PIMS Lunchbox Lecture: Mina Aminghafari
Topic
Machine Learning: Data Quality and Preprocessing and Denoising
Speakers
Details
Abstract
Artificial intelligence and machine learning are transforming numerous fields—including engineering, business, science, and healthcare—by enabling more accurate, data-driven decision-making. These techniques help us gain deeper insights from data and can accelerate complex tasks, such as identifying high-risk individuals early or reducing costs through targeted laboratory testing. In this talk, I will provide an overview of fundamental machine learning approaches with a special focus on the importance of data quality. I will also discuss various preprocessing and denoising methods that can significantly improve data quality and, in turn, enhance model performance and reliability.
Additional Information
Dr. Mina Aminghafari is an Associate Professor of Machine Learning at the University of Calgary and a professional Statistical Society of Canada statistician. Before this role, she served as an Associate Professor at Amirkabir University of Technology (2013 to 2023) and as an assistant professor at the same university (2006-2013). She has also held positions as a Senior Data Scientist in Canada's education and insurance industries (2018-2023). She has worked on different projects related to using machine learning in healthcare. For example, she is a co-PI on several diagnostic and health care projects, e.g. a project entitled Decoding "Healthy" from "Pathogenic" Antinuclear Antibody Patterns Using Artificial Intelligence, Funded by the McCaig Institute for Bone and Joint Health.
The lecture will be held on Thursday, April 17th, from 12 PM -1 PM MT.
This event is a part of the PIMS Lunchbox Lecture Series at the University of Calgary.