Zigfried Hampel-Arias

Zigfried Hampel-Arias

Astrophysicist, GPU-burner, Outdoorsman.

The following links host my updated Resume and CV.

Professional and Technical Skills

I am experienced with:

  • Obtaining funding for and leading multi-disciplinary, multi-year scientific projects

  • Data analysis, in particular big data, statistics, deconvolution, modeling & simulation

  • Python, C, C++ programming

  • Machine learning methods (CV, GAN, AE, NeRFs, RL) and tools (PyTorch, scikit-learn, TensorFlow)

  • Scientific computing (NumPy, SciPy, pandas, CUDA, PyOpenCL)

  • Distributed and parallel computing (AWS, SLURM, PBS, HTCondor)

  • Data visualization (matplotlib, Plotly Dash, PyOpenGL)

  • Version control systems (git, SVN)

  • Public speaking, mentoring, and leadership roles

Education

PhD, Physics
University of Wisconsin—Madison

BS, Chemical Physics
Rice University

Languages Spoken

English, Español (Native)
Français (Working Proficiency),
Ελληνικα (ενα λιγο, i.e. basic)

Selected Articles

“Empowering Latin American Innovation for American Security”
Special Issue from Office of Naval Research, 2025. Article link forthcoming.

“DeepFake Detection Challenge Part II”
IQT, In-Q-Tel, 7 July, 2021, Article link

“DeepFake Detection Challenge”
IQT, In-Q-Tel, 21 June, 2021, Article link

“What AI Can and Cannot Do for the Intelligence Community”
Defense One, Defense One, 5 Jan., 2021, Article link

“Why IQT made the COVID-19 Diagnostic Accuracy Dash App.”
Modern Data, Modern.Data, 27 August, 2020, Article link

“Learning to Run a Power Network Challenge.”
Gab41, IQT Labs, 4 May, 2020, Article link

Selected Presentations

“3D Scene Understanding Machine Learning Methods for Hyperspectral Imagery”
Invited talk to Statistics Department at Texas A&M University, 31 Oct 2025.

“Machine Learning for Hyperspectral Imagery Applications: Trusting Our Models”
Invited talk to Statistics Department at Rice University, 30 Oct 2025.

“Machine Learning & Generative Modeling: A Physicist’s Perspective”
Guest lecture at Texas A&M University, 11 Nov 2024.

“Machine Learning for Hyperspectral Imagery Applications: Trusting Our Models”
Invited talk to Statistics Department at Utah State University, 15 Oct 2024.

“Approaches for Multi-modal Synthetic Media Detection”
Invited talk at Applied Imagery Pattern Recognition, 49th IEEE AIPR 2020.
Online info

“Operationalize COVID-19 Statistics with Dash - Featuring IQT’s COVID-19 Diagnostic Accuary Tool”
Invited Webinar Talk for Plotly Dash.
Webinar link

“PyUnfold: the Python Package for Iterative Unfolding”
Talk at IIHE, ULB, Bruxelles, Belgium.
Slides

“Overview of Semi-Supervised Classification with GCNs”
Invited talk at ML6, Ghent, Belgium.
Slides

“Introduction to Machine Learning Workshop”
Lecture & Keras Workshop at IIHE, ULB, Bruxelles, Belgium.
Slides

Honors and Awards

LANL National Security and International Studies Fellowship

DoE/DoS Embassy Science Fellowship

BAEF and WBI Postdoctoral Fellowships

NSF Graduate Research Fellowship

Fulbright Research Fellowship
See my blogpost regarding my Fulbright research experience.

MMUF Undergraduate Fellowship

Selected Publications

Below is a list of the publications for which I am a major contributor.

Hyperspectral neural radiance fields for 3D scene understanding of gas plumes SPIE Applications of Machine Learning, 2025. SPIE Publication

3D scene understanding of hyperspectral imagery using neural radiance fields and Gaussian splatting SPIE Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXXI, 2025. SPIE Publication

New Methods for New Space: Multi-Sensor Change Detection in Remote Sensing Imagery Pattern Recognition and Computer Vision in the New AI Era, pp. 161-187 (2025). Book Chapter

2d spectral representations and autoencoders for hyperspectral imagery classification and explanability IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), 2024. IEEE Publication

Adaptive Radio Frequency Target Localization Conference Proceedings of the Society for Experimental Mechanics Series, 2024. Springer Proceeding

Physics-guided neural networks for hyperspectral target identification SPIE Applications of Machine Learning, 2023. SPIE Publication

Hyperspectral Target Identification Using Physics-Guided Neural Networks with Explainability and Feature Attribution IEEE International Geoscience and Remote Sensing Symposium, 2023. IGARSS Publication

Experiments in anomalous change detection: improving detector discrimination through feature layers SPIE Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX, 2023. SPIE Publication

Localizing Radio Frequency Targets Using Reinforcement Learning
IEEE ROSE Conference, 2021. IEEE Publication

Multimodal Approach for DeepFake Detection
49th Annual IEEE AIPR 2020. IEEE Proceeding

L2RPN: Learning to Run a Power Network in a Sustainable World
NeurIPS 2020 White Paper. ChaLearn

Cosmic Ray Observations at the TeV Scale with the HAWC Observatory
I.e. my PhD thesis.

Observation of Anisotropy of TeV Cosmic Rays with Two Years of HAWC
Astrophys. J. 865, 57 (2018). arXiv:1805.01847
Official press release

PyUnfold: A Python Package for Iterative Unfolding
Journal of Open Source Software 3(26), 741 (2018). arXiv:1806.03350

Constraining the \(\bar{p}/p\) Ratio in TeV Cosmic Rays with the Moon Shadow
Phys. Rev. D 97, 102005 (2018). arXiv:1802.08913

The All Particle Cosmic Ray Energy Spectrum Measured by the HAWC Experiment from 10 to 500 TeV
Phys. Rev. D 96, 122001 (2017). (Editor’s Suggestion) arXiv:1710.00890
Official press release

All-Particle and Light-Component Cosmic Ray Energy Spectrum Measured by the HAWC Experiment
PoS: Proceedings of the 35th ICRC (Busan), 2017. arXiv:1801.05526

Towards a Measurement of the \(e^{+}e^{-}\) Flux Above 1 TeV with HAWC
PoS: Proceedings of the 34th ICRC (The Hague), 2015. arXiv:1508.03466