Technical expertise: MRI and other imaging methods, neuroimaging methods, big data, informatics, bioinformatics, machine learning, deep learning, artificial intelligence (AI), natural language processing, large language models, image analysis, data analysis, biostatistics, predictive modeling, meta-analysis, data sciences, and imaging clinical trials. Some software used in the lab:
SQL/Sequel, Python, Matlab, scripts
R, SPSS
SPM, FSL, AFNI, Freesurfer, CAT12, 3D slicer
Mpower/Montage, VNA PACS, ITKsnap, Radiant
AI algorithms, deep learning algorithms, convolutional neural networks (CNN, RNN), Jupyter Notebook, LLM
ATLAS, OMOP, TriNetX.
Other machine learning methods (neural networks, k-NN, SVM, LSTM, XGBoost)
Other modeling methods (PCA, GLM)
Clinical domain expertise: Neuroscience, medicine, neurology, physiology, animal models, retinal diseases (glaucoma, diabetic retinopathy), neurodegenerative diseases, cancer, and COVID-19.
Our research is translational and transdisciplinary. We collaborate closely with radiologists, neurologists, ophthalmologists, neuroscientists, engineers, computer scientists, and physicists to solve clinically relevant problems. Trainees learn from a large group of experts based on their projects. Group members include imaging scientists, data analysts, engineers, and a few medical doctors.
Mentoring: We place strong emphasis on mentoring and career development. I have mentored over two dozen predocs, three dozen postdocs and two dozen non-tenure track faculty and tenure-track faculty. Grantsmanship is important part of the training and about half of our graduate-level trainees are funded by individual and/or institutional training grants. Many of our trainees have become leaders in their fields. Most of our trainees end up as tenured faculty, tenure-track faculty, research scientists in academic institutions, research scientists in drug companies, and medical physicists in hospitals.
