Welcome
Ian Kolaja is a research engineer working at the intersection of reactor physics, computational modeling, and data-driven methods. He earned his PhD in Nuclear Engineering from UC Berkeley, with a designated emphasis in Computational and Data Science and Engineering. He also completed his undergraduate degree at Berkeley after transferring from Fullerton College. Ian is a strong advocate for transfer students and for those impacted by the foster youth system.
Research
Ian's dissertation addressed key challenges in monitoring, modeling, and operating advanced reactor systems, with a focus on pebble-bed reactors (PBRs) and extensions to molten salt reactors (MSRs). His work combined high-fidelity simulation, synthetic gamma-ray spectroscopy, and machine learning to enable data-driven reactor operation.
Fuel pebbles discharged from PBRs must be characterized rapidly under intense radiation fields, where conventional gamma spectroscopy performs poorly. Ian adapted bent crystal diffraction (BCD) spectrometers, which are widely used in astrophysics but previously unexplored for reactor monitoring, as energy-selective filters for gamma rays. Coupled with machine learning models, these measurements enabled accurate inference of pebble properties such as burnup, residence time, and neutron fluence.
He also developed sequence models, including long short-term memory (LSTM) networks, to link measurement data with reactor operating history. These models were used to predict reactivity and flux or power distributions over time, enabling forecasting of reactor behavior and optimization of startup and running-in sequences.
For MSRs, Ian demonstrated that BCD spectrometers could isolate short-lived isotopes such as Np-239, enabling real-time tracking of plutonium production for safeguards and monitoring applications.
Ian's dissertation will be available on ProQuest on September 30th, 2026.
Industry Experience
Before his PhD, Ian worked at Kairos Power, where he was the first developer of the Kairos Power Advanced Core Simulator (KPACS). KPACS supported equilibrium and transient core studies for the company's generic fluoride-salt-cooled high-temperature reactor (gFHR) benchmark and contributed to early design analyses for KP-Hermes 1.
Outside of Nuclear
Ian has a wide range of creative pursuits including music production and filmmaking. Before pursuing science in community college, he wanted to be a film director. He participated and won awards at a handful of student film festivals in Southern California. He also recently has released his first EP, a collection of indie pop songs.
Publications
"Machine Learning Prediction of Pebble History in Pebble Bed Reactors"
I. Kolaja et al., in
Proceedings of the International Conference on Physics of Reactors (PHYSOR 2024), Apr. 2024, pp.
1437-1446.
doi: 10.13182/PHYSOR24-43890.
"Neutronics, thermal-hydraulics, and multi-physics benchmark models for a generic pebble-bed fluoride-salt-cooled high temperature reactor (FHR)"
N. Satvat et al., Nuclear Engineering and Design, vol. 384, no. C, Nov. 2021
doi: 10.1016/j.nucengdes.2021.111461.
"First Direct Measurements of Superheavy-Element Mass Numbers,"
J. M. Gates et al., Phys. Rev. Lett., vol. 121, no. 22, p. 222501, Nov. 2018,
doi: 10.1103/PhysRevLett.121.222501.
Preprints
"Predicting and forecasting reactivity and flux using long short-term memory
models in pebble bed reactors during run-in"
I. Kolaja et al., arXiv, Nov. 2025, url: https://arxiv.org/abs/2511.05118
"Burnup measurement using bent crystal diffraction spectrometers for pebble
bed reactor"
I. Kolaja et al., arXiv (Submitted to journal), Oct. 2025, url: https://arxiv.org/abs/2510.08835
Curriculum Vitae
You can download a PDF copy of my CV here.
Presentations
UC Berkeley Colloquium: PhD Exit Talk
Advanced Fuel Measurement and Operation Modeling for Pebble Bed Reactors
I. Kolaja, UC Berkeley, November 2025
Slides
Recording
PhD Qualifying Examination
I. Kolaja, UC Berkeley, May 2024
Slides
Machine Learning Prediction of Pebble History and Nuclide Concentration in PBRs
I. Kolaja et al., International Conference on Physics of Reactors (PHYSOR 2024), Apr. 2024
Session: Machine Learning and Artificial Intelligence for Reactor Physics: I
Slides
Resources
NE150/215M: Introduction to Nuclear Reactor Theory
UC Berkeley Nuclear Engineering Department, Spring 2022
GSI Discussion Slides by Ian Kolaja
Note: I joined the course a few weeks in, so weeks 1-3 are missing. These slides have not been edited since the course, so if you see any mistakes please let me know!
Discussion 4: Criticality
Discussion 5: Neutron Transport
Midterm 1 Review: Slides
Midterm 1 Review: Worksheet
Midterm 1 Review: Solutions
Discussion 6: Diffusion Equation Pt. 1
Discussion 7: Diffusion Equation Pt. 2
Discussion 8: Diffusion Equation Pt. 3
Discussion 9: Multigroup Diffusion
Discussion 10: Reactor Kinetics and Long-Term Core Behavior
Midterm 2 Review: Slides
Midterm 2 Review: Worksheet
Midterm 2 Review: Solutions
Resources for Graduate Students
Curated for Nuclear Engineering students
Best Practices for Scientific Computing (by Greg Wilson et al.)
Media
[Video] PhD Exit Talk at UC Berkeley Nuclear Engineering Colloquium: Advanced Fuel Measurement and Operation Modeling for Pebble Bed Reactors
By Ian Kolaja
Youtube, November 2025
[Video] ARCO: Making Advanced Reactors Smarter
By Ian Kolaja, Laura Shi, and Eddie Bird
Vimeo, June 2019
[Article] How UC Berkeley researchers are making nuclear reactors smarter
By Laura Shi, Ian Kolaja, and Eddie Bird
Fung Institute for Engineering Leadership, August 2019
[Video] Nuclear Values
By Ian Kolaja
Vimeo, March 2018