Akhtar Khan Headshot

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

Personal Links
Areas of Expertise

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. Mig√≥rski, A. A. Khan, Nonlinear Quasi-Hemivariational Inequalities: Existence and Optimal Control, Siam J. Control Optim., 59, 1246-1274 (2021)

Currently Teaching

MATH-241
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-381
3 Credits
This course covers the algebra of complex numbers, analytic functions, Cauchy-Riemann equations, complex integration, Cauchy's integral theorem and integral formulas, Taylor and Laurent series, residues, and the calculation of real-valued integrals by complex-variable methods.
MATH-411
3 Credits
This course covers numerical techniques for the solution of nonlinear equations, interpolation, differentiation, integration, and the solution of initial value problems.
MATH-495
1 - 3 Credits
This course is a faculty-directed project that could be considered original in nature. The level of work is appropriate for students in their final two years of undergraduate study.
MATH-498
1 - 3 Credits
This course is a faculty-guided investigation into appropriate topics that are not part of the curriculum.
MATH-625
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-702
3 Credits
The course covers the solutions of linear systems by direct and iterative methods, numerical methods for computing eigenvalues, theoretical and numerical methods for unconstrained and constrained optimization, and Monte-Carlo simulation.
MATH-799
1 - 3 Credits
Independent Study

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