Teaching
I teach courses in programming, data analytics, and open-source software development in Syracuse University’s School of Information Studies, and lead hands-on research-computing workshops through the Open Source Program Office. Materials for my courses and workshops are linked below.
Courses
IST 400/600 Open Source Software Development Practicum
Fall 2026Syracuse University
Students contribute to open-source projects while learning the tools and practices of modern software development: environments, Git and GitHub, continuous integration, documentation, packaging, and licensing.
IST 356 Programming Techniques for Data Analytics
Fall 2025, Spring 2026Syracuse University
DSC 520 High Performance Scientific Computing
Spring 2023UMass Dartmouth
PHY 113 Classical Physics I
Fall 2022UMass Dartmouth
Workshops
OSPO Workshop: Computing 101, R, and Python
Summer 2025Git & GitHub, Bash, and Conda · Syracuse University
OSPO Workshop: Computing 101 and R
Winter 2025Git & GitHub · Syracuse University
OSPO Workshop: Advanced Python
December 2024Object-oriented programming in Python · Syracuse University
OSPO Workshop: Computing 101 and Python
August 2024Introduction to Bash, Conda, and Git & GitHub · Syracuse University
Lectures and tutorials
PyCBC Inference tutorial: standard IMR, ringdown, and hierarchical parameter estimation
December 2025GWPAW, Georgia Tech
Bayesian Inference in Gravitational-wave Astronomy
2025NP3M Summer School, Indiana University
Physics of Gravitational-wave Emission and Detection
202157th Karpacz Winter School of Theoretical Physics, Poland
Bayesian inference & PyCBC
2020Statistical Methods for the Detection, Classification, and Inference of Relativistic Objects, ICERM, Brown University (online)
Bayesian inference with PyCBC
2020PyCBC Inference Online Workshop
Gravitational-wave parameter estimation with PyCBC Inference
2019PyCBC Inference Workshop, University of Portsmouth, UK
Other resources
- PyCBC tutorials
- Jupyter notebooks on analyzing gravitational-wave data with PyCBC.
- PyCBC Inference documentation
- Guides and examples for gravitational-wave parameter estimation.