Doctoral

Jenna helped validate the first mutually conjoint bilateral machine learning audiogram.

She went on to become a clinical audiologist.

Jeff characterized neural networks made from a particular neuronal cell line for use as a spinal cord injury treatment.

He went on to a career in healthcare entrepreneurship.

Katherine generalized machine learning audiometry to extended models with wider application.

She went on to a career in aviation research.

Rebecca validated the first web-based machine learning audiogram.

She went on to become a clinical audiologist.

Robert is working to implement Distributional Active LEarning for tests of cognition.

Mark is building out the behavioral task dictionary to enable behavioral quantification in interactive virtual worlds.

Dom is working to enable any behavioral task sequence within interactive virtual worlds to be informative about underlying cognitive processes.

Ruiye investigated the encoding of vocalizations in the auditory system.

She went on to a career in data science.

Nikki investigated the effects of dialysis on hearing thresholds in patients with kidney failure.

David studied how to optimize the diagnosis and treatment of auditory processing disorders using distributed computing.

He went on to a career in serious game design, medical informatics and data analytics.

Wensheng characterized the population encoding of sound in auditory cortex using machine learning analytic methods.

She went on to a career in data science.

Alex helped clinically validate the first dynamically masked machine learning audiogram.

Paul worked on understanding the nature of dynamic range encoding of auditory cortex neurons.

He went on to a career in academic neuroscience research and software design.

Brittany performed the first clinical validation of machine learning audiometry.

She went on to become an academic clinical audiologist.