About Me

I am an experimental and computational materials scientist working at the intersection of AI for Science and materials physics, currently a Postdoctoral Researcher at Oak Ridge National Laboratory. I build ML and LLM tools grounded in real materials R&D, atomic-scale characterization, and physics-based modeling, with a focus on understanding how structure, disorder, magnetism, interfaces, and defects control material behavior.

I specialize in designing and executing complex neutron and synchrotron scattering experiments, analyzing high-dimensional experimental datasets, and building computational workflows that translate noisy measurements into validated physical insight. My background spans quantum materials, magnetic and topological systems, thin films, metal-organic interfaces, STM/MBE surface science, crystal synthesis, local-structure analysis, DFT, Reverse Monte Carlo modeling, and Python/HPC-based scientific computing.

Industry Direction

I am interested in roles where materials expertise, experimental problem-solving, and AI-driven modeling support the development of next-generation technologies. My strongest fit is in AI for Science / materials informatics, materials R&D, quantum device materials, semiconductor-adjacent materials, advanced metrology, and scientific computing for complex physical systems.

My goal is to translate deep research experience into practical R&D impact: connecting atomic-scale structure, disorder, interfaces, and defects to device-relevant material behavior, while building reproducible workflows that make complex characterization data more actionable for research and engineering teams.

Technical Strengths

Experimental Materials Research & Project Execution

I design and execute neutron and synchrotron scattering experiments on quantum, magnetic, topological, and strongly correlated materials. My work includes proposal development, beamline execution, sample-environment planning, data-quality troubleshooting, and coordination with facility scientists and collaborators under time-sensitive experimental conditions.

Materials Characterization, Synthesis & Experimental Systems

My hands-on experience includes neutron/X-ray diffraction, total scattering/PDF, diffuse scattering, inelastic neutron scattering, STM/SP-STM, MBE thin-film growth, UHV systems, crystal synthesis, inert-atmosphere handling, cryogenic/high-pressure measurements, and custom experimental setup integration. I use these methods to connect structure, disorder, interfaces, magnetism, and defects to material behavior.

Computational Modeling & Scientific Software

I develop Python/C++ workflows for scientific data analysis, inverse modeling, and materials simulation. My work includes Reverse Monte Carlo analysis, PDF modeling, magnetic refinement, DFT, phonon calculations, symmetry analysis, HPC workflows, and reproducible experiment–simulation comparison.

AI for Science — ML & LLM Agent Tooling

I build ML and LLM tools grounded in real materials R&D rather than black-box prediction. This includes browser-first scientific apps (Pyodide/WebGPU/React), LLM agents exposed to a pure scientific core through MCP tools that assess fits, sample posteriors, and suggest next steps, retrieval-grounded reasoning, local/offline inference, and evaluation harnesses that benchmark agent behavior against non-LLM baselines. Shipped in MATERIA, NEBULA3D, and RMCProfile Workbench, with Athanor as an exploratory closed-loop materials-screening agent.

Technical Stack

Materials Characterization & Metrology

Neutron Scattering Synchrotron X-ray Scattering X-ray Diffraction Total Scattering / PDF Diffuse Scattering Inelastic Neutron Scattering STM / SP-STM Surface Characterization Low-Temperature / High-Pressure Measurements

Materials Synthesis & Experimental Systems

Crystal Growth Solid-State Synthesis Inert-Atmosphere Handling MBE Thin-Film Growth UHV Systems Cryogenic Sample Environments High-Pressure Sample Environments Custom Experimental Setup Integration National-Lab Beamtime Execution

Scientific Computing & Modeling

Python C/C++ NumPy / SciPy / Pandas scikit-learn RMC / PDF Modeling Monte Carlo Methods Inverse Modeling Numerical Optimization DFT Phonon Calculations Magnetic / Crystallographic Refinement Model Validation

AI / ML & LLM Tooling

Machine Learning LLM Agents MCP / Agent Tools Retrieval-Grounded Reasoning (RAG) Local / Offline Inference (Ollama / LM Studio) Evaluation Harnesses Physics-Grounded Surrogate Models Browser-First ML (Pyodide / WebGPU) React / TypeScript

Data & Research Workflows

HPC / SLURM Linux / Bash Git / GitHub Reproducible Scientific Workflows Signal Extraction Scientific Visualization Automated Analysis Pipelines Experiment–Simulation Comparison

Education

• Ph.D. in Physics — Brown University (Providence, RI)