Basca Jadamba Headshot

Basca Jadamba

Associate Professor

School of Mathematical Sciences
College of Science

585-475-3994
Office Hours
Fall 2022-2023: Wednesdays 12:30-2:30, Fridays 11:00-12:00, and by appointment
Office Location
Office Mailing Address
Gosnell Hall 2272, 85 Lomb Memorial Drive, Rochester, NY 14623

Basca Jadamba

Associate Professor

School of Mathematical Sciences
College of Science

Education

BS, National University of Mongolia (Mongolia); MS, University of Kaiserslautern (Germany); Ph.D., University of Erlangen-Nuremberg (Germany)

Bio

  • At School of Mathematical Sciences since 2008.
  • Advises undergraduate and graduate students in research
  • Teaches courses at undergraduate and graduate levels.
  • Faculty advisor of RIT Student Chapter of Association for Women in Mathematics. 

585-475-3994

Areas of Expertise

Select Scholarship

  • B. Jadamba, A. A. Khan, M. Sama, H-J. Starkloff, Ch. 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), https://doi.org/10.1137/20M1323953
  • J. Gwinner, B. Jadamba, A. Khan, F. Raciti. Uncertainty Quantification in Variational Inequalities: Theory, Numerics, and Applications, monograph, CRC Press, (2021), https://doi.org/10.1201/9781315228969
  • B. Jadamba, A. A. Khan, M. Richards, M. Sama, A convex inversion framework for identifying parameters in saddle point problems with applications to inverse incompressible elasticity, Inverse Problems, 36 (7), 074003, 25 pages (2020)
  • B. Jadamba, A. A. Khan, M. Richards, M. Sama, Ch. Tammer, Analyzing the role of inf-sup condition in inverse problems for saddle point problems with application in elasticity imaging, Optimization,(2020), https://doi.org/10.1080/02331934.2020.1789128 
  • B. Jadamba, M. Pappalardo, F. Raciti. Efficiency and vulnerability analysis for congested networks with random data, J. Optim. Theory Appl. (2018), https://link.springer.com/article/10.1007/s10957-018-1264-y
  • B. Jadamba, A. Khan, G. Rus, M. Sama, B. Winkler. A New convex inversion framework for parameter identification in saddle point problems with an application to the elasticity imaging inverse problem of predicting tumor location. SIAM J. Appl. Math., 74(5), 1486-1510 (2014), https://doi.org/10.1137/130928261
  • Ch. Eck, B. Jadamba,  P. Knabner. Error estimates for a finite element discretization of a phase field model for mixtures. SIAM J. Num. Anal., 47, 4429-4445 (2010), https://doi.org/10.1137/050637984
     

Currently Teaching

MATH-219
3 Credits
This course is principally a study of the calculus of functions of two or more variables, but also includes the study of vectors, vector-valued functions and their derivatives. The course covers limits, partial derivatives, multiple integrals, and includes applications in physics. Credit cannot be granted for both this course and MATH-221.
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-326
3 Credits
This course provides an introduction to boundary value problems. Topics include Fourier series, separation of variables, Laplace's equation, the heat equation, and the wave equation in Cartesian and polar coordinate systems.
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-412
3 Credits
This course covers numerical techniques for the solution of systems of linear equations, eigenvalue problems, singular values and other decompositions, applications to least squares, boundary value problems, and additional topics at the discretion of the instructor.
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-712
3 Credits
This is an advanced course in numerical methods that introduces students to computational techniques for solving partial differential equations, especially those arising in applications. Topics include: finite difference methods for hyperbolic, parabolic, and elliptic partial differential equations, consistency, stability and convergence of finite difference schemes.
MATH-790
0 - 9 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.