Learn about Innovative Data Science Methodologies – June 16, 2022 – 10:00 AM – 12:00 PM

Are you looking to adopt novel and innovative data science methodology for your data science research? Are you interested in innovative methodological techniques in data science?

The DSI is hosting its first presentation featuring the three recipients of our Seed Funding for Methodologists competition. This hybrid event for DSI members will consist of three short presentations each followed by a Q&A session. The presentations will showcase the innovative methodological techniques being developed and provide a networking opportunity for applied researchers.

• Date:  Thursday, June 16, 2022  
• Time: 10:00 am to 12:00 pm

This event is for DSI members only. Please fill out this form to register.

Presenters:

Murat Erdogdu – Assistant Professor, Department of Computer Science, Faculty of Arts & Science

• Prof. Erdogdu is developing theoretical tools to compute the asymptotic generalization error of certain overparameterized estimators and characterize the convergence rate of overparameterized neural networks beyond the kernel regime. This new theoretical tool will enable researchers to more carefully develop machine learning models that take their model’s limitations into account, across many application areas.

Aya Mitani – Assistant Professor, Dalla Lana School of Public Health

• Prof. Mitani is developing a methodology that applies multilevel matrix-variate analysis to longitudinally collected dental data while accounting for correlation. The unique correlation structure of teeth provides an excellent application area, and Mitani aims to connect with researchers and oral health practitioners to prevent and manage oral diseases with greater precision, improving oral and general health outcomes across populations by applying these new methods and tools.

Linbo Wang – Assistant Professor, Department of Computer and Mathematical Sciences, University of Toronto Scarborough

• Prof. Wang is developing innovative tools to find causal relationships with observational and/or experimental datasets. These new tools will allow researchers to better understand the underlying causal mechanisms and help decision-makers make more informed decisions. There is broad and impactful potential for the application of these methods.