Basca Jadamba
Professor
School of Mathematics and Statistics
College of Science
Associate Head, Applied and Computational Mathematics
585-475-3994
Office Hours
Fall 2024: Wednesdays 10:30-12:00, and by appointment
Office Location
Office Mailing Address
Gosnell Hall 2312, 85 Lomb Memorial Drive, Rochester, NY 14623
Basca Jadamba
Professor
School of Mathematics and Statistics
College of Science
Associate Head, Applied and Computational Mathematics
Education
BS, National University of Mongolia (Mongolia); MS, University of Kaiserslautern (Germany); Ph.D., University of Erlangen-Nuremberg (Germany)
Bio
- At School of Mathematics and Statistics 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
Inverse Problems
Continuous Optimization
Partial Differential Equations
Numerical Analysis
Finite Element Methods
Mathematical Modeling
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
Multivariable Calculus
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-412
Numerical Linear Algebra
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-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-790
Research & Thesis
0 - 9 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.