Sahar Asgari
About Sahar Asgari
Sahar Asgari is a Data Scientist with expertise in machine learning and data analysis, particularly in the context of Covid-19 risk assessment. She has worked on various projects involving data balancing techniques, anomaly detection, and outbreak prediction, and currently holds positions at McMaster University and the Ontario Ministry of Health.
Current Position at McMaster University
Sahar Asgari currently serves as a Machine Learning Research Assistant at McMaster University in Hamilton, Ontario, Canada. In this role, she engages in various research projects that leverage machine learning techniques to analyze complex datasets. Her work includes the application of advanced algorithms to enhance data interpretation and predictive modeling.
Role at Ontario Ministry of Health
Sahar Asgari is employed as a Data Scientist at the Ontario Ministry of Health in Toronto, Ontario, Canada. In this capacity, she utilizes artificial intelligence and machine learning methods to track Covid-19 outbreaks. Her responsibilities include implementing data balancing techniques and conducting risk assessments to inform public health decisions.
Education and Academic Background
Sahar Asgari completed her Master's degree in Energy Systems Engineering with a focus on Energy and Environment at the University of Tehran from 2013 to 2015. She further pursued her academic career at McMaster University, where she earned a Doctor of Philosophy (PhD) in Machine Learning Research Assistance from 2017 to 2021.
Data Science Projects and Techniques
Sahar Asgari has implemented various data science techniques in her projects. She applied data balancing methods such as downsampling, upsampling, and synthetic dataset generation in Covid-19 risk analysis. Additionally, she conducted data exploration and used K-means and Autoencoder methods in a Drug Anomaly Detection project. Her work also includes analyzing mobility data to predict Covid-19 outbreaks and creating dashboards using Power BI.
Machine Learning Model Development
In her projects, Sahar Asgari developed and compared multiple machine learning models, including random forest, deep learning, XGBoost, and ElasticNet. These models were utilized to assess the risk of Covid-19 in undetected populations. She also employed Gains and lift charts to identify true positive cases in Covid-19 risk prediction models, demonstrating her expertise in predictive analytics.