Andrew S.

Key Skills

Software

CVAT

Other

Internal/Proprietary Tooling

Top Subject Matter

Computer Science

Object Detection - Pavement Imagery

Segmentation - Corrosion Imagery

Top Data Types

Image

Video

Computer Code Programming

Top Task Types

Segmentation

Computer Programming/Coding

Object Detection

Freelancer Overview

Graduate Research Assistant (Texas State University) focusing on AI for pavement crack detection from surface images.. Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include any major programming language, data pipelines, and compute science problems. Education includes Master of Science, Texas State University (2026) and Bachelor of Science, Texas State University (2023).

Labeling Experience

Graduate Research Assistant (Texas State University) focusing on AI for pavement crack detection from surface images.

Don't discloseImage

Conducted AI research for TxDOT on pavement condition assessment using 2D/3D surface images and developed models to identify cracks. Built and maintained pipelines to prepare large pavement imaging datasets for model training and evaluation. Designed, trained, and evaluated CNN and transformer-based models to support crack detection outcomes for publication. • Data involved 2D/3D pavement surface imagery for crack detection research. • Created and maintained data pipelines for large-scale imaging datasets. • Trained CNN and transformer-based models used in the crack detection workflow. • Produced results leading to a conference poster and peer-reviewed publication.

2023 - 2025

Undergraduate Researcher - Texas State University

3D SensorSegmentation

As an undergraduate researcher, Andrew contributed to NASA-backed SpaceX-21 research using International Space Station derived samples. He developed data organization and quality-control pipelines and applied classical machine learning and deep learning methods for corrosion segmentation and labeling. The work required proficiency in data preparation, model development, and validation to support peer-reviewed scientific outcomes. • Supported corrosion-related research using microgravity-derived samples • Built data organization and quality-control pipelines • Applied classical ML and deep learning for corrosion segmentation and labeling • Contributed to a peer-reviewed publication

2023 - 2024

Undergraduate Researcher (Texas State University) applying ML to corrosion segmentation and labeling.

Don't discloseImageSegmentation

Developed data organization and quality-control pipelines for NASA-backed SpaceX-21 research involving bacterial adhesion and corrosion using ISS-derived samples. Applied classical machine learning and deep learning methods to corrosion segmentation and labeling to generate annotated training data for model development. Produced results that contributed to a peer-reviewed publication. • Built labeling/quality-control pipelines to support segmentation annotations. • Performed corrosion segmentation using classical ML and deep learning. • Labeled/annotated corrosion regions to create training data. • Contributed to a NASA-backed SpaceX-21 research effort culminating in publication.

2023 - 2024

Education

Work History