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Education

Doctor of Philosophy, Mechanical and Aerospace Engineering

University of Notre Dame / Notre Dame, IN, USA

January 2025

Bachelor of Science, Nuclear Engineering

Thomas Edison State University / Trenton, NJ, USA

December 2017

Associate of Science, Nuclear Engineering

Three Rivers Community College / Norwich, CT, USA

May 2015

Professional Experience

AI/ML Scientist

Los Alamos National Laboratory / Los Alamos, NM, USA

May 2026 - Present
  • Build and validate end-to-end ML pipelines (data ingestion, preprocessing, feature engineering, training, and evaluation) for multiphysics simulations.
  • Deploy ML models into production-level codes with performance monitoring and hands-on user adoption support.

Metropolis Postdoctoral Fellow

Los Alamos National Laboratory / Los Alamos, NM, USA

February 2025 - May 2026
  • Use machine learning to optimize energy grid group structures for nuclear data.
  • Apply reinforcement learning to train autonomous decision-making agents that minimize errors in nuclear data and numerical simulations.
  • Prototype large language model (LLM) and retrieval-augmented generation (RAG) applications for nuclear-data and simulation workflows.

Graduate Student Researcher

University of Notre Dame / Notre Dame, IN, USA

September 2018 - January 2025
  • Dissertation: Hybrid Numerical Methods and Solution Verification for the Neutron Transport Equation. DOI: 10.7274/27927858
  • Designed and developed an end-to-end ML pipeline (data ingestion, preprocessing, feature engineering, training, and evaluation) for data reduction in numerical simulations while remaining within a prescribed tolerance.
  • Performed solution verification for model risk evaluation of high-dimensional numerical simulations.

Graduate Student Assistant

Lawrence Livermore National Laboratory / Livermore, CA, USA

June 2021 - August 2021
  • Integrated parallel C++ production-level high-dimensional physics codes on Lawrence Livermore’s HPCs.
  • Communicated with multiple developer teams to ensure that the needs from both code base developers and the users of these code bases were met.

Graduate Student Assistant

Los Alamos National Laboratory / Los Alamos, NM, USA

May 2019 - July 2019
  • Worked in a collaborative environment to quantify experimental errors in nuclear data using novel machine learning and artificial intelligence.
  • Utilized machine learning techniques to analyze and identify outliers in nuclear cross-sectional data.

Boilers and Turbines Operator

NRG Energy Power Station / Middletown, CT, USA

June 2015 - June 2018
  • Responsible for safe and efficient operation, testing, inspection, and preventative maintenance of condensate, feed water, hydrogen, nitrogen, and fuel oil systems.
  • Make decisions within prescribed operating procedures and guidelines. Effectively communicate information to supervision and other plant personnel.

Journal Publications

Single Grid Error Estimation for Neutron Transport Solvers.

Ben Whewell and Ryan G. McClarren, (2025). Journal of Verification, Validation and Uncertainty Quantification. 10(2): 021001. DOI: 10.1115/1.4069426

Collision-Based Hybrid Method for Two-Dimensional Neutron Transport Problems.

Ben Whewell and Ryan G. McClarren, (2025). Nuclear Science and Engineering, 1-23. DOI: 10.1080/00295639.2025.2489778

Multigroup Neutron Transport Using a Collision-Based Hybrid Method.

Ben Whewell, Ryan G. McClarren, Cory D. Hauck, and Minwoo Shin, (2023). Nuclear Science and Engineering, 197:7, 1386-1405. DOI: 10.1080/00295639.2022.2154119

A low-rank power iteration scheme for neutron transport criticality problems.

Jonas Kusch, Benjamin Whewell, Ryan G. McClarren, and Martin Frank, (2022). Journal of Computational Physics, 470, 111587. DOI: 10.1016/j.jcp.2022.111587

Data Reduction in Deterministic Neutron Transport Calculations Using Machine Learning.

Ben Whewell and Ryan G. McClarren, (2022). Annals of Nuclear Energy, 176, p. 109276. DOI: 10.1016/j.anucene.2022.109276

Evaluating 239Pu(n,f) cross sections via machine learning using experimental data, covariances, and measurement features.

B. Whewell, M. Grosskopf, and D. Neudecker, (2020). Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors, and Associated Equipment, 978, p. 164305. DOI: 10.1016/j.nima.2020.164305

Technical Skills

Programming Languages

Python C++ R Cython HTML CSS SQL

Data Analysis & Processing

NumPy Pandas ETL PySpark Hadoop

Data Visualization

Matplotlib Seaborn Power BI Microsoft Excel Tableau

Databases

PostgreSQL MySQL SQLite Snowflake MongoDB Dolt

Cloud & DevOps

Microsoft Azure Amazon Web Services Git/GitHub Linux/Unix

MLOps & Deployment

MLflow Tensorboard Weights and Biases CI/CD Model Monitoring Feature Stores Docker Kubernetes Data Version Control (DVC)

Web Frameworks

FastAPI Flask Django

Machine Learning

PyTorch TensorFlow Keras Scikit-Learn XGBoost OpenCV Optuna

Reinforcement Learning

Gymnasium Stable Baselines3 PettingZoo Ray RLlib Multi-agent RL

NLP & LLMs

HuggingFace LlamaIndex LangChain LangGraph Generative AI RAG Vector Databases Ollama Pydantic