Wanjiku Ikonye

Dedicated and passionate PhD candidate in Mathematics with a strong foundation in both applied and pure mathematics. Seeking opportunities to contribute to cutting-edge research and innovation in mathematical sciences. Bringing expertise in numerical analysis, partial differential equations, and mathematical modeling, along with a proven track record of academic excellence and a commitment to advancing mathematical knowledge.

• Society for Industrial and Applied Mathematics (SIAM)
• American Mathematical Society (AMS)
• Association for Women in Mathematics (AWM)

• Graduate Research Assistant, Washington State University, Pullman, WA, 09/01/18, Present, Conducted independent research on numerical solutions to nonlinear partial differential equations (PDEs)., Developed and implemented algorithms for simulating fluid flow in porous media using MATLAB and Python., Collaborated with faculty and peers to publish 3 peer-reviewed papers in top-tier journals., Presented findings at the SIAM Conference on Applied Mathematics (2022) and the AMS Joint Mathematics Meeting (2023).
• Research Intern, Pacific Northwest National Laboratory (PNNL), Richland, WA, 06/01/17, 08/31/18, Assisted in developing statistical models for climate prediction using R and Python., Analyzed large datasets to validate hypotheses and improve model accuracy., Co-authored a technical report on the application of machine learning in climate modeling.

• Graduate Research Fellowship, Washington State University
• Outstanding Teaching Assistant Award, Washington State University
• Dean’s List, University of Washington
• Undergraduate Research Scholarship, University of Washington

• Efficient Algorithms for High-Dimensional PDEs in Fluid Dynamics, Journal of Computational Mathematics, 2023, Developed a novel algorithm for solving high-dimensional PDEs with applications in fluid flow simulations.
• Iterative Methods for Sparse Linear Systems in Computational Physics, SIAM Journal on Scientific Computing, 2021, Investigated and compared the performance of iterative solvers for large-scale linear systems.
• Machine Learning Approaches to Climate Prediction, Proceedings of the AMS Joint Mathematics Meeting, 2022, Presented a new approach to integrating machine learning with traditional climate models.