Programs: R, Python, Matlab, Unix, Git/GitHub, Google Analytics, LaTeX, Microsoft Office, SQL
Projects: Big data processing, statistical analysis and modeling, visualization, machine learning/artificial intelligence, package development, web-based applications, continuous integration and deployment, experimental design, image analysis, graphic design
Statistical Learning for Engineers (Fall 2020). Taught by Jin Seob Kim, PhD, Department of Mechanical Engineering
Introduction to Computational Medicine: Imaging (Fall 2020). Taught by Michael Miller, PhD and Tilak Ratnanather, DPhil, Department of Biomedical Engineering
Advanced Data Science for Biomedical Engineering (Spring 2020). Taught by Brian Caffo, PhD, Department of Biomedical Engineering
Introduction to Data Science (Spring 2020). Taught by Tamás Budavári, PhD, Department of Applied Mathematics & Statistics
Introduction to Neuro-Image Processing (Spring 2020). Taught by Siamak Ardekani, PhD, Department of Biomedical Engineering
Neural Implants and Interfaces (Spring 2020). Taught by Gene Fridman, PhD, Department of Biomedical Engineering
Structure and Function of the Auditory and Vestibular Systems (Fall 2019). Taught by Kathleen Cullen, PhD and Paul Fuchs, PhD, Department of Biomedical Engineering
Cracking the code: Theory and modeling of information coding in neural activity (Fall 2020). Taught by Michael Bonner, PhD, Department of Cognitive Science
Introduction to Computational Cognitive Science (Fall 2019). Taught by Tal Linzen, PhD, Department of Cognitive Science
All courses taken for credit from Johns Hopkins University