Sarah Wallace Portfolio

View My GitHub Profile

Sarah Wallace

Data Science • Machine Learning • Data Engineering | Python • Java • SQL • Docker

About Me

    Hi, I’m Sarah Wallace - a data science and AI practitioner based in Austin, Texas. I recently earned my B.S. in Computer Science (AI focus) and am pursuing an M.S. in Computer Science (AI/ML specialization). My academic and portfolio work spans end-to-end academic ML workflows: data exploration, feature engineering, model training and tuning, cross-validation, interpretability, and leakage audits.

    On the software engineering side, I’ve built and deployed Java/Spring Boot applications with Docker and REST APIs. This experience provides me with practical deployment exposure, which translates into containerization and API integration skills relevant to ML systems. I also document projects with stakeholder-style reports, highlighting value delivered in ways that resonate with both technical and non-technical audiences.

    With a strong foundation in ML fundamentals, including pipelines, feature engineering, cross-validation, and interpretability, I’m extending those skills into deep learning frameworks like PyTorch and TensorFlow. I already have knowledge of gradient descent, GPU acceleration, and neural network architectures from my coursework, and I’m reinforcing this through applied practice with Udemy courses and Google Cloud certification labs. Adopting these frameworks builds on familiar ML principles, with the main learning curve in framework syntax and APIs.

    I’ve been recognized with academic awards in Computer Science Project Development, Lab Science, and Technical Communication. The Technical Communication award specifically cited my ability to adapt communication to the audience’s goals and roles, a skill I now apply to bridging the gap between technical teams, stakeholders, and decision-makers. Combined with prior sales experience, this enables me to deliver not only robust ML systems but also clear, actionable insights.

    I also hold an ITIL v4 certification, which integrates Agile, Lean, and DevOps principles into service delivery. This provides me with exposure to Agile-aligned development methodologies and enhances my ability to work effectively in structured, cross-functional program environments. I’m actively seeking opportunities across ML/AI engineering, data science, and AI-focused program management internships, where I can apply this mix of technical depth, analytical rigor, and audience-centered communication.

Looking for the engineering details? My GitHub Landing Page has full skills lists and technical project breakdowns.

OR request read-only access to project code here → https://swall1545.github.io/access (include your GitHub username, work email, and project names)

Skills & Technologies Highlights

    I bring an integrated foundation across applied machine learning (pipelines, feature engineering, interpretability), computer science fundamentals (algorithms, data structures, performance analysis), and mathematics (calculus, linear algebra, probability, statistics).

    For a full technical breakdown (frameworks, algorithms, testing stacks, etc.), see my GitHub README.

Overview:

Project Write-Ups

Selected case studies in data science, machine learning, and optimization.
Code is private (academic policy) but recruiters can request read-only access → Access Form.

Alzheimer’s Disease Progression Prediction (D797)
Built a scikit-learn pipeline (ColumnTransformer + RandomForest) to predict Alzheimer’s risk from demographic and clinical data. Validated with k-fold CV and conducted a feature leakage audit on cluster-derived variables. Delivered stakeholder-style report and Jupyter visuals.
Skills: Python, scikit-learn, Pandas, Random Forest, SHAP

Air Quality Prediction & Model Optimization (D682)
Developed XGBoost and Random Forest models to forecast pollutant levels from weather and emissions data. Applied advanced feature engineering, outlier handling, SHAP analysis, and hyperparameter tuning, achieving measurable RMSE improvements.
Skills: Python, XGBoost, Random Forest, scikit-learn, SHAP

Ambulance Dispatch Route Optimization (D795)
Implemented Dijkstra’s and Floyd-Warshall algorithms for emergency routing with priority triage. Benchmarked computational complexity and demonstrated ~7.7× faster per-call routing after all-pairs precompute.
Skills: Python, Algorithms, Performance Analysis

Edge AI Capstone: Operator Fusion with MLIR
Proposed and designed an MLIR operator-fusion pass to optimize edge inference. Authored system requirements, architecture, and implementation plan, integrating both technical and business cases. Peer-reviewed and refined via quantitative and qualitative analysis.
Skills: MLIR, C++, Compiler Optimization, Edge ML, Docker

Education

Western Governors University (WGU)
M.S., Computer Science (AI/ML Specialization) — Expected 2027

B.S., Computer Science (AI focus) — 2025

Honors: Certificates of Excellence (Project Development, Technical Communication, Lab Science) · Sigma Alpha Pi Honor Society

Relevant Coursework (selected):

Certifications:

Contact