Akhtar Khan Headshot

Akhtar Khan


School of Mathematics and Statistics
College of Science

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


School of Mathematics and Statistics
College of Science


MS, Technical University Kaiserslautern (Germany); Ph.D., Michigan Technological University


  • Joined School of Mathematical Sciences at RIT in 2008. 
  • I advise undergraduate and graduate students in research in applied and computational mathematics.   


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

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.
1 - 3 Credits
This course is a faculty-guided investigation into appropriate topics that are not part of the curriculum.
3 Credits
This course covers numerical techniques for the solution of nonlinear equations, interpolation, differentiation, integration, and matrix algebra.
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.
1 - 3 Credits
Independent Study

In the News

  • December 12, 2023

    Vietnamese professor sitting at a desk with an open book and laptop.

    RIT hosts Abel Visiting Scholar

    RIT recently hosted Abel Visiting Scholar Nguyen Thi Van Anh, lecturer at Hanoi National University of Education in Vietnam. The Abel Visiting Scholar Program allows professional mathematicians based in developing countries to visit an international research collaborator for one month.