoc Thai graduated from UMass Amherst with a MS in Statistics in 2013 and she is currently a data scientist at Harvard Pilgrim Health Care where she builds statistical models from large claims and clinical databases to help identify effective interventions for care management programs. More recently she has also started pursuing a PhD in Population Health at Northeastern University where her research interests focus on social determinants of health and health economics.

I owe a lot of what I have been able to do in both industry and advanced study to the Statistics program. In my opinion there are two attributes of the program that were instrumental in preparing me to work as a statistician: the strong curriculum and various faculty-guided research opportunities.

The broad curriculum that spanned both theory and applications provided a solid foundation for my work. I studied not only foundational material such as the theory of statistical inference and generalized linear models, but also more specialized topics such as Bayesian Inference and Network Analysis. I am pleasantly surprised at how often this material ties into to my current projects.

The department also provided me with valuable opportunities to apply what I learned in the classroom to real-world projects, both through my consulting work with the Five College Consortium and other research opportunities such as the MS final project. For my final project I worked on a social network study with two other graduate students under the guidance of two very engaged faculty members, which was a great opportunity to learn from experts in their fields and collaborate with fellow grad students. Not only did this experience help build my ability to conduct a statistical research study, it also improved my interpersonal skills when working in a team, both of which are highly valuable skills for a statistician. I was also able to showcase this research experience during my job interviews which garnered positive feedback from hiring managers.

I feel very fortunate to be able to utilize my statistical training in my work in healthcare research which was something I had hoped to do before the MS degree. This was made possible in large part due to the foundation and direction that the program gave me, and for that I am really thankful.

Kostis Gourgoulias is currently a Research Scientist, assisting the research division at  Babylon Health with the goal of making healthcare affordable and accessible to every human on Earth. His day-to-day work is split between research work on the mathematics behind generative modelling, amortized inference, and machine learning methods, as well as the development of prototypes for the products at Babylon. He graduated in 2017 with a PhD in (applied) mathematics, jointly supervised by Professor Katsoulakis and Professor Rey-Bellet. His thesis, titled “Information Metrics for Predictive Modelling and Machine Learning”, was awarded the distinguished thesis distinction from the Department of Mathematics and Statistics.

I had great fun during my PhD studies in the department. I remember when I first read the acceptance letter (which I still have!) about how intellectually stimulating the studies in Amherst will be -- I now think that is an understatement! Apart from the standard curriculum, I had the opportunity to take classes discussing ideas at the forefront of research in applied mathematics, where we would study and present published work to the rest of the class.

The close collaboration of the Mathematics and Statistics department with other disciplines made venturing out and meeting Professors from other departments simple (the department has strong ties with biostatistics, physics, computational social science, computer science, etc., through the work of the faculty) and the applied math seminar always had interesting and varied topics from the frontier. In fact, I was often given the chance to talk to some of the speakers over lunch or dinner, which gave me unique insights into the life after the PhD -- a lot of my fellow graduate students took advantage of this too!

I felt like the department supported me throughout the creative process of writing the PhD; I had a lot of attention from the computing centre and other faculty. I never felt uncertain about my funding and communication regarding logistical issues, such as travel funding or summer assistantships, was always clear.  When I, with a few friends from biostatistics, engineering, evolutionary biology, and CS, decided to start our own data science group (http://gridclub.io) so that we could practice more in the problems that are of interest in industry, the department had a modern stance and supported the endeavour wholeheartedly.

After the PhD I was able to extend my research around statistical inference in the age of super-expressive models, like deep neural networks, etc.,  and to me, that speaks to the strong background knowledge I acquired by the people in the department and the insightful directions of my advisors. I think it’s a great place for a prospective student to carry out PhD research.

 

Applied Mathematics M.S. Program

This is a two-year professional degree program designed to prepare students in the mathematical sciences for a career in contemporary industry or business. The students receive thorough training in applied mathematics and scientific computing, exposure to mathematics-related subjects in science and engineering, and experience in a group project. The program's graduates have been successful in securing desirable positions with companies ranging from small, local firms to large, international corporations.

 

Director: Markos Katsoulakisinfo-icon

On this page:
Program goals and structure
Application and admissions
Recent Applied Math MS Graduates
Center for Applied Mathematics and Computation

Program goals and structure

The Master's Degree Program in Applied Mathematics is specially designed to prepare graduates for a successful career in today's industrial/business world. Accordingly, the program is structured into the following three components:

  • a core of graduate courses in applied subjects within the Department of Mathematics and Statistics;
  • a selection of advanced courses in other departments;
  • a group project in which an applied scientific problem is undertaken in a colloborative effort.

The graduate courses in the Department concentrate on Analytical Methods, Numerical Methods, and Probability/Statistics. These courses give the student a thorough background in advanced applied mathematics.

Given the interdisciplinary nature of Applied Math, students are encouraged to take courses outside the Department. These are determined depending on each student's interests and preparation. In recent years, they have been chosen from Computer Science, Engineering (Industrial, Mechanical, Electrical), Physics, and Management Science. These courses expose the student to the use of practical mathematical tools by scientists and engineers. Courses taken outside of the department require the director's approval in order to count towards the MS degree.

Students in the program are required to complete at least one course that includes a group project component. The group projects are intended to emulate industrial teamwork on a large, technical problem. Through the combined efforts and diverse talents of the group members, a mathematical model is developed, a computer code is implemented, and a final report is written. In the process, the students learn how to start solving a new and hard problem, how to make a professional presentation of their work, and how to collaborate effectively with their coworkers.

Applications and admissions

Those wishing to be considered for Fall admission should submit all application materials to the Graduate Admissions Office during the preceding Spring. Reviewing of applications will start after the application deadline January 10, with precedence given to those before that date. Later applications are considered provided that openings are available.

All applicants are expected to have a strong undergraduate preparation in mathematics, including advanced calculus, linear algebra, and differential equations. Some exposure to computer science and/or scientific computing is also desirable, as is some knowledge of another area of science or engineering. A Bachelor's Degree in Mathematics, however, is not necessary. Students with undergraduate majors in Physics or Engineering, for instance, and with sufficient mathematical background, are encourage to apply.

The program is able to offer a tuition waiver and a stipend to a limited number of students upon admission. This financial support takes the form of a teaching assistantship in the department. The duties of the students in the Master's Degree Program are usually restricted to grading or consulting for an undergraduate course, although instructing in an elementary course is also possible.

For additional information, contact the Program Director Markos Katsoulakis.

Recent Applied Math MS Graduates

List under constant construction. Recent alumni of the Applied Math MS program may contact Graduate Program Manager Kaitlyn O'Konis (kokonis@umass.edu) to provide the department with their most recent employment information.

2022

2021

2020

Applying to the Graduate Programs

Program Prerequisites

All applicants are expected to have a strong undergraduate preparation in mathematics, including (at least) advanced calculus and linear algebra. Other advanced courses such as real analysis, differential equations, and abstract algebra, are highly recommended. Please see each program's webpage for additional information specific to that program. A list of courses taken, with names of textbooks and instructors, would be a useful supplement to the application.

For the statistics option, an undergraduate degree in statistics is not required. Many of our successful statistics students have a degree in mathematics or another field such as engineering, computer science, biological sciences, together with substantial coursework in mathematics.

Application Process

For detailed instructions regarding the application process, and a link to the online application, please visit the UMass Amherst Graduate School website here.  After submitting an application, you can check whether your materials have been received by logging in to Slate. Instructions are available here.

Please note that applications and all supporting materials must be submitted directly to the UMass Amherst Graduate School using the Slate application, not to the Department of Mathematics and Statistics. The Department will not receive an application to review until the application fee is paid to the Graduate School and all required materials have been received.

Please make sure that your application clearly indicates which degree(s) you wish to apply for. These categories have different criteria for admission, and the applications may even be read by different people.  On the online application, choose "Program: Mathematics", and then make the following make the following selection under "Intended Degree(s)":

  • Ph.D. in Mathematics (this includes students interested in applied areas): choose Mathematics (Ph.D.)
  • Ph.D. in Statistics: choose Statistics (Ph.D.)
  • M.S. in Statistics at Amherst campus: choose Statistics - Statistics (M.S.) Amherst
  • M.S. in Statistics at Newton/Mt. Ida satellite campus and 100% remote M.S. degree in Statistics: choose  Statistics - Statistics (M.S.) Newton
  • M.S. in Applied Mathematics: choose Mathematics - Applied Mathematics (M.S.)

Applicants to the Ph.D. in mathematics can indicate specific areas or subjects they are interested in under Sub-field or specialization. Do not put statistics in this space — instead use the statistics items from the pull-down menus as explained above if you are intending to apply to a Statistics degree program.

A complete application to the Graduate School consists of the following (see here for complete details and instructions):

  • Completed UMass Graduate School Application
  • Application fee (Fee waiver information available here)
  • Official transcripts from all undergraduate and graduate institutions attended
  • At least three letters of recommendation
  • A personal statement explaining the student's interest in pursuing graduate studies in mathematics or statistics
  • The following exam scores (all score reports should be sent by ETS using the code for UMass, which is 3917):
    • The GRE mathematics subject exam score is required for applicants to the Mathematics PhD program.  It is not required for applicants to the PhD in Statistics.
    • The GRE general exam scores are strongly recommended, but not required.
    • UPDATE for Admission in Fall 2024: Because of the continuing impact of COVID-19 on ETS test administration, the GRE subject test will  not be required in order to apply to the PhD program in Mathematics. Optionally, prospective candidates are welcome to submit their subject GRE scores in case they have taken the Mathematics test.
  • For international applicants:
    • English Language Proficiency scores. Most students submit TOEFL scores, but other test scores can be used to demonstrate English proficiency. Please review the International Applicants page for details regarding accepted test scores and eligibility for a waiver from the English proficiency requirement.

 

Additional Information

Frequently Asked Questions

Financial Aid & Assistantship Information

Department of Mathematics & Statistics Graduate Student Handbook

Deadline

To be considered for a fall teaching assistantship, all application materials should be submitted to the Graduate School by January 10. Most offers for Amherst campus programs are made by mid-April. Applications for both the in-person and 100% remote M.S. Statistics degree program at Newton Mt. Ida are accepted on a rolling basis until June 30 (domestic students) and May 31 (international students) for the fall semester. Except in unusual circumstances, we do not admit students for the Spring semester.

The University of Massachusetts has signed the Council of Graduate School's resolution, which states that students offered admission including financial support for the Fall semester have at least until April 15th to decide whether to accept or decline their offer. 

Axioms - Handbook for Graduate Students

Please follow the link below to access the most recent Graduate Student Handbook.

 

 

Financial Aid & Housing

Financial Aid

Applying for an MS or PhD degree also serves as an application for financial support (except for the MS in Statistics at Mount Ida).  All admitted PhD students in the department are offered funding.  Some MS students are also offered funding.  Most of this support is in the form of Teaching Assistantships (TA), which includes a waiver of tuition and 9-month stipend. TAs are graduate student employees and part of the Graduate Employee Organization (GEO) union. Union benefits include 95% cover of health insurance and University health fees as well as other excellent benefits. Graduate students are responsible for other fees. Teaching Assistantship duties may involve teaching one section of an elementary course each semester, or equivalent work connected with a large lecture course. Summer teaching opportunities may also be available. 

For Fall 2023, new Ph.D. students will receive a TA stipend of at least $25,247 for a 20-hour per week assistantship. New Teaching Assistants in M.S. programs will normally receive a stipend of at least $18,616 and somewhat reduced duties, approximately 15 hours per week. 

A number of advanced Ph.D. students hold Research Assistantships attached to grants held by particular faculty members. 

Additional Resources for financial aid:

 

Student Housing

ON CAMPUS: As of Spring 2020, there is no designated on-campus housing specifically for Graduate Students. On-campus housing for Graduate Students is expected to become available beginning Fall 2024 at Fieldstone. Family housing is available for students who are single, married, or in a domestic partnership and living with dependent child(ren) and/or dependent relative(s). Detailed information and access to the application is available here.

OFF CAMPUS: Students looking for housing in Amherst and the surrounding area are encouraged to utilize the UMass Off Campus Housing Search. The website includes current listings, roommate search, and other resources. The Off Campus Student Life office is another helpful resource for students looking to live off campus.

 

 

Graduate Degree in Statistics

Overview
M.S. Degree in Statistics
The Fifth Year M.S. in Statistics
M.S. in Statistics at Newton Satellite Campus (Boston Area), Completely Flexible Program (In Person/Remote or 100% Remote), Evening Degree
Ph.D. Degree in Statistics
Data Science Certificate (possible to earn completely remotely/online)
Related Info

Overview of the Statistics Graduate Program

 

The Department of Mathematics and Statistics, offers graduate degrees in statistics at the MS and PhD levels. Note that until 2023, these degrees were granted as concentrations of the corresponding math degrees.  This page summarizes the main features of the Statistics degrees, and contains the most up-to-date information. The information on this page supersedes the information in the Axioms (Handbook), which are in the process of being updated.

The M.S. degree provides students with training in statistical applications, statistical computing and theory, preparing them for statistics and data science careers in industry, government, educational organizations, consulting firms, health care and research organizations, or for moving on to a Ph.D. in Statistics or Biostatistics. The Ph.D. degree provides a combination of theory and application preparing students for positions in academia, industry or government. The Certificate in Statistical and Computational Data Science is a joint program with Statistics and Computer Science. Each of these programs is described in more detail below.

M.S. Degree in Statistics

The M.S. program in Statistics is designed to prepare students for statistics and data science positions in industry, government, educational organizations, consulting firms, health care and research organizations. It also serves as a basis for future work towards a Ph.D. in Statistics or Biostatistics. This program is designed to provide the student with a background in basic theory along with experience in various applications, including computational aspects. As part of their training, students will receive comprehensive exposure to popular statistical software packages. In addition to courses offered within the department, the program allows room for the students to take statistics courses in other departments on campus.

Prerequisites: Students entering the M.S. program are expected to have had Linear Algebra and Calculus up through Multivariate Calculus (this is typically covered by a three-semester sequence in U.S. schools).

The requirements for the M.S. degree in Statistics involve coursework, a project and consulting or qualifying exams.

Courses

The student must complete 30 hours of coursework with grades of C or better, including at least 24 hours with grades of B or better (pass or fail grades cannot be used to satisfy this requirement). In addition, the student must have at least an overall B average.

The required 30 hours must include

  1. Stat 625: Regression Modeling,
  2. Stat 607-608: Probability and Mathematical Statistics I, II,
  3. Stat 535: Statistical Computing,
  4. At least five other courses which are either Statistics courses numbered 526 or above, from within the department, or some courses outside the department numbered 500 and above subject to prior approval by the Statistics coordinator (pre-approved list below).

 

Consulting or Basic Exam

Students completing the M.S. program in Statistics are required to either complete at least one credit of statistical consulting (typically STAT 598C) or pass two of three basic exams we offer: applied statistics, probability, and statistics, which are based on ST625 and ST535, ST607, and ST608, respectively. The Basic Exam is given twice a year, in January and in August.

Project

The project is completed under the guidance of a faculty member. This project must have prior approval of the Statistics coordinator and involves 3 credit hours which may be used to satisfy the 30 hour coursework requirement. The project can take many forms; an expository report on a particular area, an examination of methods through simulations or a detailed statistical analysis of real data. A final report is required. This requirement is typically satisfied by the successful completion of the project seminar course Stat 691P.

The Fifth Year M.S. in Statistics

This section explains how a UMass Amherst or Five College student can complete the M.S. degree in Statistics in a fifth year.

Entering the fifth year M.S. in Statistics

In order to enter the fifth year M.S. in Statistics program, students need to

  1. Start taking graduate courses in the fall of their senior years, typically Stat 535, Stat 607, Stat 608, and/or Stat 625. Since
    1. a maximum of 6 credits can be counted toward both the M.S. in Statistics and the baccalaureate degree, and
    2. at most 12 credits of graduate work taken while enrolled as an undergraduate (6 double-counted and an additional 6 taken above and beyond undergraduate degree requirements) may be counted toward the M.S. in Statistics,
    students who would like to pursue the fifth year M.S. in Statistics should prepare to take at least 126 total credits (120 for the baccalaureate degree, up to 6 double-counted graduate credits and an additional 6 graduate credits) by the end of their senior year. (Note:  taking fewer than 12 credits is permitted to pursue the M.S. in Statistics, but taking the full 12 makes for the smoothest path to completing the MS in 1 year). Information about the transfer of credits from undergraduate to graduate is available on the Graduate School's website.
  2. apply in their senior years to the regular M.S. program in statistics program by following instructions here.

Finishing the fifth year M.S. degree in Statistics

After being accepted into the program, students

  1. need to take additional 18 credits and fulfill the requirements for the regular M.S. degree in Statistics in the fifth year
  2. may use courses taken as an undergraduate to fulfill the requirements of the M.S. degree in Statistics, although no more than 12 credits may be counted toward the M.S. degree in Statistics. For example, if a senior takes all four Stat 535, Stat 607, Stat 608, and Stat 625 graduate courses and does not apply more than 6 credits to requirements for their baccalaureate degree, they can be used to satisfy requirement for the M.S. degree in Statistics. 
  3. are not obligated to finish the program in the fifth year, although financial assistantship, if any, is only guaranteed for the fifth year

Please note that students who are interested in the fifth year M.S. program in Statistics should start planning during the fall of the their junior year and contact the Coordinator of the Statistics Program if there are any questions. To process the transfer of credits from undergraduate to the graduate degree, students must submit a Transfer of Credit form. This may be submitted to Kaitlyn O'Konis, Graduate Program Manager, at kokonis@umass.edu.

M.S. in Statistics at Newton Satellite Campus (Boston Area), Completely Flexible (In Person/Remote or 100% Remote)

-For information regarding this program, please see the following link.

-A 100% Remote Option is available for this program.

http://people.math.umass.edu/~conlon/statmtida/

-Note: non-degree students can register for graduate Statistics courses at Newton Mount Ida starting one week before the beginning of classes each semester. See:

http://www.umass.edu/graduate/apply/non-degree-students

Ph.D. Degree in Statistics

The Ph.D. degree in Statistics prepares students for academic positions or positions in Academia, or as applied statisticians in industry or government. Entering students are expected to have had Linear Algebra, Calculus and Advanced Calculus. Typically an incoming student in the Ph.D. program in Statistics will have had an introductory course or two in Statistics at the undergraduate level. Student seeking the Ph.D. degree in Statistics must complete the following: coursework, qualifying exams, language requirement and dissertation.

Coursework

  1. The student must complete successfully 36 hours of coursework, including Math 523 (or Math 623, or Math 605), Stat 535, 607, 608, 625, 705, and 725.
  2. The student must also complete five elective courses, including two 600 level statistics courses, and 3 courses of the student’s choice, which require prior approval by the statistics coordinator (pre-approved list below).

Qualifying Exams

There are two tiers of exams, Basic and Advanced, which are intended to measure a student's overall mastery of standard material. The exams are administered during the week preceding each semester (August and January).

Basic Exams: The student must pass three Basic Exams at the Ph.D. level: the Applied Statistics exam, and the Basic Probability and Basic Statistics exams, which cover the material from Stat 535 and Stat 625, Stat 607 and Stat 608 respectively.

Advanced Exams: The student must pass the Advanced Exam in advanced statistics and the oral literature-based exam. The advanced statistics exam version I is based on advanced topics in Stat 607 and Stat 608, and topics from Stat 705. The advanced statistics exam version II is based on advanced topics in Stat 607 and Stat 608, and topics from Stat 725. The two versions are offered in alternate years depending which of Stat 705 and Stat 725 is offered in a year.

For the literature-based exam, students need to choose a topic from the list of topics in the Axioms and form an exam committee that includes the primary faculty of that topic and two secondary faculty. Students are then given reference papers on the chosen topic to read. The exam is in the form of oral presentation and responding questions in front of the exam committee. A student may select a non-standard exam topic, in which case, the student must have the agreement of their committee members on the topic and the reading list.

In order to take the literature-based exam, a student is responsible for forming an exam committee by the end of September for a January exam, or by the last day of spring classes for an August exam. Decisions on passing the exam are by unanimous consent of the exam committee. A student who does not pass will have one more chance to pass the literature-based exam. The second attempt may be on the same or a different topic.

Dissertation

After passing the Advanced Exam, the student becomes a Ph.D. in Statistics candidate. The student must write a satisfactory dissertation and pass a final oral examination (primarily a defense of the dissertation), and must satisfy all other requirements of his or her dissertation committee. The student is required to register for a minimum of 18 dissertation credits.

Data Science Certificate (possible to earn completely remotely/online)

The Certificate in Statistical and Computational Data Science is offered jointly between Statistics and Computer Science. The Certificate can be completed in one year and requires 5 courses total, with a minimum of 2 courses in each of Statistics and Computer Science.

It is possible to earn the Certificate completely remotely/online; please see the following link: https://people.math.umass.edu/~conlon/statmtida/datascience.html

For more information on the Certificate, please see the following link:

https://ds.cs.umass.edu/academics/certificate-data-science

Approved Courses Outside the Department

The following courses are pre-approved to count toward STAT MS and PhD degrees (as specified) without additional prior approval.  Please contact the Statistics Coordinator for pre-approval of any other courses outside the department.

Toward MS Degree only:
    * PHYSICS 597D (ST- Topics in Statistics and Data Analysis)     * COMPSCI 514 (Algorithms for Data Science), CS 590V (Data Visualization and Exploration)     * Biostats 597D, Biostats 650, Biostat 690Z  

Toward MS or PhD degree:     * CS589 (Machine Learning), CS 682 (Neural Networks), CS 688 and CS690OP, CS 690D, CS 696DS, CS 611 (Advanced Algorithms), COMPSCI 688 (PROBABILISTIC GRAPHICAL MODELS),     * Biostat 683/Biostat 690B (intro to causal inference), Biostat 690T (Applied Statistical Genetics), Biostat 730, Biostat 740 (Analysis of Mixed Models Data), Biostat 743 (Categorical), Biostat 748 (Applied Survival Analysis), Biostat 749 (Clinical Trials), Biostat 750 (Applied Statistical Learning), Biostat  790A,     * Psych 891FM     * PoliSci 797TA (Text as Data)

 

Related Information

 

 

Financial Aid and Housing

Statistical Consulting Center

Computing Facilities

Recent Courses

 

 

Recent Graduate Courses

The following is a list of some graduate courses that have been offered over the last five years. In general, courses numbered 600-699 are basic graduate courses preparing students to take the basic part of the qualifying exams, while 700-799 are more advanced courses. We have listed 500-599 courses that are most often taken by students in the Applied Math Masters and Statistics Master’s programs, but 500-599 courses are open to graduate students and advanced undergraduate students. The exact topics covered by each of these classes may vary from year to year. Statistics courses are listed separately from mathematics courses.

Courses marked with an asterisk (*) are special topics courses, designed by the instructor to lead graduate students to deeper study of a particular area that might lead to thesis research. The other courses are offered at least every other year, and many are offered every year.

Courses were held at the UMass Amherst Campus unless otherwise noted.

Detailed course descriptions can be found on the Course Descriptions page, by selecting the desired semester from the drop-down box, then clicking the change button.

 

Recent Mathematics Courses

Semester Course Number Course Title
Spring 2023 Math 612 Algebra II
Spring 2023 Math 621 Complex Analysis
Spring 2023 Math 624 Real Analysis II
Spring 2023 Math 646 Applied Math & Math Modeling
Spring 2023 Math 652 Int Numerical Analysis II
Spring 2023 Math 672 Algebraic Topology
Spring 2023 Math 690STA* Math Theory of Machine Learning Part II
Spring 2023 Math 691Y Applied Math Project Sem
Spring 2023 Math 697CM* Combinatorial Optimization
Spring 2023 Math 697U* Stochastic Processes and Applications
Spring 2023 Math 704 Topics in Geometry II
Spring 2023 Math 790STA* Abelian Varieties
Spring 2023 Math 790STB* Probabilistic Methods in Nonlinear Evolution Equations
Spring 2023 Math 797W* Algebraic Geometry
Fall 2022 Math 605 Probability Theory I
Fall 2022 Math 611 Algebra I
Fall 2022 Math 623 Real Analysis I
Fall 2022 Math 645 ODE and Dynamical Systems
Fall 2022 Math 651 Numerical Analysis I
Fall 2022 Math 671 Topology I
Fall 2022 Math 691T S-Teaching in University
Fall 2022 Math 691Y Applied Math Project Seminar
Fall 2022 Math 697MA* Mathematical Theory of Machine Learning
Fall 2022 Math 703 Topics in Geometry I
Fall 2022 Math 797EC (now Math 714) Elliptic Curves
Fall 2022 Math 797P (now Math 706) Stochastic Calculus
Fall 2022 Math 797RT (now Math 717) Intro to Representation Theory
Fall 2022 Math 797TT* Information Theory and Optimal Transport
Spring 2022 Math 612 Algebra II
Spring 2022 Math 621 Complex Analysis
Spring 2022 Math 624 Real Analysis II
Spring 2022 Math 646 Applied Math & Math Modeling
Spring 2022 Math 652 Int Numerical Analysis II
Spring 2022 Math 672 Algebraic Topology
Spring 2022 Math 691Y Applied Math Project Sem
Spring 2022 Math 697AC* Analytic Combinatronics
Spring 2022 Math 697U (now Math 606) Stochastic Processes & Appl
Spring 2022 Math 705 Symplectic Toplogy
Spring 2022 Math 708 Complex Algebraic Geometry
Spring 2022 Math 725 Intro Functional Analysis I
Spring 2022 Math 797LD* Low Dimensional Toplogy
Fall 2021 Math 605 Probability Theory 1
Fall 2021 Math 611 Algebra I
Fall 2021 Math 623 Real Analysis I
Fall 2021 Math 645 ODE and Dynamical Systems
Fall 2021 Math 651 Numerical Analysis I
Fall 2021 Math 671 Topology I
Fall 2021 Math 691T S-Teaching in Univ
Fall 2021 Math 691Y Applied Math Project Seminar
Fall 2021 Math 703 Topics in Geometry I
Fall 2021 Math 713 Intr-Algebraic Number Theory
Fall 2021 Math 718 Lie Algebras
Fall 2021 Math 731 Partial Differential Equations I
Fall 2021 Math 797E* ST-Homological Algebra
Fall 2021 Math 797NS* ST-Networks and Spectral Graph Theory
Spring 2021 Math 621 Complex Analysis
Spring 2021 Math 624 Real Analysis II
Spring 2021 Math 646 Applied Math and Math Modeling
Spring 2021 Math 652 Int Numerical Analysis II
Spring 2021 Math 672 Algebraic Topology
Spring 2021 Math 612 Algebra II
Spring 2021 Math 691Y Applied Math Project Seminar
Spring 2021 Math 697FA* Math Foundations of Probabilistic Artificial Intelligence II
Spring 2021 Math 697U (now Math 606) Stochastic Processes and Applications
Spring 2021 Math 704 Topics in Geometry II
Spring 2021 Math 797RM* Moduli Spaces / Reprsnt Theory
Spring 2021 Math 797W (now Math 707) Algebraic Geometry
Fall 2020 Math 605 Probability Theory I
Fall 2020 Math 611 Algebra I
Fall 2020 Math 623 Real Analysis I
Fall 2020 Math 645 ODE and Dynamical Systems
Fall 2020 Math 651 Numerical Analysis I
Fall 2020 Math 671 Topology I
Fall 2020 Math 691T S-Teaching in Univ C
Fall 2020 Math 691Y Applied Math Project Seminar
Fall 2020 Math 697B Introduction to Riemann Surfaces
Fall 2020 Math 697PA* Math Foundations/ProbabilistAI
Fall 2020 Math 703 Topics in Geometry I
Fall 2020 Math 731 Partial Differential Equations I
Fall 2020 Math 797EC (now Math 714) Elliptic Curves
Fall 2020 Math 797RT (now Math 717) Intro/Representation Theory
Spring 2020 Math 534H Intro to Partial Differential Equations
Spring 2020 Math 612 Algebra ll
Spring 2020 Math 624 Real Analysis ll
Spring 2020 Math 646 Applied Math & Math Modeling
Spring 2020 Math 652 Numerical Analysis ll
Spring 2020 Math 672 Algebraic Topology
Spring 2020 Math 691Y Applied Math Project Seminar
Spring 2020 Math 697SS* Sums of Squares
Spring 2020 Math 697U (now Math 606) Stochastic Processes & Applications
Spring 2020 Math 705 Symplectic Topology
Spring 2020 Math 708 Complex Algebraic Geometry
Spring 2020 Math 725 Functional Analysis
Spring 2020 Math 797D* Topology & Geometry of Singular Spaces
Spring 2020 Math 797DS* Infinite Dimensional Integral Systems
Spring 2020 Math 797P Stochastic Calculus
Fall 2019 Math 532H Nonlinear Dynamics & Chaos w/ Applications
Fall 2019 Math 611 Algebra l
Fall 2019 Math 621 Complex Analysis
Fall 2019 Math 623 Real Analysis l
Fall 2019 Math 645 ODE & Dynamical Systems
Fall 2019 Math 651 Numerical Analysis l
Fall 2019 Math 671 Topology l
Fall 2019 Math 691T Teaching in University
Fall 2019 Math 691Y Applied Math Project Seminar
Fall 2019 Math 697SG* Symmetric functions and representation theory of the symmetric group
Fall 2019 Math 703 Topics in Geometry l
Fall 2019 Math 718 Lie Algebras
Fall 2019 Math 731 Partial Differential Equations l
Spring 2019 Math 534H Intro to Partial Differential Equations
Spring 2019 Math 612 Algebra ll
Spring 2019 Math 624 Real Analysis ll
Spring 2019 Math 672 Algebraic Topology
Spring 2019 Math 691Y Applied Math Project Seminar
Spring 2019 Math 697AM (now Math 646) Applied Mathematics & Math Modeling
Spring 2019 Math 697CM* Combinatorial Optimization
Spring 2019 Math 697U Stochastic Processes & Applications
Spring 2019 Math 704 Tpcs In Geometry II
Spring 2019 Math 725 Functional Analysis
Spring 2019 Math 797AS* Algebraic Surfaces
Spring 2019 Math 797CV* Calculus of Variations
Spring 2019 Math 797P (now Math 706) Stochastic Calculus
Spring 2019 Math 797W Algebraic Geometry
Fall 2018 Math 532H Nonlinear Dynamics & Chaos w/ Applications
Fall 2018 Math 611 Algebra l
Fall 2018 Math 621 Complex Analysis
Fall 2018 Math 623 Real Analysis l
Fall 2018 Math 645 ODE & Dynamical Systems
Fall 2018 Math 651 Numerical Analysis l
Fall 2018 Math 671 Topology l
Fall 2018 Math 691Y Applied Math Project Seminar
Fall 2018 Math 697CP* Convex Polytopes
Fall 2018 Math 703 Topics in Geometry l
Fall 2018 Math 731 Partial Differential Equations l
Fall 2018 Math 797DE* Dynamical Systems and Ergodic Theory
Fall 2018 Math 797EC (now Math 714) Elliptic Curves
Fall 2018 Math 797RT (now Math 717) Representation Theory
Spring 2018 Math 534H Intro to Partial Differential Equations
Spring 2018 Math 612 Algebra ll
Spring 2018 Math 621 Complex Analysis
Spring 2018 Math 624 Real Analysis ll
Spring 2018 Math 672 Algebraic Topology
Spring 2018 Math 691Y Applied Math Project Seminar
Spring 2018 Math 697AM (now Math 646) Applied Mathematics & Math Modeling
Spring 2018 Math 697U Stochastic Processes & Applications
Spring 2018 Math 697WA* Nonlinear Waves & Applications in Continua and Lattices
Spring 2018 Math 705 Symplectic Topology
Spring 2018 Math 708 Complex Algebraic Geometry
Spring 2018 Math 797DC* Derived Categories

Recent Statistics Courses

Semester Course Number Course Title
Spring 2023 Stat 608 (offered at Amherst and Mount Ida campus) Mathematical Statistics II
Spring 2023 Stat 610 (Mount Ida campus) Bayesian Statistics
Spring 2023 Stat 690STA* Applied Semiparametric Regression
Spring 2023 Stat 697MV* (Mount Ida campus) Applied Multivariate Statistics
Spring 2023 Stat 697V* (Mount Ida campus) Data Visualization
Fall 2022 Stat 607 Mathematical Statistics I (Offered at Amherst and Mt. Ida campus)
Fall 2022 Stat 625 Regression Modeling (Offered at Amherst and Mt. Ida campus)
Fall 2022 Stat 691P Project Seminar (Newton-Mt. Ida campus)
Fall 2022 Stat 697BD* Biomed and Health Data Analysis
Fall 2022 Stat 697L* Categorical Data Analysis (Newton-Mt. Ida campus)
Fall 2022 Stat 697SC* Statistical Consulting: Bringing theory to practice
Fall 2022 Stat 725 Eastmtn Theory and Hypothesis Testing I
Spring 2022 Stat 608 (offered at both Amherst and Mt. Ida campuses) Mathematical Statistics II
Spring 2022 Stat 697DS (Newton-Mt. Ida) Statistical Methods for Data Science
Spring 2022 Stat 697MV* (Newton-Mt.Ida) Applied Multivariate Statistics
Spring 2022 Stat 697TS* Time Series Analysis
Spring 2022 Stat 697V* (Newton-Mt.Ida) Data Visualization
Fall 2021 Stat 607 (offered at both Amherst and Mt. Ida campuses) Mathematical Statistics I
Fall 2021 Stat 610 Bayesian Statistics
Fall 2021 Stat 625 (offered at both Amherst and Mt. Ida campuses) Regression Modeling
Fall 2021 Stat 691P (Newton-Mt. Ida campus) S-Project Seminar
Fall 2021 Stat 697L (Newton-Mt. Ida campus) ST-Categorical Data Analysis
Fall 2021 Stat 697ML ST-Stat Machine Learning
Fall 2021 Stat 697TS (Newton-Mt. Ida campus) ST-Time Series Analysis and Appl
Fall 2021 Stat 705 Linear Models I
Fall 2021 Stat 797S ST-Estimation/Semi Non Parametric Models
Spring 2021 Stat 608 (offered at Amherst and Mount Ida campuses) Mathematical Statistics II
Spring 2021 Stat 697D* Applied Statistics and Data Analysis
Spring 2021 Stat 697DS* (Mount Ida campus) Statistical Methods for Data Science
Spring 2021 Stat 697MV* (Mount Ida campus) Applied Multivariate Statistics
Spring 2021 Stat 697V* (Mount Ida campus) Data Visualization
Fall 2020 Stat 607 (offered at Amherst and Mount Ida campuses) Mathematical Statistics I
Fall 2020 Stat 610 Bayesian Statistics
Fall 2020 Stat 625 (offered at Amherst and Mount Ida campuses) Regression Modeling
Fall 2020 Stat 691P S-Project Seminar
Fall 2020 Stat 697BD* Biomedical and Health Data Analysis
Fall 2020 Stat 697L* (Mount Ida campus) Categorical Data Analysis
Fall 2020 Stat 697ML* Statistical Machine Learning
Fall 2020 Stat 697TS* (Mount Ida campus) Time Series Analysis and Application
Fall 2020 Stat 725 Estimation Theory and Hypothesis Testing I
Spring 2020 Stat 526 (Mount Ida Campus) Design Of Experiments
Spring 2020 Stat 598C Statistical Consulting Practicum
Spring 2020 Stat 608 (offered at Amherst and Mount Ida Campuses) Mathematical Statistics ll
Spring 2020 Stat 691P Project Seminar
Spring 2020 Stat 697DS* (Mount Ida Campus) Statistical Methods/Data Science
Spring 2020 Stat 697L* Categorical Data Analysis
Spring 2020 Stat 697TS* Time Series Analysis and Appl
Spring 2020 Stat 797S* Estimation/SemiNonParametMD
Fall 2019 Stat 535 (offered at Amherst and Mount Ida Campuses) Statistical Computing
Fall 2019 Stat 598C Statistical Consulting Practicum
Fall 2019 Stat 605 (now Math 605) Probability Theory l
Fall 2019 Stat 607 (offered at Amherst and Mount Ida Campuses) Mathematical Statistics l
Fall 2019 Stat 610 Bayesian Statistics
Fall 2019 Stat 625 (offered at Amherst and Mount Ida Campuses) Regression Modeling
Fall 2019 Stat 697BD* Biomedical and Health Data Analysis
Fall 2019 Stat 697ML* Statistical Machine Learning
Fall 2019 Stat 705 Linear Models 1
Spring 2019 Stat 597G* Intro to Statistical Learning
Spring 2019 Stat 598C Statistical Consulting Practicum
Spring 2019 Stat 608 Mathematical Statistics ll
Spring 2019 Stat 691P Project Seminar
Spring 2019 Stat 697D* Applied Statistics & Data Analysis
Fall 2018 Stat 535 Statistical Computing
Fall 2018 Stat 598C Statistical Consulting Practicum
Fall 2018 Stat 605 (now Math 605) Probability Theory l
Fall 2018 Stat 607 Mathematical Statistics l
Fall 2018 Stat 625 Regression Modeling
Fall 2018 Stat 697S* Statistical Network Inference
Fall 2018 Stat 725 Estimation Theory & Hypothesis Testing
Fall 2018 Stat 797N* Non-parameteric Regression for Data Analysis
Spring 2018 Stat 526 Design of Experiments
Spring 2018 Stat 598C Statistical Consulting Practicum
Spring 2018 Stat 608 Mathematical Statistics ll
Spring 2018 Stat 691P Project Seminar
Spring 2018 Stat 797L* Mixture Models
Fall 2017 Stat 535 Statistical Computing
Fall 2017 Stat 597L* Dynamic Linear Models

Sample Qualifying Exams

Basic Exams

Advanced Exams

Advanced Statistics Version II

Applied Mathematics Practice Problems

Stochastics Practice Problems

Topology Practice Problems

Old Exams (no longer offered)