The future of synthetic crowds for simulation, animation, safety-critical analysis, and design is ultra-high fidelity physically enabled crowds. Our group explores and developed new techniques in Reinforcement Learning, Multi-agent Reinforcement Learning, Deep Learning, Steering, Biomechanics, and Physical Character Control to support the next generation of crowds models and applications. These approaches support the modelling and animation of more diverse and representative crowds with a long-term goal of modelling disabilities, mobility devices, and cognition in synthetic crowds.

We improve design pipelines and strengthen outcomes and deliverables by augmenting designer skills with machine learning, artificial intelligence, and optimization via static and dynamic analysis. Our augmented pipelines produce environments that are human-aware and predictive to a broad range of scenarios.

Building more engaging games by understanding how players move and engage in the environment. Analyze and model the underlying difficulty to produce target level difficulties through optimization. Use these levels to procedurally augment player experience or provide options for player controlled experiences

We develop game platforms based on high-fidelity clinical tracking and measurement tools. Using these platforms we engage in participatory design and development of visual metaphors and game mechanics for reaching rehabilitation goals. We drive characters and visualizations using computational geometry to capture clinical motor speech targets. This approach augments clinical practice to improve attrition rates in a variety of rehabilitation and therapy settings.

Often complex designs are difficult to engage with. We explore and build interactive technologies using augmented and virtual reality to facilitate and streamline stakeholder and collaborator communication. Our platforms have highlighted and verified design flaws and errors while improving the accuracy of the perception of design choices.