Doctoral theses in progress:

• Low-Power Spiking Neural Networks with Low Memory Footprint
• Spiking Neural Network Model for On-Device Learning in Edge Systems
• Application of Neuroprocessors for the Analysis of Event-Based Vision Data
• Reinforcement Learning Algorithms for Applications in Magnetorheological Soft Components
• The Use of Connectomes in the Analysis of Electromyographic Signals
• Low-Power Spiking Large Language Models

Doctoral theses defended:

• Application of machine learning techniques in glaucoma detection and control