Our aim is to improve human-machine cooperation. For machines, this is achieved through novel human-style reasoning techniques, and for humans, through computer-enhanced multimodal feedback.

CeTI is pioneering advancements in human-machine interaction by combining neuroscience, machine learning, psychology, and human-computer interaction. Our research focuses on developing probabilistic models to predict human reasoning at multiple time scales. By integrating these innovative approaches, we aim to enhance the synergy between humans and machines, leading to more intuitive and efficient cooperation.

The CeTI team brings together experts from diverse fields to address the complexities of human-machine cooperation. Neuroscientists study brain function to inform the development of human-style reasoning techniques for machines. Machine learning specialists create algorithms that enable these techniques. Psychologists and human-computer interaction experts design and test computer-enhanced multimodal feedback systems that improve human performance and interaction. Together, we strive to develop systems that can predict and adapt to human reasoning over various time scales.

CeTI objectives to revolutionize human-machine cooperation by integrating human-style reasoning in machines and enhancing human capabilities through multimodal feedback. Our innovative approach will set new standards for human-machine interaction, benefiting sectors such as robotics, healthcare, education, and beyond. CeTI is committed to advancing a future where human and machine collaboration is more intuitive, efficient, and effective, resulting in enhanced productivity and user experiences for all.