This neuroscience research investigates how the human brain constructs and adapts goals. Using fMRI and a dynamic decision-making game, the study identifies neural activity in the prefrontal cortex and anterior cingulate cortex associated with goal selection, valuation, and adaptation. The findings may help develop AI systems better aligned with human goals.

This neuroscience research investigates how the brain assigns value during decision-making. Using low-intensity focused ultrasound and human single-neuron recordings, the study examines the ventromedial prefrontal cortex and its role in transforming perception into choices. The findings may improve understanding of disorders such as obsessive-compulsive disorder and maladaptive decision-making.

This research improves neural implants for vision restoration by reproducing natural brain activity patterns. Using a two-way stimulation approach in the retina, electrical signals are optimized to activate neurons precisely. This enables more accurate visual perception, moving beyond crude light flashes toward meaningful vision, with potential to restore recognition of familiar faces.

This research examines the legal risks of mind-reading neurotechnology in criminal justice. By developing a neurorights framework—covering mental autonomy, privacy, and integrity—it aims to protect freedom of thought while enabling responsible forensic use of brain data as neurotechnologies rapidly advance.

This research uses advanced brain imaging, long-term clinical monitoring, and sensor data to understand why deep brain stimulation helps Essential Tremor patients—and why it sometimes stops working. By modelling neural pathways and analysing two-year outcomes, the project identifies optimal DBS targets and the main causes of treatment failure, improving long-term patient care.

My research improves brain–computer interfaces for children with disabilities by reducing the repetitive calibration needed before use. Using transfer learning and a team-selection algorithm, data from other users help personalise the system, cutting calibration by up to 90%. This makes creative activities like painting more accessible, enjoyable, and sustainable.