CV

Education

• B.A. in Physics, Carleton College, 2019
• Ph.D in Applied Plasma Physics, Massachusetts Institute of Technology, 2025 (expected)

Work experience

• Research Assistant (2020 - Present)
  • Massachusetts Institute of Technology
  • Thesis project: Developing data-driven tools to understand, predict, and avoid “density limit” disruptions at DIII-D
  • Side project: Modeling the impact of disruptions on the economics of a tokamak power plant
  • Supervisor: Dr. Robert Granetz, Dr. Cristina Rea, Dr. Earl Marmar, Professor Nuno Loureiro
• National Ignition Facility (NIF) Summer Scholar (Summer 2019, 2020)
  • Trained machine learning models to predict neutron yield of fusion experiments at NIF, provide physical insight into degradation mechanism
  • Supervisor: Dr. Shahab Khan

Skills

• Plasma physics
• Data science
• Scientific machine learning/AI
  • Symbolic regression
  • Deep learning
• Public speaking
• Technical writing
• Fusion energy economic analysis

Publications and preprints

Chaos in the quantum Duffing oscillator in the semiclassical regime under parametrized dissipation

Maris, Andrew D., Bibek Pokharel, Sharan Ganjam Seshachallam, Moses ZR Misplon, and Arjendu K. Pattanayak. "Chaos in the quantum Duffing oscillator in the semiclassical regime under parametrized dissipation." Physical Review E 104, no. 2 (2021).

Investigating Boosted Decision Trees as a Guide for Inertial Confinement Fusion Design

Maris, Andrew D., Shahab F. Khan, Michael M. Pokornik, J. Luc Peterson, Kelli D. Humbird, and Steven W. Haan. "Investigating boosted decision trees as a guide for inertial confinement fusion design." Physics of Plasmas 30, no. 4 (2023): 042713.

The impact of disruptions on the economics of a tokamak power plant

Maris, Andrew D., Allen Wang, Cristina Rea, Robert Granetz, and Earl Marmar. "The impact of disruptions on the economics of a tokamak power plant." Fusion Science and Technology (2023).

Talks

What Jurassic Park gets wrong about Chaos Theory

May 18, 2019

Outreach talk at TEDx Asbury Park, Asbury Park, New Jersey

Investigating the robustness of machine learning-based disruption prediction with synthetic training data

October 14, 2022

Talk at 26th Workshop on MHD Stability Control, Spokane, Washington

The impact of disruptions on the economics of a tokamak power plant

March 14, 2023

Guest seminar at MIT Energy Intiative - Fusion Study Team, Cambridge, MA