(Received continuously; accepted with minor revisions)
Abstract — His work is a
mix of scientific software engineering and machine learning research with engineering efforts across
GUI development, data pipelines, and HPC-scale analysis code. His research interests include
foundation models, AI agents, and data-driven methods for accelerating scientific work and
extracting insight from large-scale datasets. (He notes that he is very easily nerd-sniped.)
Correspondence via
GitHub,
LinkedIn, or
Google Scholar.
Fig. 1. Live stylized simulation, SrTiO3 with polychromatic beam. The specimen may be
reoriented by dragging.
Background
Prior to Argonne, the author worked in SaaS as an
undergraduate co-op SDET and contributed to small satellite flight software development.
During graduate school he worked on evolutionary computing applied to
DOE advanced manufacturing.
References
Yin, X., Du, M., Prince, M. H. & Cherukara, M. J. Artifact-centered
claim-aware observability for autonomous scientific agents. arXiv
2608.18312 (2026).
arXiv
Sainju, R., Jarosz, D., Shang, H., Prince, M., Aydelott, R. M.,
Cherukara, M. J. et al. A corrective agentic hybrid RAG and an
operations-grounded evaluation for a scientific facility. arXiv
2607.24663 (2026).
arXiv
Andrejevic, N., Du, M., Sharma, H., Horwath, J. P., Luo, A., Yin, X.,
Prince, M. et al. AlphaDiffract: automated crystallographic analysis of
powder X-ray diffraction data. arXiv 2603.23367 (2026).
arXiv
Vriza, A., Prince, M. H., Zhou, T., Chan, H. & Cherukara, M. J.
Operating advanced scientific instruments with AI agents that learn on the job.
npj Computational Materials 12, 160 (2026).
Article,
arXiv
Prince, M. H., Chan, H., Vriza, A., Zhou, T., Sastry, V. K., Luo, Y.,
Dearing, M. T. et al. Opportunities for retrieval and tool augmented large
language models in scientific facilities. npj Computational Materials
10, 251 (2024).
Article,
arXiv
Prince, M., Gürsoy, D., Sheyfer, D., Chard, R., Côté, B., Parraga, H.,
Frosik, B. et al. Demonstrating cross-facility data processing at scale with
Laue microdiffraction. SC'23 Workshops (2023).
Article
Prince, M. H., DeHaan, K. & Tauritz, D. R. A multi-objective
evolutionary algorithm approach for optimizing part quality aware assembly job
shop scheduling problems. EvoApplications (2021).
Preprint,
Article
Prince, M. H., McGehee, A. J. & Tauritz, D. R. EDM-DRL: toward
stable reinforcement learning through ensembled directed mutation.
EvoApplications (2021).
Preprint,
Article