I hold a position as Research Assistant with Prof. Tanya Berger-Wolf. My research is also supported by the Dean’s Scholar Award and by the FMC Technology Fellowship. My general research interests are parallel algorithms and computer architectures, with special focus on sparse algebra and convex optimization on GPUs. My research aims to design novel GPU-based algorithmic techniques to efficiently solve ILP (along with its LP relaxation), QP and SDP. My focus is on large sparse problems arising from a computational biology, an applicative area that uses solutions of algorithmic problems to answer questions about biological systems. In addition, my work has application in a wide range of disciplines, such as combinatorial optimization, operations research, control theory, structural optimization, economics and several other engineering fields. Up to now, I have worked on improving the state-of-the-art in Sparse Matrix-Vector multiplication (SpMV) on GPUs, providing a solid foundation for my future research. Despite the fact that adapting sparse computation to GPUs is a challenging task, we can envision a speedup that will allow us to solve convex optimization problems faster than any state-of-the-art solver.

I am also contributing to IBEIS (Image-Based Ecological Information System). This research project aims to build a large autonomous computational system that starts from image data and progresses all the way to answering ecological and conservation queries. Based on image analysis algorithms, IBEIS can identify individuals from species with distinctive striped, spotted, wrinkled or notched markings, such as elephants, giraffes and zebras. I am focusing on scaling IBEIS and its algorithms to the magnitude of image data available nowadays, with particular interest in using GPUs.