skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 2

Search for: All records

Creators/Authors contains: "Vargas, Santiago"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; ; (, Journal of Chemical Theory and Computation)
    Free, publicly-accessible full text available April 22, 2026
  2. ; ; ; ; ; ; (, Chemical Reviews)
    Free, publicly-accessible full text available April 9, 2026
  3. ; ; (, Digital Discovery)
    QTAIM coupled to graph neural networks can improve model performance and generalizability. 
    more » « less
    Full Text Available 
  4. ; ; ; (, Journal of the American Chemical Society)
    Full Text Available 
  5. ; ; ; ; (, ChemRxiv)
    Accepted Manuscript Full Text Available
  6. ; ; (, Journal of the American Chemical Society)
    Full Text Available 
  7. ; ; ; ; ; ; ; (, 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Workshop on ML for Systems)
    BBR is a newer TCP congestion control algorithm with promising features, but it can often be unfair to existing loss-based congestion-control algorithms. This is because BBR's sending rate is dictated by static parameters that do not adapt well to dynamic and diverse network conditions. In this work, we introduce BBR-ML, an in-kernel ML-based tuning system for BBR, designed to improve fairness when in competition with loss-based congestion control. To build BBR-ML, we discretized the network condition search space and trained a model on 2,500 different network conditions. We then modified BBR to run an in-kernel model to predict network buffer sizes, and then use this prediction for optimal parameter settings. Our preliminary evaluation results show that BBR-ML can improve fairness when in competition with Cubic by up to 30% in some cases. 
    more » « less
    Accepted Manuscript Full Text Available
  8. ; ; ; (, Proceedings of the ACM SIGCOMM Internet Measurement Conference)
    Full Text Available 
  9. ; ; ; (, Journal of Chemical Theory and Computation)
    Full Text Available 
  10. ; ; ; ; (, Proceedings of the Web Conference 2021)
    null (Ed.)
    Full Text Available