Summary
Alan Davila is a senior software engineer with a decade of experience turning scientific training into practical machine learning and data visualization solutions with high social impact. Based in Austin, he applies ML to big data, predictive analytics, and yield optimization, leveraging Python, TensorFlow, and modern visualization toolchains. At KLA-Tencor, he built ML-powered yield prediction from inspection and metrology data, automated wafer signature detection with Mask R-CNN, and created synthetic wafer data pipelines to train DNNs with minimal customer data, achieving substantial speedups and sub-second dashboards with Datashader. He also led QA efforts, cutting regression time from 400 to 16 hours and advancing CI and automated testing across teams. His early work in physics—ranging from high-energy experiments with MPI and ROOT to teaching and collaboration—gives him a unique ability to combine rigorous analysis with scalable software engineering. He holds a BS in Physics (Summa Cum Laude) and a PhD in High Energy Physics, reflecting a deep theoretical foundation and practical experimentation.
11 years of coding experience
11 years of employment as a software developer
BS (Summa Cum Laude), Physics, BS (Summa Cum Laude), Physics at The University of Texas at El Paso
PhD, High Energy Physics (Heavy Ion Collisions), PhD, High Energy Physics (Heavy Ion Collisions) at The University of Texas at Austin
Spanish, English