Inga A. Girshfeld

I recently earned my Ph.D. in Applied Mathematics from the University of Southern California, where I worked under the guidance of Xiaohui Chen. My research focused on machine learning and statistical inference for complex distributional data, with an emphasis on Wasserstein regression, optimal transport, and deep learning methods for prediction between probability distributions. I also completed an M.S. in Statistics at USC, an M.S. in Applied Mathematics from California State University, Northridge, and a B.S. in Applied Mathematics from University of California, Los Angeles.

My academic and research experience spans applied mathematics, machine learning, scientific computing, and numerical methods. I have contributed to research projects at NASA Ames Research Center involving hazard simulation and optimization, and at the Interdisciplinary Research Institute for Sciences (IRIS), where I developed numerical methods for high-frequency exterior scattering problems. My work frequently involves Python, PyTorch, high-performance computing, and computational modeling for large-scale and high-dimensional systems.

Beyond research, I enjoy building collaborative and supportive communities. During graduate school, I taught courses ranging from Precalculus to Numerical Methods at USC and CSUN, served as President of the Math Graduate Student Association at USC, and mentored students through programs such as the Directed Reading Program and the Association for Women in Mathematics Chat With a Mathematician initiative.

My recent work on Neural Local Wasserstein Regression was presented at the Joint Mathematics Meetings and published in Proceedings of Machine Learning Research.

Outside of research, I enjoy lifting, cooking, reading, and writing. 

I am currently seeking opportunities in machine learning, AI, data science, and quantitative research.