Antony Sikorski

Antony Sikorski

Data Science, ML, Spatial Statistics

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About Me

Welcome to my page! My name is Antony, and I like working on problems at the intersection of ML, data science, and statistics. Currently, I solve such problems while working on AI @ Meta.

I completed my Statistics PhD and Data Science MS as a GRFP Fellow at the Department of Applied Mathematics and Statistics at the Colorado School of Mines. Before that, I completed my BS in Applied Mathematics from the University of California, San Diego. At Mines, I developed deep learning and statistical methods for working with big spatial data under the guidance of Dr. Daniel McKenzie and Dr. Doug Nychka. During that time, I interned at Reality Labs @ Meta, the Learning the Earth with Artificial Intelligence and Physics (LEAP) NSF Science & Technology Center, NASA’s Jet Propulsion Laboratory (JPL), and Excelitas Technologies .

When I’m not working, I like to spend my time skiing, longboarding, going to concerts, lifting, and eating really spicy food!

Contact

Feel free to reach out via LinkedIn (antonysikorski) or email:

Email: antony dot sikorski at gmail dot com

News

  • (August 2026) Excited to announce that this month I’ll be starting work as a Data Scientist working on AI at Meta’s NYC office!
  • (August 2026) Our paper “A Non-stationary, Amortized, Transfer Learning Approach for Modeling Italian Air Quality”, has been accepted in Spatial Statistics!
  • (July 2026) I have successfully defended my PhD thesis, titled “Fast, Neural, and Sparse Methods for Big Spatial Data”!
  • (April 2026) Two major updates:
    1. We have released our new work, which extends the LatticeVision framework and applies it to modeling atmospheric pollution across all of Italy! Check out the paper and the accompanying code!
    2. I have been awarded the Graduate Research Award for excellence in research by the Applied Mathematics and Statistics Department here in Colorado School of Mines!
  • (May 2025) Excited to share that we have released LatticeVision, a framework for non-stationary parameter estimation with image-to-image neural networks. Check out the paper and the accompanying code!
    • Update (Jan 2026) This work has been accepted at AISTATS! Looking forward to presenting in Morocco!
  • (Feb 2025) I’ll be joining Meta as a Data Scientist Intern working on optimization in Reality Labs for the summer of 2025!
  • (Oct 2024) Our paper “Normalizing Basis Functions: Approximate Stationary Models for Large Spatial Data” has been published in Stat! The article can be found here, and the revised preprint can be found on arXiv.
  • (Oct 2024) The new version of our R package LatticeKrig has been published on CRAN!
  • (May 2024) I have been selected to perform research and co-mentor undergraduate students as a Momentum Fellow at LEAP, an NSF center focused on combining AI and climate science!
  • (April 2024) Excited to announce that I have been awarded the National Science Foundation Graduate Research Fellowship (NSF GRFP)!