Caiya Wulf

Developed deep learning models using LSTM with attention mechanisms in PyTorch to predict anomalies in CNC machining operations. Applied advanced neural network architectures to industrial predictive maintenance, demonstrating practical applications of AI in manufacturing environments.

- Coordinated a cross-functional project to optimize freight movement, resulting in an estimated yearly savings of $400,000-$800,000 by implementing Lean Six Sigma guidelines and maintaining detailed project tracking using Microsoft Excel. - Identified frequent freight bottlenecks in the consolidation process by analyzing historical shipping data and implemented adjusted storage methods, reducing throughput time and improving overall efficiency within multiple departments. - Presented findings to regional management and transitioned the project to another engineer to ensure continuity and control of process changes after my departure.

Highlighted Coursework: Simulation & Probabilistic Modeling: Developed, validated, and tested a simulation of a library printing system, predicting savings and efficiency improvements. Linear Programming & Optimization: Applied advanced modeling techniques in Julia to solve complex logistical and operational problems. Machine Learning & Predictive Analytics: Explored classification, regression, and clustering algorithms for insights into complex datasets. Big Data Analytics: Worked with large-scale datasets, mastering tools like SQL, Python, and Tableau for data visualization and storytelling.