Master's

Yasaman investigated the best procedure to embed informative behavioral tasks within standard Minecraft gameplay.

Steven deployed the final cloud-based machine learning audiogram.

He went on to a career in software development.

Jake worked on updating and evaluating machine learning contrast sensitivity function estimators.

Scott researched artificially-induced neuronal plasticity using implantable brain-computer interfaces.

He went on to a career in imaging research and software engineering.

Hanqi worked to support and extend the ability to acquire real-time cognitive data with active machine learning.

Jonathan researched how to generalize a Bayesian probabilistic classifier with a 4-parameter linking function and binomial likelihood.

He went on to doctoral training in computer science at UCLA.

Thomas researched the biases in functional neuronal recording for various methods depending on the topographical arrangements of various response types.

He went on to medical school at Tulane University and a career in internal medicine.

Brian developed the machine learning visual field (threshold perimetry) test.

He went on to a career in data science.

James developed the first conjoint psychometric test: the bilateral audiogram.

He went on to a career in software engineering.

Dan worked to link front end behavioral data collection with back end active machine learning for scalable data accumulation.

He went on to work for a healthcare startup and attend medical school at Rutgers University.

Matt worked in the lab Summer 2019 as a master of social work / master of public health student.

Michael worked to add Bayesian active learning to our newest cognitive tests.

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

Trevor evaluated the efficiency of Bayesian active model selection for audiometric diagnoses.

He went on to a career in healthcare data science.

Qihan coded for our brain training video games.

He went on to a career as a software developer.

Mariluz is working on discerning trends in population executive function tests to construct prior beliefs for Bayesian active learning tests of cognition.

As of Fall, 2023 Mariluz is a doctoral candidate in biomedical engineering at the University of California, San Diego.

Shohaib worked to bring Bayesian active learning to visual contrast sensitivity functions.

Kiron was instrumental in the application of simulations to confirm the properties of machine learning audiometric tests.

He went on to a career in machine learning software development.

Dan analyzed data from pediatric brain tumor patients to construct better predictors of clinical outcomes.

She went on to a career of machine learning software development.

John worked in the lab as a Medical/Masters student from Summer 2008 through Fall 2008.

Kevin worked in the lab from Summer 2018 to Spring 2020 as a biomedical engineering undergraduate and computer science master's student to implement semantic auditory search apps and analyze the resulting data.

He went on to pursue a PhD in Bioengineering at University of Pennsylvania

Xiaochen worked on developing Python-based active learning code for cognitive testing.

He went on to pursue a PhD in computer science at Purdue University.

Zhiting worked to extend the machine learning contrast sensitivity estimator to include other related data streams.