Course Atlas

Graduate MATH Courses

MATH511 Analysis I Credits: 3
Content: An introduction to fundamental analytic concepts including: The complex number system, geometry and topology of the complex plane, analytic functions, conformal mappings, complex integration, and singularities.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC E406 TuTh      10:00AM - 11:15AM Shanshuang Yang 18
MATH515 Numerical Analysis I Credits: 3
Content: Course will cover fundamental parts of numerical linear algebra including matrix factorizations, solution of linear systems and least-squares problems, the calculation of eigenvalues and eigenvectors, and basic notions on iterative methods for large-scale matrix problems. Issues pertaining to conditioning and numerical stability will be thoroughly analyzed. We will also point out and use links to other mathematical and computer science disciplines such as mathematical modelling, computer architectures and parallel computing.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC N302 TuTh      1:00PM - 2:15PM James Nagy 20
MATH517 Iterative Methods for Linear Systems Credits: 3
Content: TBA
Texts: TBA
Assessments: TBA
Prerequisites: Prerequisite MATH 516
Section Location Meeting Time Instructor Enrollment (max)
1 MSC E406 MW      8:30AM - 9:45AM Yuanzhe Xi 15
MATH521 Algebra I Credits: 3
Content: Finite groups, Sylow theorems, principal ideal domains and unique factorisation domains, structure theorem for modules over principal ideal domains and consequences in linear algebra, tensor products, symmetric and exterior algebras, the functors Ext and Tor.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC E406 MW      2:30PM - 3:45PM Parimala Raman 15
MATH531 Graph Theory I Credits: 3
Content: The course will cover some fundamental concepts in structural and extremal graph theory, including matchings, connectivity, graph planarity, graph colorings, flows, minors and topological minors, Hamiltonian cycles and paths, Ramsey Theory, and Szemeredi's regularity lemma.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC E408 MW      10:00AM - 11:15AM Liana Yepremyan 15
MATH543 Algebraic Topology I Credits: 3
Content: Homotopy theory, the fundamental group, free products of groups with amalgamation, Van Kampen's Theorem, covering spaces, classification of surfaces, classifying spaces, higher homotopy groups
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC E406 MW      1:00PM - 2:15PM Suresh Venapally 15
MATH545 Introduction to Differential Geometry I Credits: 3
Content: An introduction to Riemannian geometry. The main goal is an understanding of the nature and uses of curvature, which is the local geometric invariant that measures the departure from Euclidean geometry. No previous experience in differential geometry is assumed, and we will rely heavily on pictures of surfaces in 3-space to illustrate key concepts.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC E408 TuTh      11:30AM - 12:45PM Yiran Wang 20
MATH550 Functional Analysis Credits: 3
Content: An introduction to concepts and applications including: metric and normed spaces, Hilbert and Banach spaces, linear operators and functionals, compactness in metric and normed spaces, Fredholm's solvability theory, spectral theory, calculus in metric and normed spaces, selected applications.
Texts: TBA
Assessments: TBA
Prerequisites: Math 511, Math 512.
Section Location Meeting Time Instructor Enrollment (max)
1 MSC E406 MW      11:30AM - 12:45PM David Borthwick 15
MATH577R Seminar in Combinatorics Credits: 3
Content: The seminar in combinatorics is a research seminar for students and faculty. It runs weekly, and features speakers from outside Emory who come to talk about topics of interest to the Emory faculty.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC E408 F      3:00PM - 4:00PM Dwight Duffus 20
MATH578R Seminar in Algebra Credits: 1-9
Content: Research topics in algebra of current interest to faculty and students.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC W301 Tu      4:00PM - 5:00PM David Zureick-Brown 20
MATH579R Seminar in Analysis Credits: 3
Content: TBA
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC E406 Th      4:00PM - 6:00PM 20
MATH590 Teaching Seminar Credits: 3
Content: This seminar will concentrate on effective teaching techniques in mathematics. Topics included will include: General advice for new TA's. General advice for International TA's. Students will present several practice lectures over different levels of material. They will receive practice on quiz and test preparation. Syllabus information on courses most likely to be taught by new TA's will be supplied. General professional development information will also be included.
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC E408 F      11:00AM - 12:00PM Bree Ettinger
Juan Villeta-Garcia
20
MATH789 Topics in Analysis: Seminar on Computational Mathematics for Data Science Credits: 3
Content: Despite the widespread and increasing use of deep learning in a wide range of areas, including (but not limited) to data science, the development of its mathematical and computational foundations remains an urgent and very active field of research. Contributing to a better understanding and improved algorithms is one of the focus areas of Emory's NSF-funded Research Training Group (RTG) on Computational Mathematics for Data Science at Emory. This RTG seminar seeks to expose graduate students, research-oriented undergraduate students, and early career researchers to recent developments of deep learning and its applications in data science (e.g., large-scale data analysis, inverse problems, data assimilation) and scientific machine learning (e.g., solving partial differential equations, optimal control problems). The overarching goal of this seminar is to help participants develop new research ideas and design projects that further advance computational methods for deep learning. We are particularly interested in mathematically sound approaches that help increase its robustness (e.g., learning from small data sets, adversarial attacks), scalability (e.g., more efficient architectures, learning algorithms, ...), and fairness (e.g., bias mitigation, ...).
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
1 MSC N304 F      3:00PM - 4:00PM Lars Ruthotto 20
MATH789 Seminar in Scientific Computing Credits: 1
Content: TBA
Texts: TBA
Assessments: TBA
Prerequisites: TBA
Section Location Meeting Time Instructor Enrollment (max)
2 MSC W201 F      12:30PM - 1:30PM Yuanzhe Xi