As a Researcher at the center for Bisopsectroscopy and Spectromix, I am part of a team investigating how portable spectroscopic technologies such as Near-infrared and Visible Microspectroscopy could be translated into clinical use as a cheaper and more sensitive option for diagnosing infectious diseases such as Malaria, Fibrosis and Bone disorder. The aim of this diverse research is to reduce costs associated with current options which ultimately allow patients access to necessary therapy in a timely manner. We identify specific biomarkers associated with each disease with unique spectroscopic signatures and utilize a model-informed paradigm of machine learning protocols to make a reliable diagnosis. There is potential to apply this technique to a wide range of other diseases and smoothly translate from preclinical to clinical settings by integrating models from multiple subject areas. Knowledge of relevant machine learning packages includes Origin, Unscrambler, MATLAB with PLS-toolbox, Quasar, and Python. Key areas of expertise include but are not limited to data analysis, cell cultures, FTIR, Raman, AFM-IR, and Mass Spectroscopic technique (LC-MS, GC-MS, and ESI/APPI ICR-MS), experience working in a PC2 environment and handling biological samples proven by work done in Monash Biospectroscopy, Austin Health, and Creek Malaria Drug Delivery Disposition and Dynamics Laboratories.