University of Canterbury, New Zealand
An insightful session exploring the design and development of location-based multiplayer AR games, showcasing how augmented reality can become a core gameplay mechanic that connects remote players through immersive shared experiences.
Planned as an online session.
University of Mississippi
A focused session on the tools, workflows, and research directions shaping modern game development and immersive interactive systems.
Planned as an online session.
University of North Carolina at Greensboro
Machine learning models are widely used in decisions that require justification, yet most of them cannot show how a prediction was reached. Explainable AI(l (XAI) provides methods to recover that reasoning after a model has been trained. This workshop is a hands-on introduction to those methods. We will work through the techniques most commonly used in practice, including LIME, SHAP, gradient-based attribution , and counterfactual explanations, with basic implementations for each so that participants can see how an explanation is produced and how its output should be read. For each method we will discuss its strengths, its limitations, and the kind of data and application it is best suited to, from tabular clinical data to images and text. Participants will be able to select an appropriate XAI method for their own problem, implement it, and judge how much confidence its explanations deserve.
Planned as an online session.
University of North Carolina at Greensboro
Optimization is a fundamental component of data science, enabling the development of predictive models, intelligent decision-making systems, and efficient computational algorithms. This workshop introduces participants to the mathematical foundations of optimization and explores modern optimization techniques used in machine learning, deep learning, and artificial intelligence. Through practical examples and hands-on exercises, participants will learn how optimization algorithms improve model performance, automate decision-making, and solve real-world data science problems.
Planned as a physical session.