Michael Paciullo

Hi! I’m Michael Paciullo, a software engineer at WRITER, building a generative AI platform for sales and marketing teams at Fortune 500 companies. I have past experience in numerical linear algebra, physical simulation, and computer graphics, and I am always looking to learn and build!

I started my career at Standard Practice, building voice AI that calls insurance companies to get doctors paid quicker! Before that, I was an undergraduate researcher at NYU’s Geometric Computing Lab, contributing to polysolve, a linear and non-linear solver API for finite element method simulations. I also worked at the Flatiron Institute, the research division of the Simons Foundation, implementing fast matrix factorization algorithms on GPUs.

Feel free to connect with me through any of the social media links below. For any industry-related inquiries, please contact me via LinkedIn. For academic inquiries, feel free to email me.

news

Apr 06, 2026	First day at Writer!
Apr 02, 2026	Last day at Standard Practice. Thanks for the good times!
Jul 17, 2024	First day at Standard Practice!
May 22, 2024	Published this website, and looking for new opportunities!
May 15, 2024	Graduated from NYU with a BA in Computer Science and Mathematics.

selected publications

The Harness Effect: How Orchestration Design Sets the Token Economics of Enterprise Agentic AI

Muayad Sayed Ali, Aliaksandra Novik, Anji Boddupally, and 29 more authors

2026

PDF

Implementing and Benchmarking a Parallel LDL^T Factorization with SYCL

Michael Paciullo and Geraud Krawezik

2023

Bib PDF

@techreport{ldlt-factor,
  author = {Paciullo, Michael and Krawezik, Geraud},
  title = {Implementing and Benchmarking a Parallel $LDL^T$ Factorization with SYCL},
  institution = {New York University},
  year = {2023},
}

Scaling the Finite Element Method to Small Problems with Dense Linear Solvers

Michael Paciullo, Daniele Panozzo, and Teseo Schneider

In Inquiry, 2024

Bib PDF

@inproceedings{dense-assembly,
  author = {Paciullo, Michael and Panozzo, Daniele and Schneider, Teseo},
  title = {Scaling the Finite Element Method to Small Problems with Dense Linear Solvers},
  booktitle = {Inquiry},
  institution = {New York University},
  year = {2024},
}