Phuc Dinh

“Correlation investigation for selective laser beam melting by machine learning” for the 3D printing industry to enhance monitoring of printed products. Collected sensor data from the printing chamber, preprocessed and fused the data together to create RGB (tri-color) images using Python. Used NumPy and Pandas as tools for data manipulation/processing as well as Scikit-Learn to normalize data, create training and testing data sets. Developed a Keras CNN model and achieved a classification accuracy of 98,7%

E-bike tool-suite - a program that collects, visualizes, and evaluates GPS location data for E-Bikes. I used Python on JupyterLab and Microsoft Power BI in order to collect, process, and visualize data. This led to an enhanced evaluation process of the E-Bike GPS test rides. Before that, it took hours of manual copy-pasting data from log files. The tool-suite ended up being adopted as part of the team’s workflow. Tech used: Python | JupyterLab | Git | Numpy | Pandas | Power BI | Matplotlib.

Ascenx offers offshore engineering services for global leaders like KLA-tencor and Lam Research. I developed a Bill of Material Watch, a tool to ensure wafer inspection tool parts being available on stock at all times. This resulted in increased reporting speed by 37%. Supported KLA-Tencor based in the USA with documents. Package investigated and designed. Generated weekly and monthly reports automatedly with VBA - Excel Macro which saved 25hrs of manual work weekly and reduced critical errors by 80%.