Akhtar Khan
Professor
School of Mathematics and Statistics
College of Science
585-475-6367
Office Hours
T/Th: 10:45-11:45, M: 9:30-10:30, or by appointment
Office Location
Office Mailing Address
Gosnell Hall 2308, 85 Lomb Memorial Drive, Rochester, NY 14623
Akhtar Khan
Professor
School of Mathematics and Statistics
College of Science
Education
MS, Technical University Kaiserslautern (Germany); Ph.D., Michigan Technological University
Bio
- Joined School of Mathematical Sciences at RIT in 2008.
- I advise undergraduate and graduate students in research in applied and computational mathematics.
585-475-6367
Areas of Expertise
Inverse Problems
Uncertainty Quantification
Optimal Control
Numerical Optimization
Parameter Identification
PDE Constrained Optimization
Set-valued Optimization
Variational and Quasi-Variational Inequalities
Regularization
Elasticity Imaging
Select Scholarship
- Uncertainty Quantification in Variational Inequalities: Theory, Numerics, and Applications. Joachim Gwinner, Baasansuren Jadamba, Akhtar A. Khan, Fabio Raciti, monograph, Taylor & Francis (2021)
- Introduction to Set-Valued Optimization. Akhtar A. Khan, Christiane Tammer, Constantin Zalinescu, monograph, Springer (2014)
- Deterministic and Stochastic Optimal Control and Inverse Problems. Baasansuren Jadamba, Akhtar A. Khan, Stanislaw Migorski, and Miguel Sama (editors), Taylor & Francis (2021)
- Variational Analysis and Set Optimization. Akhtar A. Khan, Elisabeth Koebis, Christiane Tammer (editors), Taylor & Francis (2019)
- B. Jadamba, A. A. Khan, M. Sama, H-J. Starkloff, Chr. Tammer, A Convex Optimization Framework for the Inverse Problem of Identifying a Random Parameter in a Stochastic Partial Differential Equation, SIAM/ASA J. Uncertainty Quantification, 9 (2), 922-952 (2021)
- S. Zeng, S. Migórski, A. A. Khan, Nonlinear Quasi-Hemivariational Inequalities: Existence and Optimal Control, Siam J. Control Optim., 59, 1246-1274 (2021)
Currently Teaching
MATH-241
Linear Algebra
3 Credits
This course is an introduction to the basic concepts of linear algebra, and techniques of matrix manipulation. Topics include linear transformations, Gaussian elimination, matrix arithmetic, determinants, vector spaces, linear independence, basis, null space, row space, and column space of a matrix, eigenvalues, eigenvectors, change of basis, similarity and diagonalization. Various applications are studied throughout the course.
MATH-498
Independent Study in Mathematical Sciences
1 - 3 Credits
This course is a faculty-guided investigation into appropriate topics that are not part of the curriculum.
MATH-602
Numerical Analysis I
3 Credits
This course covers numerical techniques for the solution of nonlinear equations, interpolation, differentiation, integration, and matrix algebra.
MATH-625
Applied Inverse Problems
3 Credits
Most models in applied and social sciences are formulated using the broad spectrum of linear and nonlinear partial differential equations involving parameters characterizing specific physical characteristics of the underlying model. Inverse problems seek to determine such parameters from the measured data and have many applications in medicine, economics, and engineering. This course will provide a thorough introduction to inverse problems and will equip students with skills for solving them. The topics of the course include existence results, discretization, optimization formulation, and computational methods.
MATH-799
MATH GRADUATE Independent Study
1 - 3 Credits
Independent Study