Instructor: Dr. Mabel Carabali (BIO)

This short course provides foundational concepts about inequalities and equity in public health. The course provides an opportunity to think critically about considerations of social determinants of health and their use public health research in the context of AI. The introductory content provides methodological tools to identify the presence and accurately measure inequalities. 

This course is listed under the “Equity” category.

Instructor: Dr. Nick King(BIO)

The course introduces learners to the range of perspectives on ethical issues associated with uses of AI in public health and support learners to critically engage with risks and opportunities of AI from one or more ethics perspectives. Beginning with an overview of the World Health Organization’s guidelines on ethics and governance of AI for health, the course will include case examples in Natural Language Processing and Large Language Models related to public health.

This course is listed under the “Equity” category.

Instructor: Dr. Douglas Manuel (BIO)

Public health predictive algorithms and models are becoming increasingly complex which poses a challenge for reproducibility, transparency and use in practice (deployment). Inefficiencies, biases and errors will occur if algorithm developers cannot report their algorithms clearly, in both human and machine-readable formats. This course introduces the concept of “data pipelines”, software libraries and standards for generating algorithms that can be easily used by others in validation studies and application. Trainees will have the opportunity in the hands-on component to create an algorithm and deploy it as a web application (web API).
Learners are required to have proficiency in R for this course. 

This course is listed under the “Methods” category.

Instructor: Dr. Zahra Shakeri (BIO)

This course will expose students to various visualization techniques and tools to transform complex data into compelling and interactive visual reports. Students will learn design principles and exploratory/ explanatory visualization techniques to accurately distill complex datasets into coherent insights for audiences with varying levels of data literacy. The class will also focus on critical thinking, problem-solving, and sound analysis practices to avoid cognitive biases. Course materials, in-class activities, and the assignments will be designed for real-world application in the data-driven and data-intensive domain of public health.

This course is listed under the “Methods” category.

Instructor: Dr. Tracie Risling (BIO)

This course will allow learners to explore the concepts of knowledge dissemination, translation and mobilization with a focus on the critical differences in the latter’s knowledge to action directives. The importance of robust knowledge mobilization (KMb) in novel areas of exploration such as the use of artificial intelligence in public health will be examined as a foundation for leaners to build their own KMb plans for current or future research study. Lastly, learners will have an opportunity to trial creative knowledge mobilization approaches supported by information on stakeholder engagement, plain language communication and other science communication best practices.

This course is listed under the “Public Health & Policy” category.

Instructor: Farbod Abolhassani (BIO)

This course provides learners with the necessary skills to work effectively with data science tools, specifically Jupyter Notebooks and versioning systems. Participants will gain a fundamental understanding of how Jupyter Notebooks function as an interactive computational environment for creating, sharing, and documenting code, and how it can be leveraged to analyze large datasets in Public Health. It will also cover version control systems, such as Git and key data science libraries, including pandas for data manipulation and analysis, and matplotlib for data visualization, as well as Anaconda, a popular distribution of Python that includes Jupyter Notebooks and key data science libraries.

This course is listed under the “Methods” category.

Instructor: Dr. Joon Lee (BIO) 

This course is an introduction to natural language processing
that covers basic raw text data pre-processing, part-of-speech tagging, and simple machine learning-based text
classification and prediction models. This course is hands-on and proficiency in Python programming is required.

This course is listed under the “Methods” category.