Understanding and taming plasma instabilities
I use simulations and machine learning to understand the consequences and causes of plasma instabilities in magnetic fusion devices. My work spans electromagnetic loads in the W7-X stellarator, interpretable machine learning analysis of the tokamak density limit, real-time instability-avoidance experiments at DIII-D, and the economic impact of disruptive instabilities.
What I work on
01 / Current workConsequences: electromagnetic loads induced in W7-X
What happens to the machine when the plasma pops.
Whenever the magnetic field inside a fusion device changes quickly, currents are induced in the conducting structures. Those currents interact with the background field and pull on the machine. Predicting the resulting forces is a prerequisite for designing future tokamaks and stellarators and safely operating the ones we have now.
02 / PhD thesisCauses: interpretable machine learning
Clarifying the density limit threshold across five tokamaks.
The density limit is one of the fundamental bounds on tokamak operating space, and for forty years it has been estimated with a scaling that does not involve the plasma edge conditions. Assembling a database across five machines showed that edge collisionality is the primary organizing parameter for the limit, and that a two-parameter dimensionless boundary predicts the threshold far better. I measured and applied feedback on that boundary in real time to avoid disruptions at DIII-D.
03 / OngoingContext: economic impact of disruptions
What plasma instabilities cost.
Disruption research is usually justified on physics grounds and evaluated on physics terms. But whether a disruption is a nuisance or a showstopper is an economic question, and answering it changes which physics problems are worth working on.
Recent talk
Collisionality scaling of the tokamak density limit
Elsewhere
Dr. Andrew D. Maris is a DOE Fusion Energy Sciences Postdoctoral Fellow at Columbia University, where he works with Prof. Carlos Paz-Soldan on transient off-normal phenomena in magnetically confined fusion plasmas.
He earned his B.A. from Carleton College in 2019 and his Ph.D. from the MIT Plasma Science and Fusion Center in 2026, advised by Cristina Rea, Robert Granetz, and Earl Marmar. His thesis, Prediction and control of the tokamak density limit, used machine-learning methods to understand, predict, and ultimately avoid the density limit in tokamaks.
Alongside plasma physics he works on fusion energy economics and policy, including published work on the cost of plasma disruptions to fusion power plants. He is a co-founder and former President of the Fusion Student Delegation, a student-led organization connecting early-career researchers with policymakers in the fusion ecosystem.