A student must present their dissertation work within 4 years of advancing to candidacy. Time extensions may be made on a case-by-case basis.

All Math and Math Stat students must contact the Office of Graduate Studies (OGS) when scheduling their final oral (defense) exams. The OGS will assist you with the processing of departmental forms and the necessary paperwork for the Office of the Registrar and the Graduate School. Please contact Trystan Denhard and Jemma Natanson (Kirwan 1106/1108)  /  for further details. 

You must attend a graduation information session, which are advertised each semester. 

Helpful Links:

    Admission to candidacy for the doctoral degree is granted by the Graduate School upon the recommendation of the MATH Graduate Committee. A student must be admitted to candidacy within five years after admission to the doctoral program and at least six months before the date on which the doctoral degree will be conferred. Before a student applies for admission to candidacy he or she must have:

    • passed two written qualifying exams at the Ph.D. level and completed the four required courses with a grade of B or higher;
    • maintained a 3.00 or better GPA in all formal course work;
    • passed the Oral Candidacy Examination.

    Please contact the Office of Graduate Studies to request information on preliminary exams, candidacy forms and the application for candidacy (j and ).


    The Oral Candidacy Examination: The candidacy examination serves as an oral test of the detailed preparation of a student in the area of specialization. The candidate should have a deep understanding of relevant research literature in the field and the skills to carry out the research for the dissertation. The examination is taken before a student embarks on serious dissertation research. The examination assumes further advanced course work beyond that required for the qualifying exams. 

    Planning the Exam: To plan the examination, the student, with the help and approval of the prospective dissertation advisor, must prepare a prospectus for the examination. This prospectus defines the primary and related areas to be covered in the examination. These areas should be identified by course citations, literature citations, tables of contents, or other appropriate means. The prospectus should be filed with the Graduate Office before the examination is scheduled, and should also record the proposed format for the examination. Typical formats for the examination are either a seminar-type presentation by the student (or possibly two such talks) on one or more recent research papers, followed by questions from the committee on the presentation and related background material, or else a more traditional oral examination on subjects or courses listed in the prospectus.


    Examination Committee: The examination committee is appointed by the Graduate Director (or if the Graduate Director is unavailable for an extended period, his or her designated representative) upon recommendation of the student's prospective dissertation advisor. The Graduate Director may if necessary consult with one or more field committee chairs in the area of specialization. The examination committee must consist of at least three members, at least one (usually the prospective dissertation advisor) representing the area in which the student plans to specialize. Usually all three of these will be faculty members from the Mathematics Department, but when there is a good academic reason, the student can petition the Graduate Committee to allow one to be from a related department (such as physics or computer science) or an outside institution (such as another university, NASA, NIH, NIST, NCHS, etc.). Disputes regarding the makeup of the examination committee will be referred to the Graduate Committee. Each committee member must agree to abide by the prospectus for the examination.


    Possible Outcomes: Upon completion of the examination, the examination committee decides to pass, fail, or defer a decision on the student. In the last-named case, the manner in which the decision is to be resolved must be specified in the report of the committee. The distinction between "fail" and "defer a decision" is based on the committee's evaluation of the probability of successful completion of the Ph.D. degree.


    Repeating the Exam: Upon failure, the Candidacy Examination may be repeated only once. Exceptions to this rule are granted only under extraordinary circumstances and upon petition to the Graduate Committee.

    (revised August, 2016) 

    Graduate Students in the MATH, STAT, or AMSC programs are expected to make reasonable progress toward their degrees. In the following charts, a supported student is one who receives funding from the Mathematics Department, either as a TA, GA or as a fellow.

    Notes:
    1. The time limits for support by the Mathematics Department apply even if the student is not supported by the Mathematics Department for some intervening period.
    2. In the absence of exceptional circumstances, students who do not pass all of their written qualifying exams by the end of the January cycle of their THIRD year will be dropped from the program.
    3. In some cases, upon admission, the Graduate Director can negotiate a slower timetable. Examples include part-time students, and students that would benefit from taking 400-level courses in their first year.
    4. Since the requirements in the Scientific Computation concentration of the AMSC program are somewhat different, there is a separate set of charts for students in this concentration.
    5. For students in the Applied Mathematics concentration of the AMSC program, "qualifying exam" may in some cases be replaced by its equivalent in other departments. In BMGT, this may mean two written examinations (since they only cover one semester of material each), and in CMSC this may mean "qualifying requirement".                                                                   6. Please note that it is a University requirement that a student be registered during the semester that they plan on completing their degree/graduating.

    To convert credit hours to units:

    • 899, units = 18 × credits
    • 799, units = 12 × credits
    • 600 level, units = 6 × credits
    • 400 level, units = 4 × credits

    For the AMSC requirements for Applied Mathematics and Applied Statistics, see the AMSC website

    Progress expected of students in the MATH and STAT Ph.D. programs

    By the end of:

    Ideal progress

    To maintain support

    To remain in program

    1st Year

    Prepare for and pass 2 qualifying exams

    Pass 12 credit hours with at least a 2.75 gpa

    Pass 24 units each term with at least a 2.75 gpa

    2nd Year

    Finish the 4 courses and choose field/advisor

    Pass 1 exam by January, and pass 24 credit hours, at least 15 at 600 level, with 3.0 gpa

    Pass 24 units each term with at least a 3.0 gpa

    3rd Year

    Advance to candidacy, start working on dissertation

    Pass all exams and course requirements by January

    Pass all exams and course requirements by January

    4th Year

    Make good progress toward dissertation

    Reach candidacy by March 1

    Pass 24 units each term with at least 3.0 gpa

    5th Year

     Finish dissertation, publish paper(s) and apply for jobs

    Make good progress toward finishing dissertation

    Pass 24 units each term with at least a 3.0 gpa and reach candidacy

    6th Year

     

    Last year of support eligibility

    Have a further 4 years to complete Ph.D.

    Progress expected of full-time students in M.A. or M.S. programs (non-thesis option)

    By end of:

    Expected progress

    1st Year

    Prepare for and pass 1 qualifying exam and complete at least 12 credits at the 600 level with 3.0 gpa

    2nd Year

    Complete course requirements and complete qualifying exams (exams must be completed by January). Must be registered during final semester.

    (International students usually need to complete the Masters degree in 2 years because of visa requirements.)

    Progress expected of part-time students in M.A. or M.S. programs

    By end of:

    Ideal progress

    To remain in program

    1st Year

    Prepare for and pass 1 qualifying exam (non-thesis option), or complete at least 12 credits at the 600 level with 3.0 gpa (thesis option)

    Pass 48 units each term with at least a 3.0 gpa

    2nd Year

    Complete course requirements and qualifying exam requirements (non-thesis option). Choose advisor and begin serious work on thesis (thesis option)

    Pass 48 units each term with at least 3.0 gpa

    3rd Year

    Complete scholarly paper (non-thesis option) or thesis, receive degree

    Pass 48 units each term with at least 3.0 gpa, pass 2 exams (non-thesis option) and complete course requirements.

    4th Year and beyond

     

    Pass final exam by end of 4th year. Complete M.A. or M.S. by end of 5th year. Must be registered during final semester.

    Progress expected of students in AMSC Scientific Computation M.S. program

    By end of:

    Ideal progress

    To remain in program

    1st Year

    Pass AMSC 660-663, plus one Core Science Course, with 3.5 gpa

    Pass 48 units each term with at least a 3.0 gpa

    2nd Year

    Complete core requirements, with 3.5 gpa

    Pass 48 units each term with at least 3.0 gpa

    3rd Year

    Complete electives and scholarly paper (non-thesis option) or thesis, receive degree

    Pass 48 units each term with at least 3.0 gpa. Must be registered during final semester. 

    Archives: F2011-S2012 F2012-S2013 F2013-S2014 F2014-S2015 F2015-S2016 F2016-S2017 F2017-S2018 F2018-S2019 F2019-S2020 F2021-S2022 F2022-S2023 

    • High order positivity-preserving entropy stable discontinuous Galerkin discretizations

      Speaker: Jesse Chan (Rice University) - https://profiles.rice.edu/faculty/jesse-chan

      When: Tue, September 12, 2023 - 3:30pm
      Where: Video https://umd.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=b2682da3-4e16-451e-93d2-b08501795c20&start=450
    • Fast and Accurate Boundary Integral Methods for Two-Phase Flow with Surfactant

      Speaker: Michael Siegel (New Jersey Institute of Technology) - https://people.njit.edu/faculty/misieg

      When: Tue, September 26, 2023 - 3:30pm
      Where: Video https://umd.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=94e6c1a5-c63b-494c-88ea-b089017d03d0
    • A Low Rank Tensor Approach for Nonlinear Vlasov Simulations

      Speaker: Jingmei Qiu (University of Delaware) - https://jingmeiqiu.github.io/

      When: Tue, October 3, 2023 - 3:30pm
      Where: Video: https://umd.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=eb06dce2-9eeb-4f6f-baf2-b0930115f592
    • Riemannian optimization and Riemannian Langevin Monte Carlo for PSD fixed rank constraints

      Speaker: Xiangxiong Zhang (Purdue University) - https://www.math.purdue.edu/~zhan1966/

      When: Tue, October 10, 2023 - 3:30pm
      Where: Video: https://umd.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=ed687d23-01aa-43b9-99fa-b09b009076b6
    • Hydrodynamics of Liquid Crystals on Curved Thin Films: Modeling and Numerics

      Speaker: Lucas Bouck (Carnegie Mellon University) - https://lbouck.github.io/

      When: Tue, October 17, 2023 - 3:30pm
      Where: Video: https://umd.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=76bd642a-c58d-42aa-9a61-b0a601108db9
    • Dynamical low-rank methods for high-dimensional collisional kinetic equations

      Speaker: Jingwei Hu (University of Washington) - https://jingweihu-math.github.io/webpage/

      When: Tue, October 24, 2023 - 3:30pm
      Where: Video: https://umd.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=434a41d5-a296-4b51-a454-b0a601108db8
    • Numerical methods for hydrodynamics at small scales

      Speaker: Sean Carney (George Mason University ) - https://math.gmu.edu/~scarney6/

      When: Tue, October 31, 2023 - 3:30pm
      Where: Video: https://umd.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=e4f592ef-ae46-4bb6-ad3c-b0c30155d802
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      Speaker: Donsub Rim (Washington University in St. Louis) - https://dsrim.github.io/

      When: Tue, November 7, 2023 - 3:30pm
      Where: Video: https://umd.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=1e3026f5-d13e-4e64-8aa5-b0c30155d807
    • Optimization on Matrix Manifolds, with Applications in Data Science

      Speaker: Pierre-Antoine Absil (University of Louvain) - https://sites.uclouvain.be/absil/

      When: Tue, November 14, 2023 - 3:30pm
      Where: Kirwan Hall 3206
    • Jaywalking at the Intersection of Machine Learning and Interesting Math

      Speaker: Michael Puthawala (South Dakota State University) - https://www.sdstate.edu/directory/michael-puthawala

      When: Tue, November 21, 2023 - 3:30pm
      Where: Video: https://umd.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=c3071a68-22fa-4e5c-b86d-b0c30155d802
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      Speaker: Ke Chen (University of Maryland College Park) - https://math.umd.edu/~kechen/

      When: Tue, November 28, 2023 - 4:30pm
      Where: Brin Math Center Colloquium Room
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      Speaker: Rohit Khandelwal (George Mason University) - https://rohitedu.github.io/

      When: Tue, December 5, 2023 - 3:30pm
      Where: Kirwan Hall 3206
    • Waveform Inversion via Reduced Order Modeling

      Speaker: Liliana Borcea (University of Michigan) - https://websites.umich.edu/~borcea/

      When: Tue, January 30, 2024 - 3:30pm
      Where: Video: https://umd.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=d13c17ba-7eaf-42a5-80ac-b12a013c35ee
    • Error control in a diffusion map-based PDE solver

      Speaker: Maria Cameron (University of Maryland) - https://www.math.umd.edu/~mariakc/

      When: Tue, February 6, 2024 - 3:30pm
      Where: Video: https://umd.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=47566412-eba1-4592-9459-b12a013c35d5
    • Coarsening and mean field control of volatile droplets

      Speaker: Hangjie Ji (North Carolina State University) - https://hji5.math.ncsu.edu/

      When: Tue, February 13, 2024 - 3:30pm
      Where: Kirwan Hall 3206
    • Nonlinear scientific computing in machine learning and applications

      Speaker: Wenrui Hao (Pennsylvania State University) - https://sites.psu.edu/whao/

      When: Tue, February 20, 2024 - 3:30pm
      Where: Video: https://umd.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=a2bf1a52-28f2-4724-8579-b12a013c35d5
    • Quantum algorithms for linear differential equations

      Speaker: Dong An (University of Maryland College Park) - https://dong-an.github.io/

      When: Tue, February 27, 2024 - 3:30pm
      Where: Video: https://umd.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=5597b928-6293-43f2-b864-b12a013c35f0
    • Data-adaptive RKHS regularization for learning kernels in operators

      Speaker: Fei Lu (Johns Hopkins University) - https://math.jhu.edu/~feilu/

      When: Tue, March 5, 2024 - 3:30pm
      Where: Kirwan Hall 3206
    • Approximation of differential operators on unknown manifolds and applications

      Speaker: John Harlim (Pennsylvania State University) - https://jharlim.github.io/myhomepage/

      When: Tue, March 12, 2024 - 3:30pm
      Where: Kirwan Hall 3206
    • Numerically stable methods for electromagnetic scattering in layered media

      Speaker: Mike O'Neil (New York University) - https://cims.nyu.edu/~oneil/

      When: Tue, March 26, 2024 - 3:30pm
      Where: Kirwan Hall 3206
    • Differentiable physics for turbulence closure modeling from data

      Speaker: Romit Maulik (Pennsylvania State University) - https://romit-maulik.github.io/

      When: Tue, April 2, 2024 - 3:30pm
      Where: Kirwan Hall 3206
    • An effective discretization scheme for singular integral operators on surfaces

      Speaker: James Bremer (University of Toronto) - https://www.math.ucdavis.edu/~bremer/

      When: Tue, April 9, 2024 - 3:30pm
      Where: Kirwan Hall 3206
    • A Domain Decomposition Method for Solution of a PDE-Constrained Generalized Nash Equilibrium Model of Biofilm Community Metabolism

      Speaker: Daniel B. Szyld (Temple University) - https://www.math.temple.edu/~szyld/

      When: Tue, April 16, 2024 - 3:30pm
      Where: AVW 1146 (ISR)
    • Solution of Forward and Inverse Problems for Extreme-Scale 1-km-Resolution Earth Mantle Models

      Speaker: Johann Rudi (Virginia Tech) - https://math.vt.edu/people/faculty/rudi-johann.html

      When: Tue, April 23, 2024 - 3:30pm
      Where: Kirwan Hall 3206
    • An exact and efficient algorithm for Basis Pursuit Denoising via differential inclusions

      Speaker: Gabriel Provencher Langlois (Courant Institute of Mathematical Sciences, NYU) - https://gabrielpl.com/

      When: Tue, April 30, 2024 - 3:30pm
      Where: Kirwan Hall 3206