1. What are the most important factors in admission decisions?
Transcript (do you have a strong enough background to succeed in grad school at Maryland?), Letters of recommendation, GRE Subject score. If you are an international applicant, the TOEFL (or IELTS or PTE) score is important.

2. How many students are accepted?
In a recent year, 190 people applied, 30 were offered admission, and 14 accepted our offer.

3. Do you admit students for the Spring semester?
No.

4. Do you admit students who are aiming only for a Masters degree?
Generally, we admit students only for PhD degree. If you are considering going for a PhD degree, you should apply directly to the PhD program. You should not apply to the Masters program with the intention of getting that degree and then deciding whether to continue. If you are in the PhD program, it is possible to stop at the Masters degree.

5. What score do I need on the GRE Subject test?
There is no minimum score, since other factors also play a role, but your chances decrease significantly if your score is much below 700.

6. Do I need the GRE Subject test?
Occasionally someone is accepted without the GRE Subject test. However, we accept only about 16% of applicants, so someone with missing information is at a severe disadvantage. (Please note, the GRE General Test is required.)

7. What grade point average do I need?
You should have at least a 3.0 grade point average, with mostly A's in your mathematics courses.

8. What score do I need on the TOEFL?
For the TOEFL, we want a total score of at least 105, with at least 23 on the speaking portion. Occasionally, we accept someone who misses by a point, but this is rare. We never accept someone with a total score below 100.

We now accept IELTS or PTE in place of the TOEFL. For the IELTS, we want a score of 7.4 or higher. For the PTE, we want a score of 72 or higher.

9. I am a student from a non-English-speaking country who received a Masters degree in the US. The Graduate School has waived the TOEFL requirement. Do I still need the TOEFL?

Yes. Because our graduate students are supported by Teaching Assistantships, it is required that their English skills are higher than the minimum required to attend courses.

10. I have a transcript from my institution that is encrypted. What should I do?

Do not upload an encrypted transcript to the application. It will upload as a black document, and will not be able to be used for evaluation. Please print the document out, scan it, then upload the scanned version. 

11. What if I don't know what area I'm interested in?
Am I allowed to change areas when I get to graduate school? You probably have some idea what direction you might be headed, for example, analysis, algebra, logic, geometry, etc. It's best to list one or two areas, since "undecided" could result in your application not being considered as carefully by admission committee members who are assigned applicants in their field to review. You can of course change your area of interest while in graduate school. There are areas of mathematics that you might currently not know anything about, but after taking a course you decide that's what you really want to do. The first two years of grad school are usually spent expanding your horizons and figuring out what area you want to pursue.

12. What if some of my application materials arrive late?
Our job is to select the best students, not to enforce arbitrary deadlines. However, when material arrives after a file has been reviewed, sometimes it gets missed. So, a few days late is often no problem, but a few weeks late could hurt your chances.

13. What should be on the Personal Statement and/or Supplementary Application Essay?
The personal statement is not like the essay that got you accepted or rejected at the undergraduate school of your dreams. We don't care about literary style, etc. We want to know what interests you mathematically and what interests you about the University of Maryland. For example, which professors might you consider working with? If you have done a research project, say something about it. If there are parts of math that you found exciting, say so. If there is something in your record that is going to raise questions (for example, it's been ten years since you were an undergraduate), answer those questions if appropriate. If this applies to you, you can also discuss your community involvement or service, leadership, or overcoming social, economic or physical barriers. It is acceptable to submit the same text for both the Essay and Statement of Purpose.

14. What courses should I take to be ready for grad school?
For almost all areas of math, we want to see real analysis (epsilon-delta proofs, compact sets, Cauchy sequences, etc.) and a theoretical linear algebra course (not just row operations on matrices; there should be linear independence, diagonalization theorems, minimal polynomials). For pure math, a good course in abstract algebra is recommended. For more applied areas, some partial differential equations or complex analysis is good. Generally, the more math, the better. If you are at a school that offers grad courses, then taking a grad course (and doing well) increases your chances. If your school doesn't offer grad courses, then take as many upper-level courses as possible.

15. How do I support myself during graduate school?
All of our accepted students (except a few who are supported by employers) are offered support by the University, usually in the form of a Teaching Assistantship. This covers your tuition and pays a salary of around $23000 per academic year. This is enough to live on, but you don't get rich. Teaching Assistants teach 4 to 6 hours per week, depending on the course. The total time commitment is around 12 to 15 hour per week. Some more advanced grad students are supported as Research Assistants, with the money usually coming from the advisor's research grant.

16. When are accepted students notified?
Acceptances are made from mid-February through April 15. Here's what happens: Since we support all accepted students with Teaching Assistantships, we need to end up with a predetermined number of students, with a very small error term. Some acceptances are sent out in mid February, but no one can be required to answer until April 15. Therefore, nothing happens for several weeks. Everyone else starts getting nervous, especially since they see on grad cafe web sites that we've made some acceptances. Finally, around April 1, these accepted students either get rejected by their higher choices and accept our offer, or they have been accepted by a higher choice and are trying to get up the courage to say no to us. (If you end up in this latter category, please be courageous. The graduate director is getting besieged with emails from people who want your place.) When it looks like we'll be below our quota, we send more acceptances. (If it looks like we have too many, we hide from the budget people and hope for rejections.) Around April 10, we have filled about half of our positions, with many offers awaiting responses. By some miracle, over the last five days, the rejections and acceptances come and go, and we hit our projected number.

17. How long does it take to get a PhD?
The median time is around 5.5 years.

18. What percentage of the students complete the program?
Our recent estimate is that around 75% of entering students will complete their PhD.

19. What do students do after they graduate? (besides celebrate)
See the list of recent jobs.

For application FAQs (technical questions), please see: https://gradschool.umd.edu/admissions/admissions-requirements

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
  • Low-rank PINNs for model reduction of nonlinear hyperbolic conservation laws

    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
  • Towards efficient deep operator learning for forward and inverse PDEs: theory and algorithms

    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
  • The Obstacle Problem: Pointwise Adaptive Finite Element Method and Optimal Control

    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