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Hi, I’m Brian Pardo, and I recently completed a PhD in theoretical astrophysics at the University of Pittsburgh.

• I’m interested in high-impact applications of machine learning and data science and continuing to learn new tools and techniques
• I’m looking to collaborate on machine learning, data science, and physics projects!
• Connect with me on LinkedIn

• Mathematics and Physics: Linear algebra, differential equations, multivariate calculus, statistics, effective field theories, general relativity
• Programming: Python (numpy, scikit-learn, pytorch, matplotlib, Jupyter notebooks), MATLAB, Mathematica, C++, Git, Linux
• Machine Learning: Data engineering, data visualization, deep learning frameworks, convolutional neural networks, optimization techniques

• Research project management
• Analytical thinking and problem solving
• Scientific communication to both technical and nontechnical stakeholders

• Radiation Reaction for Non-Spinning Bodies at 4.5PN in the Effective Field Theory Approach. arXiv | To appear in Physical Review D
• Gravitational radiation from inspiralling compact objects: Spin-spin effects completed at the next-to-leading post-Newtonian order. arXiv | Physical Review D
• Next-to-leading order spin-orbit effects in the equations of motion, energy loss and phase evolution of binaries of compact bodies in the effective field theory approach. arXiv | Physical Review D
• Particle decay in post inflationary cosmology. arXiv | Physical Review D
• Photoinduced Phase Transitions by Time-Resolved Far-Infrared Spectroscopy in V₂O₃. Physical Review Letters