Raymond Schireman

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Portfolio

Dissertation: “Accelerating Theoretical Anharmonic Vibrational Analyses with Machine Learning”

Capstone Thesis: “Evaluation of Deep Eutectic solvents as extraction media for flavonoids from Mahonia aquifolium”

Automated production part reporting with Python, reducing reporting time by 80 % and increasing accuracy, enabling faster decision-making, and improving overall operational efficiency.
Designed and implemented data pipelines to integrate and process data from multiple sources, enhancing data accessibility for analytics.
Collaborated with cross-functional teams to translate business requirements into data models, driving successful project outcomes.
Developed comprehensive reports and dashboards that facilitated informed decision-making among stakeholders, promoting a data-driven culture.

Developed and implemented predictive machine learning models to accurately predict vibrational properties of molecular crystals 100x faster than quantum-mechanical methods, enabling real-time design feedback.
Leveraged Python programming skills to automate routine tasks and enhance efficiency of research processes, reducing the time to generate datasets for material property prediction by 75 %.
Utilized HPC resources to perform complex molecular dynamics simulations and quantum chemistry calculations, significantly accelerating research timelines and enhancing accuracy in predicting chemical behaviors.
Designed and implemented various hyperparameter optimization strategies to streamline data analysis processes in advanced academic research.
Presented results/findings at technical conferences and furnished several publications highlighting the application of techniques to simulate material properties.
Contributed to documentation for the Vermont Advanced Computing Core (VACC), facilitating the use of Intel’s oneAPI.
Utilized distributed training strategies to scale model training across many GPUs on clusters to rapidly train and prototype models

Designed and developed ML models to classify logos and insignias in user-uploaded images, increasing the speed at which accurate information about Russian soldiers/equipment in the area passes from local civilians on the ground to Ukrainian soldiers.

Served as a veteran contributor to PyTorch Lightning with 8 merged PRs deprecating functions/properties and setting up automated tests for the next release version.
Developed a feature to allow the evaluation of gradients in inference mode, preventing the copying of tensors, resulting in a 10% increase in inference speed.

Python-based webapp deployed on cloud services automating the generation of CRYSTAL23 input files for materials simulations.
Designed and implemented an intuitive user interface and hosted PostgreSQL database, streamlining the input parameter process and enhancing user experience.

RAG demo running Gemma-7b-it completely locally on an RTX 3070 to answer questions about the CRYSTAL23 manual.

“VSCF and VCI Analysis of the Anharmonic Coupling of Stretching Vibrations in Ice XI”, Champlain Area Chemistry Symposium, October 2022
“LightningANI: A Scalable Template for Building End-to-End Neural Network Potential Applications for Accelerated Material Property Prediction”, Northern New England Materials Research Conference, September 2022, Poster Presentation
“VSCF and VCI Analysis of the Anharmonic Coupling of Stretching Vibrations in Ice XI”, ACS Fall Meeting, August 2022
“Utilizing Machine Learning for the Calculation of Vibrational Frequencies of Molecular Crystals”, UVM Student Research Conference 2021, Poster Presentation
“Anharmonicity of Ice-XI with the VSCF and VCI Models”, UVM Student Research Conference 2020, Poster Presentation

Rama Oktavian, Raymond Schireman, Lawson T. Glasby, Guanming Huang, Federica Zanca, David Fairen-Jimenez, Michael T. Ruggiero, and Peyman Z. Moghadam. Computational Characterization of Zr-Oxide MOFs for Adsorption Applications. ACS Applied Materials & Interfaces, 14(51):56938–56947, 2022
Raymond Schireman, Jefferson Maul, Alessandro Erba, and Michael T. Ruggiero. Anharmonic Coupling of Stretching Vibrations in Ice: A Periodic VSCF and VCI Description. Journal of Chemical Theory and Computation, 18(7):4428–4437, 2022