Nicholas Rasmussen

Background

About Nicholas Rasmussen

I am a Ph.D. candidate in Data Science and Engineering at the University of South Dakota, where my journey in advanced research has been both extensive and rewarding. My dual experiences, including volunteer research at Virufy, have significantly honed my expertise in signal processing and strengthened my commitment to the scientific method. I am dedicated to driving sustainable machine learning solutions that can make a meaningful impact on the world.My research portfolio includes five publications—three peer-reviewed and two archived—and I hold a patent for a complex clipping technique. Most of my work focuses on signal processing, particularly in the bioacoustical domain, spanning both natural science and medical applications. My contract work with the Biomedical Advanced Research and Development Agency (BARDA) has also been a significant part of my career.Key highlights of my career include:Volunteer Research Assistant at Virufy: Gained foundational skills in technical research, contributing to various projects and collaborating on publications.BARDA Projects: Played a pivotal role in developing and testing models for biomedical research, significantly improving data analysis precision through innovative signal processing techniques.U-Discover Grant (2022): Enhanced signal processing for medical analysis, presenting findings to government officials, and showcasing life-saving diagnostic tools.SPACT REU: Worked on anti-counterfeiting technologies using generative image transformation with a U-Net for pixel binarization.SDN Communications: Managed fiber optic projects and databases, leveraging SQL and GIS platforms to provide actionable insights.DoD Grant Research: Focused on active learning and UPAM, presenting at conferences, publishing research, and applying for further grants.My passion lies in deep learning and its applications, particularly in conserving biological systems and advancing human-robot interactions. This diverse experience equips me with the skills and knowledge to push the boundaries of what's possible in data science and engineering.