• Review the current state-of-the-art for visualization models and simulation techniques to determine the mobility with muti-modal transportation using different technologies for smart city initiatives.• Identify appropriate datasets for the study area; commence data acquisition.• Explore the data collected methods using analytical and visualization techniques.• Develop the visualization/ simulation model for the study area and the mobility with multimodal transportation.

I worked as a Machine Learning Data Scientist at the Centre for Applied AI (CAAI) at Sheridan College, collaborating with Naryant and Osteoporosis Canada on a research project to improve imminent fracture risk (IFR) assessments.Key Responsibilities:- Conducted machine learning and data science research to analyze Osteoporosis data and identify patient characteristics that could predict IFR.Utilized machine learning techniques (Scikit-learn, TensorFlow, Keras) and Python programming to develop and evaluate machine learning models.- Performed data cleaning, feature extraction, and exploration to prepare data for analysis.- Researched emerging technologies and best practices in machine learning relevant to the project.- Documented research findings and participated in team meetings and presentations.