Patrick Roos, PhD

Chief Data Scientist


Machine Learning
Artificial Intelligence
GCP, AWS, Cloud
Recommendation Engines


Ph.D. Computer Science, UMD
M.S. Computer Science, UMD
B.S. Data Science, College of Charleston



Recent Projects

Railroad Technology and Information Services
Next Generation Estimated Time of Arrival System
Built a highly-accurate system to estimate when cargo and intermodal trains will arrive on the North American major rail lines. The approach is implemented with a graph-style database in HBase and machine learning models developed on Spark. Set up a secured EMR cluster for the analysis.

Large Multinational Construction Company
Deep Learning for Complex Construction Sequencing
Extracted features from highly complex datasets and developed a reinforcement learning approach to determine the sequence and install order for millions of items (pipe, steel, concrete) on a construction site in three-dimensional space. Used GPU clusters to optimize construction planning prior to construction start.

Technical Expertise

  • Python data stack: scikit-learn, pandas, numpy, Jupyter, others
  • Machine Learning: Supervised and Unsupervised Learning, Reinforcement learning, MCTS, Survival Analysis, CLV, Customer Segmentation, Customer 360, Recommendation Engines
  • Google Cloud Platform: GKE, BQ, Cloud Run, Cloud Functions, AI Platform
  • Amazon Web Services: EC2, ECS, RedShift, EMR, Lambda, S3
  • Hadoop: Spark, MapReduce, Hive, HBase
  • Biomedical Informatics and Genomics: Bowtie, BLAST, Kraken