Zachary Caterer, an undergraduate student at the University of Wisconsin-Eau Claire (UWEC), is focused on the development and integration of novel imaging techniques, machine learning, and artificial intelligence to enhance diagnostic accuracy and improve point-of-care practices. At UWEC, he contributed to a project in the Department of Biomedical Engineering and Materials Science and Department of Computer Science, where he assisted in developing deep learning algorithms for automated and precise detection and classification of laser-based spectroscopic images. This advancement has the potential to revolutionize diagnostic processes, aiming to enhance patient treatment and outcomes. As an Amgen Scholar, Zachary is currently working on integrating machine learning and fluorescence microscopy techniques to create an automated system for the accurate detection of Mycobacterium tuberculosis (Mtb) in patient smears. By utilizing environment-sensitive probes and the low-cost fluorescence microscope Octopi, his objective is to streamline the diagnostic process and improve the efficiency and reliability of TB detection. The project involves investigating optimal parameters for probe detection, distinguishing between live and dead cells, assessing drug resistance, and detecting Mtb cells in human samples. Through this research, Zachary aims to contribute to global efforts in combating tuberculosis and ultimately enhance patient care. Zachary extends his sincere gratitude to the Amgen Foundation and The Kamariza Lab for the invaluable opportunity to develop and execute his project, further refining his skills as a researcher.