Seminar: Data Science from a Mathematical Viewpoint

Data Science from a Mathematical Viewpoint

• Speaker: Dr. Matthew Horak, Senior Data Scientist at Lockheed Martin
• Date: Friday, March 6, 2020
• Time: 3:30 pm – 5:00 pm (refreshment at 3:00 pm)
• Location: BANN 501

Abstract:

“Data Science” means many different things to different people and is practiced in many different ways at different institutions.  Much of the differences come from the background and skills of the individual and the needs of the institution.  In this talk, I will give a quick overview of some of the basic aspects that seem to be shared by most data science work before moving into a more detailed picture of what my job as a strongly mathematically inclined data scientist at an aerospace engineering corporation looks like.  I will end with a short abstract mathematical problem of the type routinely encountered by my group.

Bio:

Dr. Matthew Horak joined Lockheed Martin in 2018 after spending about 15 years in academia.  His formal training and early academic career focused on the “pure” mathematics areas of geometric group theory and computational geometry.  From there, it moved towards probability and machine learning, which brought him to the data science group in Space Systems at Lockheed Martin.  Most of his work at Lockheed involves machine learning and mathematical analysis for the Space IT group, including anomaly detection in satellite telemetry data as well as projects to improve operational models for satellite engines and a project to develop tool for analysis and visualization of supply chain risk.

Edited 2/15/2020 as CS Seminar Room has been changed to BANN 501.