CV

Education

• B.S. in Mathematics, Caltech, 2016

Work experience

• 2018 - present: Data Scientist
  • The Aerospace Corporation
  • Selected Projects:
    • Deep reinforcement learning for autonomous systems
    • Anomaly detection for launch vehicle telemetry
    • Time series event classification
    • Physics-informed neural networks for surrogate modeling
    • Dynamics extraction from video and sensors
• 2017 - 2018: Graduate Teaching Assistant
  • University of California Los Angeles
  • Taught: ordinary differential equations, complex analysis, dynamical systems
• 2016 - 2017: Residential Counselor
  • The Gatton Academy of Mathematics and Science
  • Duties included: Event planning and hosting, curfew checks, tutoring, academic counseling, mental health support
• 2014 - 2016: Undergraduate Tutor
  • California Institute of Technology
  • Tutored:
    • Single & Multi-variable Calculus
    • Linear Algebra
    • Differential Equations
    • Abstract Algebra
    • Real and Complex Analysis

Skills

• Python
  • Numerical Computing: numpy, scipy
  • ML: pandas, scikit-learn, tensorflow, pytorch, jax, mlflow, ray (rllib, tune), openai-baselines, pyspark
  • Data Viz: matplotlib, seaborn, plotly, streamlit, blender
• LaTeX
• git
• docker

Publications

The Mattig Expression for a d-dimensional Gauss Bonnet FRWL Cosmology

Andrew, Keith; Steinfelds, Eric; Zolman, Nick. (2016). "The Mattig Expression for a d-dimensional Gauss Bonnet FRWL Cosmology." arXiv preprint.

The Origin and Evolution of the Galaxy Mass–Metallicity Relation

Xiangcheng Ma, Philip F. Hopkins, Claude-André Faucher-Giguère, Nick Zolman, Alexander L. Muratov, Dušan Kereš, Eliot Quataert, The origin and evolution of the galaxy mass–metallicity relation, Monthly Notices of the Royal Astronomical Society, Volume 456, Issue 2, 21 February 2016, Pages 2140–2156, https://doi.org/10.1093/mnras/stv2659

Adinkras, Dessins, Origami, and Supersymmetry Spectral Triples

Marcolli, M., Zolman, N. Adinkras, Dessins, Origami, and Supersymmetry Spectral Triples. P-Adic Num Ultrametr Anal Appl 11, 223–247 (2019). https://doi.org/10.1134/S2070046619030051

Learning to Predict 3D Rotational Dynamics from Images of a Rigid Body with Unknown Mass Distribution

Mason, J. J., Allen-Blanchette, C., Zolman, N., Davison, E., & Leonard, N. E. (2023). Learning to predict 3D rotational dynamics from images of a rigid body with unknown mass distribution. Aerospace, 10(11), 921.

A unified framework to enforce, discover, and promote symmetry in machine learning

Otto, Samuel E., et al. "A unified framework to enforce, discover, and promote symmetry in machine learning." arXiv preprint arXiv:2311.00212 (2023).

SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning

Zolman, Nicholas, et al. "SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning." arXiv preprint arXiv:2403.09110 (2024).