Chris Orban
Physics and Astronomy · The Ohio State University
Publications
125
Citations
1,173
Est. group size
~5
Recurring co-author estimate
Active years
24
Publishing since 2002
Chris Orban studies how ultra-intense lasers interact with matter, particularly how they can accelerate charged particles like electrons and protons and drive small-scale fusion reactions, using computer simulations and machine learning to understand and control these processes. The group also works on tools and methods for physics education, such as video tracking software and virtual reality plotting for students.
Publication output has declined from about 16 papers per year in 2017 to roughly 3-6 per year in recent years, suggesting a slowing pace of output over the last decade.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- Toward intelligent control of MeV electrons and protons from kHz repetition rate ultra-intense laser interactions
APL Machine Learning · 2025
- Applying Machine Learning Methods to Laser Acceleration of Protons: Synthetic Data for Exploring the High Repetition Rate Regime
Contributions to Plasma Physics · 2025
- Towards Intelligent Control of MeV Electrons and Protons from kHz Repetition Rate Ultra-Intense Laser Interactions
arXiv (Cornell University) · 2025
- Applying Machine Learning Methods to Laser Acceleration of Protons: Synthetic Data for Exploring the High Repetition Rate Regime
arXiv (Cornell University) · 2025
- Applying Machine‐Learning Methods to Laser Acceleration of Protons: Lessons Learned From Synthetic Data
Contributions to Plasma Physics · 2024
- Blurring the Boundaries between Science, Math and Computer Science
2024
- Detailed characterization of kHz-rate laser-driven fusion at a thin liquid sheet with a neutron detection suite
High Power Laser Science and Engineering · 2023
- Methods to Simplify Object Tracking in Video Data
The Physics Teacher · 2023
- Applying Machine Learning Methods to Laser Acceleration of Protons: Lessons Learned from Synthetic Data
arXiv (Cornell University) · 2023
- Particle-In-Cell Code Comparison for Ion Acceleration: EPOCH and Smilei
arXiv (Cornell University) · 2023
- Error Metrics from ML models trained in Desai et al.
Zenodo (CERN European Organization for Nuclear Research) · 2023
- Modified Fuchs et al. model Synthetic Data Sets
Zenodo (CERN European Organization for Nuclear Research) · 2023
- Code-to-code comparison and validation of the radiation-hydrodynamics capabilities of the FLASH code using a laboratory astrophysical jet
Physics of Plasmas · 2022
- PICUP Spring 2022 Webinar Series: Quantum Mechanics and Data Science for HS Physics
PICUP Collection · 2022
- Keep it simple, keep it fun: Interactivity is key for coding in introductory physics
PICUP Collection · 2022
- Bulletin of the American Physical Society×26
- arXiv (Cornell University)×14
- PICUP Collection×7
- APS Division of Plasma Physics Meeting Abstracts×5
- Physics of Plasmas×4
- Yutong Li
Physics and Astronomy · The Ohio State University
- R. L. Daskalova
Physics and Astronomy · The Ohio State University
- Anthony Zingale
Physics and Astronomy · The Ohio State University
- D. S. Clark
Physics and Astronomy · The Ohio State University
- German Tiscareno
Physics and Astronomy · The Ohio State University
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 19, 2026.
Claim or correct this profile