2018 - 2019 Fellows Call for Proposals

Each year approximately eight research awards are made to students enrolled in the CSE program. Research projects are selected for support on a competitive basis. Proposals are solicited each Spring for the academic year beginning the following Fall. The proposed research must be interdisciplinary and computationally oriented, and the proposal must be submitted jointly by the student and two faculty co-advisors who must be CSE affiliates from different disciplines (Note: The faculty can be from the same department as long as their disciplines are different). The CSE Fellows selected by an evaluation panel receive financial support equivalent to 25% research assistantship in their home department plus a tuition waiver. At the end of each academic year, that year's CSE Fellows present their research results at the annual CSE Symposium. Current CSE Fellows are listed below and past fellows are listed by academic year.

2017-2018 FELLOWS

Daniel George – (Astronomy) - Enabling Real-time Multimessenger Astrophysics via Numerical Relativity and Deep Learning

Mert Hidayetoglu – (Electrical and Computer Engineering) - Large Inverse-Scattering Solutions on Supercomputers and its Application to Imaging

Pouyan Karimi – (Mechanical Science and Engineering) - Simulation of Nanocomposites with a Focus on Electrical Properties and Shielding Performance

Kaijian Liu – (Civil and Environmental Engineering) - Transforming Data into Actionable Knowledge: A data-driven Approach to Enhanced Bridge Deterioration Prediction and Maintenance Decision Making

Shriyaa Mittal – (Center for Biophysics and Quantitative Biology) - A Cloud-Based Platform to Design ‘Optimal’ Probes for Protein Dynamics

Rambod Mojgani – (Aerospace Engineering) - Nonlinear Dimensionality Reduction of Convection-Dominated Flows

Darin Peetz – (Civil and Environmental Engineering) - A New Solver for the Generalized Eigenvalue Problem at Large Scales Combining Jacobi-Davidson and Multigrid Methods

Sixian You - (Bioengineering) - Computational Analysis of Label-Free Opto-Histology in Breast Cancer