Derek W.

Key Skills

Software

No software listed

Top Subject Matter

Machine Learning Models / Gravitational wave detection / LIGO noise modeling

Materials characterization / superconductors / neutron scattering

Quantum chemistry / photoionization modeling / HPC

Top Data Types

3D Sensor

Image

Text

Top Task Types

Data Collection

Segmentation

Freelancer Overview

Research Assistant, University of Minnesota. Brings 4+ years of experience across complex professional workflows, research, and quality-focused execution. Work involved training ML models on gravitational wave data from LIGO in order to detect and characterize wave patterns. Education includes Bachelor of Science in Physics, University of Minnesota (2026). AI-training focus includes data types such as 3D sensor data involving geo-location, gps time, SNR, etc., and labeling workflows including Computer Programming, Image Segmentation, and Data Collection.

Labeling Experience

Undergraduate Researcher (Materials Science), University of Minnesota

OtherData Collection

Designed and executed iterative experimental protocols to produce single-crystal samples, while building and operating instrumentation pipelines to collect transport and electromagnetic response data. Performed physics-driven analysis and visualization using custom Python/MATLAB programs to derive characterization insights from experimental measurements. Coordinated field-scale neutron scattering experiments with multi-institutional teams to support strain-dependent magnetic ordering studies. • Developed experimental data collection workflows for transport/electromagnetic response • Implemented custom Python/MATLAB analysis for visualization and interpretation • Supported neutron scattering experiments and coordinated with multi-institutional collaborators • Studied relationships between strain, impurity, temperature, and lattice behavior

2023 - Present

Research Assistant (Gravitational Wave Detection), University of Minnesota

Developed automated signal injection pipelines that embedded 100,000+ simulated gravitational wave signals into real LIGO noise datasets for model training and benchmarking. Restructured training data storage and retrieval to improve throughput while validating changes via controlled before/after benchmarking. Performed dataset-wide evaluation and benchmarking to measure sensitivity improvements and support Bayesian parameter estimation workflows. • Created and maintained simulation-to-training dataset generation logic • Implemented data storage/retrieval architecture and ran benchmarking studies • Applied Bayesian parameter estimation to infer physical parameters from waveform data • Validated model improvements across large datasets

2023 - 2026

Research Intern (Quantum Chemistry & Photoionization), Kansas State University

Other

Built and executed high-fidelity photoionization models of chiral molecules using quantum chemistry simulations on national HPC infrastructure. Interpreted simulation results independently and adapted computational methods based on intermediate findings during the project lifecycle. • Ran advanced quantum chemistry calculations on HPC systems • Interpreted outputs to guide method adaptations • Used established simulation workflows for model development

2024 - 2024

Education

Work History