I am 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 combine deep neural networks with optimal sampling techniques to efficiently perform experimental design. Check out our recent 2022 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. Currently, I am pursuing research at the intersection of computational fluid dynamics and data science.
My recent research activity centers around 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 into my current work. I hope to have some publications out shortly surrounding these techniques.
Spectral Proper Orthogonal Decomposition and Resolvent Analysis of a Mach 1.5 jet.
Pickering et al., Journal of Fluid Mechanics (2020)
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.
Submitted a new paper to JASA! We use reduced-order modeling for capturing physics of turbulet jet noise.4 February, 2020
Defended my thesis, Resolvent modeling of turbulent jets, check out the recording!22 May, 2020
Just submitted a new paper to JFM! We use data to determine an optimal eddy-viscosity for resolvent analysis.14 April, 2020
Lift-Up in turbulent jets paper accepted to JFM!
APS Division of Fluid Dynamics
Chicago, Illinois USA
179th Meeeting of the Acoustical Society of America
Chicago, Illinois USA
25th International Congress on Theroretical and Applied Mechanics