Basca Jadamba
Professor, Applied Mathematics
School of Mathematics and Statistics
College of Science
Associate Head, Applied and Computational Mathematics
585-475-3994
Office Hours
Spring 2025: W 2PM-4PM, F 10AM-12PM, and by appointment
Office Location
Office Mailing Address
Gosnell Hall 2312, 85 Lomb Memorial Drive, Rochester, NY 14623
Basca Jadamba
Professor, Applied Mathematics
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-421
Mathematical Modeling
3 Credits
This course explores problem solving, formulation of the mathematical model from physical considerations, solution of the mathematical problem, testing the model and interpretation of results. Problems are selected from the physical sciences, engineering, and economics.
MATH-495
Undergraduate Research in Mathematical Sciences
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-622
Mathematical Modeling I
3 Credits
This course will introduce graduate students to the logical methodology of mathematical modeling. They will learn how to use an application field problem as a standard for defining equations that can be used to solve that problem, how to establish a nested hierarchy of models for an application field problem in order to clarify the problem’s context and facilitate its solution. Students will also learn how mathematical theory, closed-form solutions for special cases, and computational methods should be integrated into the modeling process in order to provide insight into application fields and solutions to particular problems. Students will study principles of model verification and validation, parameter identification and parameter sensitivity and their roles in mathematical modeling. In addition, students will be introduced to particular mathematical models of various types: stochastic models, PDE models, dynamical system models, graph-theoretic models, algebraic models, and perhaps other types of models. They will use these models to exemplify the broad principles and methods that they will learn in this course, and they will use these models to build up a stock of models that they can call upon as examples of good modeling practice.
MATH-702
Numerical Analysis II
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-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.