Education

The Digital Innovation Lab is a home for collaborative education efforts. The lab is working on a range of projects from traditional uses of computer vision to new computing approaches that reenvision routine clinical data and bring a richer understanding of the relationships hidden in existing records. The team draws its strength from common goals and the many disciplines of its members.

Mentorships and internships

The Digital Innovation Lab has a range of projects that provide unique learning experiences related to creating and validating digital tools used in medicine. Across all projects, scientific curiosity and a motivation to try to solve analytical problems are two skills the team looks for in trainees interested in partnering with the lab. The preferred skills for technical tasks vary depending on the projects worked on during training.

Prospective trainees are urged to think about what types of projects they are passionate about and how they align with their skills and development interests. Below are four general project areas. Some projects may require a mix of skills from different areas. Trainees are not expected to have every qualification. Trainees skilled in one or more of the following areas are encouraged to apply:

Data engineering

  • Knowledge or experience with SQL and database management and design.
  • Basic knowledge of R or Python.
  • Code and query optimization.
  • Generating workflows, code bases, and functions or packages that can be reproduced.

Statistical analysis and model development

  • Knowledge and experience using R and generating R Markdown reports.
  • Ability to perform data cleaning and exploratory data analysis and apply statistical tests.
  • Experience with complex data generation processes. This includes clustered or repeated data and time series data that may come from wearable and streaming devices. It also includes computer vision approaches, such as image segmentation classification tasks.
  • Working knowledge or experience applying regression and classification models.

Image processing

  • Knowledge and experience using Python.
  • Ability to preprocess images, generate labels and apply various algorithms.
  • Experience with OpenCV, scikit-image or PyTorch.

Application development

  • Experience developing interactive proof-of-concept applications using Streamlit, Dash, Shiny or flexdashboard.
  • Experience developing applications with Swift for iOS.