Karamatou Yacoubou Djima
Assistant Professor of Mathematics
Applied mathematician interested in problems at the intersection of harmonic analysis and machine learning, including analysis on graphs, diffusion geometry, and image processing.
My research interests lie broadly in the fields of applied harmonic analysis and machine learning. One of the main ideas that I exploit is the representation of data (for example, human voice, social media trends, or, more related to my work, images) into relatively simple building blocks that harness hidden structures in the data to extract relevant information and often reduce the computational cost of algorithms. A few subareas of applied harmonic analysis and machine learning included in my past and current work are diffusion geometry, analysis on graphs, composite wavelets, and image processing. I have enjoyed applying tools from these domains on projects such as early diagnosis of autism spectrum disorder via extraction of biological markers present in placenta images, detection of age-related macular degeneration via identification of anomalies in retinal images, motion detection in animated images for Pixar.
I have taught a wide range of courses, from calculus and differential equations to more advanced courses, Fourier and wavelet analysis, numerical analysis, and recently, machine learning in the EDGE Program. I enjoy teaching mathematics courses at all levels as they offer varied audiences and, thus, exciting pedagogical challenges and rewards. In each class, my goal is to make students reach a competency level that yields confidence, genuine enjoyment, and deep appreciation for the mathematical thought process. Ideally, this is accompanied by curiosity about mathematics and its incredible applications. Whenever possible, I draw on my applied mathematics background to introduce certain concepts or make them more relevant through carefully picked examples.
Education
- B.S., CUNY College of Staten Island
- M.S., University of Maryland-College Park
- Ph.D., University of Maryland-College Park
Current and upcoming courses
Calculus IIA
MATH120
This course is a variant of MATH 116 for students who have a thorough knowledge of the techniques of differentiation and integration, and familiarity with inverse trigonometric functions and the logarithmic and exponential functions. It includes a rigorous and careful treatment of limits, sequences and series, Taylor's theorem, approximations and numerical methods, Riemann sums, improper integrals, l'Hôpital's rule, and applications of integration.
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Selected Topics Tpc: Numerical Analysis
MATH349
Topic for Fall 2026: Numerical Analysis. This course is an introduction to the mathematical foundations and computational techniques of numerical analysis, the study of algorithms for solving problems of continuous mathematics using discrete approximations. Numerical analysis is one of the oldest and most influential areas of mathematics. It offers powerful tools for approximations and simulations of complex systems. The course will focus on both the theory and practice of numerical methods, their accuracy, efficiency, and limitations. The first part of the course will cover continuous problems, including solving nonlinear equations, numerical differentiation and integration, and polynomial interpolation. Numerical linear algebra topics will follow, including Gaussian elimination, LU decomposition, and iterative methods for large systems. The final part of the course will introduce numerical methods for differential equations: Euler's and Runge-Kutta methods. Throughout the course, we will analyze the stability, convergence, and computational complexity of algorithms. We will also apply them to practical problems using software such as MATLAB.