Dimitri Lopez

Bio

I hope to improve public health through computational models that are scientifically rigorous and robust enough to run in real time. I am a second-year PhD student as part of the Network Science Institute at Northeastern University. I am advised by Michael Johansson.

I have been leading the development of EMBERS, an operational infectious disease forecasting engine built on branching processes that submits weekly to the CDC's FluSight Forecast Hub. The goal is a computationally efficient and mechanistically interpretable framework for infectious disease forecasting. I am also studying the optimal allocation of testing resources to improve surveillance and patient outcomes in locations where dengue and chikungunya are endemic.

Before joining the Network Science Institute, I completed my Master's in computer science at Rensselaer Polytechnic Institute (RPI), following my Bachelor's in computer science and mathematics there. Born and raised in New Mexico, I had the chance to work at Los Alamos National Laboratory as a part of the Statistical Sciences group under Joanne Wendelberger and the Applied Computer Science group under Patrick McCormick. I also briefly worked as a software engineer at both Amazon.com and Amazon Web Services.

Publications

Most recent publications on Google Scholar.
^‡ indicates equal contribution.

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Talks

Vitæ

Full Resume in PDF.

• Northeastern University September 2025 - Current

  PhD in Network Science
  Advised by Michael Johansson as a part of the Network Science Institute.
• Rensselaer Polytechnic Institute August 2023 - August 2024

  Master of Science in Computer Science
  (Machine Learning / Data Science Concentration)
  4.0 / 4.0 GPA. Machine Learning, Computer Vision, Stochastic Modelling, Computational Math, Reinforcement Learning, Graph Mining, Optimization, Network Science
• Rensselaer Polytechnic Institute May 2022 – December 2024

  Graduate Researcher
  Under Jianxi Gao and Lu Zhong. National COVID-19 healthcare research published by Nature Medicine quantifying healthcare resilience and adaptability during successive disruptions. A novel mathematical framework was developed and applied to hundreds of millions of healthcare records identifying faltering healthcare systems and services. Provided strategies to boost resilience and adaptivity - potentially saving countless lives and improving treatment for tens of millions of people. Found novel metrics for city efficiency through analyzing terrabytes worth of human mobility records on resource acquisition within the largest metropolitan areas in the United States.
• Amazon.com May 2023 – August 2023

  Software Development Engineer Intern
  Ordering team at Amazon.com. Created a suite of debugging tools for a 15-year-old codebase that persists every Amazon order to and from disk. Learned how production systems are refactored safely at scale and kept reliable under extreme load.
• Amazon Web Services August 2022 – December 2022

  Software Development Engineer Intern
  Developed a suite of features for real-time analytics on AWS’s Distributed Load Testing, enabling users to monitor load test progress and the number of available resources in real time. Full scale feature planning and full stack development using React, Typescript, and AWS cloud services.
• Los Alamos National Laboratory May 2021 - August 2021

  Undergraduate Intern for the Applied Computer Science Group
  I developed a suite of simulations for the Nvidia group with the goal of benchmarking the Nvidia cuNumeric Legate library — a drop-in parallel computing replacement for NumPy. Developed 10,000+ line codebase featuring fluid simulations, numerical method on physics problems, disease spread, emergent properties in complex systems, and more.
• Rensselaer Polytechnic Institute September 2020 – December 2023

  Bachelor of Science in Computer Science and Mathematics
  3.80 / 4.0 GPA. Principles of Software, Programming Languages, Algorithms, Proof Writing, Linear Algebra, Graph Theory, Discrete Mathematics, Cognitive Science
• Los Alamos National Laboratory May 2019 - September 2020

  Undergraduate Intern for the Statistical Sciences Group
  Pioneered novel statistical methods for analyzing and visualizing large scale data generated by exascale systems (super computers). Improved career growth forecasting techniques for all LANL positions through statistical analyses and Markov chain modeling.
• Los Alamos High School May 2020

  J. Robert Oppenheimer Scholarship
  For great promise in the mathematical sciences.
• Los Alamos High School May 2020

  Rensselaer Medalist
  Awarded to the top STEM student at Los Alamos High School.

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