Learning & Affect Technologies Engineering (LATTE)

At Latte we research and develop systems that learn and help people learn. We are developing e-learning and collaborative applications that use machine learning algorithms to improve the user experience or produce innovative functionalities.

Our current work includes:

• Text mining functionalities to support collaborative learning
• Affective computing
• Social network analysis
• Computer supported collaborative work and learning
• Physiological (EEG/EKG/EMG) signal processing using machine learning algorithms

News & Conferences

• FLAIRS Workshop on Affective Computing. Submit your papers!
• Rafael appointed Associate Editor of IEEE Transactions on Affective Computing.
• Stephen O'Rourke graduates (10th graduating Postgrad student from Latte). Congratulations!.
• Omar Alzoubi wins research conversazione award. Congratulations!.
• Cameron Higgins wins best student paper award for his thesis "Classification of emotion in
  physiological signals". Congratulations!.