Welcome!

I am an Associate Professor in the Department of Crop and Soil Sciences at the University of Georgia and the Principal Investigator of the Computational Agriculture Lab. For our current research, team, and openings, please visit the lab’s website: compaglab.github.io. You can reach me at ethan.pickering@uga.edu.

Note: this personal page has yet to be fully updated. The newest work, news, and publications from my lab can be found on the Computational Agriculture Lab website.

Before joining UGA, I led AI research at Bayer Crop Science from 2022 through 2025, first as AI Genomics Modeling Team Lead and then as Head of Data Science and AI Research, developing methods for using Artificial Intelligence (AI) to inform genetic design of climate resilient crops. I also lectured graduate courses in Mechanical Engineering at MIT in 2023.

Previously, I was a postdoctoral associate in Mechanical Engineering working with Professor Themis Sapsis on prediction of extreme events in chaotic systems at the Massachusetts Institute of Technology. Specifically, we combined deep neural networks with optimal sampling techniques to efficiently perform experimental design. Check out our publication in Nature Computational Science on Discovering Extremes with AI, as well as our related preprints on the publications page.

I completed my Ph.D. in Mechanical Engineering working with Professor Tim Colonius on reduced-order turbulence models at the California Institute of Technology. I began my studies (B.S.) at Case Western Reserve University in Mechanical and Aerospace Engineering (Summa Cum Laude) and continued with a Masters (M.S.) in Mechanical Engineering studying building energy efficiency data analytics. I have work experience with Philips Healthcare, NASA, and the Great Lakes Energy Institute. In these roles I spent time working as a prototype engineer, a thermodynamic system modeler, and a building energy data analyst.

Active Learning for Discovering Extremes

My research at MIT centered on discovery and prediction of extreme events by leveraging recent advancements in deep neural networks and Bayesian statistics. Check out this quick 3 minute video for a snapshot of that work. Please check out the publications page for more information on this work.

Data-Driven Turbulence Modeling

My research investigates the fundamental mechanisms that exist in complex and chaotic fluid flows by leveraging large, high-fidelity datasets to inform and validate reduced-order modeling strategies. These mechanisms are of importance as they govern engineering quantities such as noise, drag, and efficiency. Unfortunately, both high-fidelity datasets and reduced order models, alone, can only provide limited insight into these mechanisms. In much of my research, I look to pose optimization problems where our models assimilate/learn various properties of turbulence from the data to yield reduced-order models that are both predictive and general (i.e. applicable to other flows geometries and conditions). In short, this research takes a constrained-‘‘machine learning’’ approach, where the Navier-Stokes equations remain a central component of the model.

To learn from the data, turbulent flows are decomposed into their most energetic components (using Spectral Proper Orthogonal Decomposition) and then modeled via linear amplification theory of the equations of motion (Resolvent Analysis). Check out the video above on how we decompose massive datasets (numerous TB) into SPOD modes and then seek to model their theoretical equivalent with resolvent analysis.