I am a Data Scientist at Elder Research, specializing in scientific machine learning, predictive analytics, and numerical methods. I develop algorithms and apply deep learning to solve problems across diverse domains, including time series analysis, natural language processing, radar applications, and data fusion. Previously, I served as a Research Scientist at Michigan Tech Research Institute, where I worked on moving target recognition and image processing in inverse synthetic aperture radar. My earlier career includes high-performance computing through an NSF Mathematical Sciences Graduate Internship at Argonne National Laboratory, finite element analysis at the Cold Regions Research and Engineering Laboratory, and Agile software development at Workforce Software, where I focused on data mining, numerical optimization, and software automation.
I hold a Ph.D. in Applied Mathematics from the University of Illinois Chicago, where I was advised by David Nicholls and developed high-order perturbation methods for electromagnetic wave scattering in periodic media. My research is primarly focused on computational mathematics, including spectral methods, finite element methods, and boundary integral methods, as well as scientific machine learning techniques such as physics-informed neural networks and large language models.
