Ryley McConkey

Email / Github / Google Scholar

I’m a postdoc working with the Atomic Architects and Multiscale Mariners research groups at MIT. I’m working on data-driven methods for turbulence modelling, such as subgrid-scale modelling for LES, benchmarking for RANS closure modelling, and superresolution of turbulent flows. I love fluid mechanics! Check out my YouTube playlist, lectures, and blog posts.

News

• December 2025: I’ll be at the NeurIPS ML for Physical Sciences workshop. We’re presenting a poster based on our on accepted workshop paper on distributional symmetry in turbulence.

• November 2025: I presented our abstract on equivariance for subgrid scale closure modelling at an Interact session at the APS DFD 2025 meeting (poster).

• November 2025: Tyler Buchanan, Richard Dwight, Paola Cinella, and I are putting together a continuously running, field-wide benchmark for RANS turbulence modelling. It’s time we had a standardized benchmark for machine learning in RANS! The data and evaluation package is now public. See the description here. It’s being advertised as part of the 2026 ERCOFTAC ML for Fluids Workshop (link), but it will run beyond the conference. To participate, email Tyler: [email protected] .

• September 2025: We have a preprint out on distributional symmetry in turbulence, and how superresolution models can learn equivariance just from the rotational nature of turbulence data.

• April 2024: I presented at the Chalmers University of Technology Data Science and Artificial Intelligence Seminar Series (slides). I also went to IKEA in Sweden. What else?
• March 2024: I presented at the ERCOFTAC ML for Fluids Workshop in London (slides). It was a great workshop, and I enjoyed my time in London!

About me

I graduated from the University of Alberta in 2019 with a Bachelor’s of Science in Mechanical Engineering (co-op). Pursing an interest in turbulence and computational fluid dynamics (CFD), I then began a Master’s degree at the University of Waterloo. In my Master’s research, I was focused on simulating a new type of wind turbine which uses vortex induced vibration (VIV) to generate energy. Then, I direct transferred to a PhD in 2020. My PhD was focused on developing new turbulence models using machine learning. I completed a 6 month visit at the University of Manchester in 2022-2023, where I focused on data-driven turbulence modelling on complex 3D flows. After completing my PhD in 2024, I started a Postdoc at MIT.

My diverse experience includes mechanical design, software implementation, and industrial research and development. At MACH32, a medical device startup company, I designed and simulated novel autoinjectors and COVID-19 protection devices. I also worked on automating CFD simulations as a Software Developer at Orbital Stack, a wind engineering startup company. In the Research and Development group (Labs) at RWDI, I developed and implemented machine learning based tools to augment simulations and wind tunnel experiments.

Here is a playlist with my favourite fluid mechanics videos: