Yigit Aydede
Sobey School of Business
Economics
Professor
Acting Department Chair
Office: Sobey 343
Phone: 902-420-5673
Email: yigit.aydede@smu.ca
I am an associate professor of economics at Saint Mary’s University. I usually teach data analytics courses such as statistics, machine learning, and econometrics both at the undergraduate and graduate levels.
My research is related to applied econometrics in demographics, health, and applied microeconomics. I am also a founder member of Research Portal on Machine Learning for Social and Health Policy, which is a joint initiative by a group of researchers from Saint Mary’s and Dalhousie universities.
Currently, I am working on COVID-19 Epidemic curve segmentation, chronic disease surveillance systems, credit risk profiling, automation of pre-immigration selection procedures, and developing machine learning methods for social scientists and policy analysts.
Most recently, one of our projects, where I am a principle investigator, is selected to the Nova Scotia COVID19 Research Coalition. In this project, which is a partnership between three universities, Saint Mary’s, Dalhousie, and Acadia, we use confidential person-level data and advance machine learning methods to understand the role that micro-environmental factors play on local transmission rates.
Starting July 1 in 2021, I am appointed to Sobey Professorship in Economics for 5 years.
Related Links
Personal Website →I present and disseminate my work using unconventional data and the latest methods in Machine Learning in my blog post series. (See CBC NEWS - How well have Canadians complied with COVID-19 restrictions? New data offers a glimpse). See my blogs at:https://github.com/yaydede
Although most of my research is in the field of population economics, my current focus is on genomics, unconventional data, chronic disease surveillance systems, and machine learning.
I am a founder member of Research Portal on Machine Learning for Social and Health Policies. We organize annual workshops on causality and statistical predictions for social scientists.
In collaboration with one of my colleagues, I have organized a 3-day online Summer School, Machine Learning for Economists and Applied Social Scientists, between July 20 and 22, followed by a 2-day Webinar series. More than 60 faculty and researchers attended these fee-based online lectures and more than 260 participants registered for the 2-day long webinar. In November 2021, we organize a 3-day workshop, "The Role of Nonparametric Methods and Machine Learning in Causal Analysis"
See the details at: https://sites.google.com/view/mlportal/home
PhD in Economics (2006), University of Delaware