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Good Practices for Research Software Engineering
by
Mon, Jan 12, 2026
1 PM – 2 PM EST (GMT-5)
Private Location (sign in to display)
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Details
Introduction to simple, yet time-tested practices and methodologies that can have long-term impacts on your productivity as a programmer as well as ensure the sustainability of the code you write. These practices are approachable and adoptable by both experienced developers and novices alike. Some examples of practices to be discussed include: writing programs for people, not computers; making incremental changes; and avoiding repetition.
Knowledge prerequisites: None
Hardware/software prerequisites: None
Workshop format: Presentation and demonstration
Target audience: Students, researchers, faculty, staff
More Software Engineering Training
Below is the full line-up of the Winter 2026 software engineering training by Research Computing:
Good Practices for Research Software Engineering on 1/12
Intro to Version Control with Git and GitHub on 1/12
Attaining vim Fluency: Edit as Fast as You Think on 1/13
Creating Reusable Python Code: From Notebooks to Scripts to Packages on 1/13
How to Package and Publish Your Python Code on 1/14
Gotcha! How to Write Software Tests to Improve Code Quality on 1/14
Debugging and Profiling Code in Python on 1/15
Continuous Integration and Continuous Delivery (CI/CD) with GitHub Workflows on 1/15
Tools That Help You Write Better Code on 1/16
Introduction to Software Reverse Engineering with Ghidra on 1/16
More Training Workshops
See the entire Research Computing Winter 2026 training program.
Speakers
Michal Grzadkowski
Michal joined Princeton Research Computing in 2021 after five years working as a Research Software Engineer at Oregon Health & Science University, where his primary project involved studying the application of machine learning models to better understand the impacts of mutations commonly implicated in tumorigenesis. This involved implementing novel methods for representing the taxonomies of mutations present in cancer cohorts, as well as developing software for deploying and consolidating thousands of classification models on a high-performance compute cluster. His present work focuses on optimizing pipelines for generating quantitative assessments of the contributions various types of assets can make to a power grid’s ability to satisfy the demand for electricity over a given time frame.