Data and Spatial Analytics

Texas A&M Transportation Institute
1111 RELLIS Parkway
Bryan, TX 77807
(979) 317-2479
[email protected]

Kapil Arya is a Senior Research Engineer at Texas A&M Transportation Institute with over 20 years of specialized expertise in data science and transportation planning. He focuses on advanced analytics and machine learning applications. His unique combination of transportation domain knowledge and data science skills enables him to tackle complex challenges at the intersection of mobility, infrastructure, and emerging technologies.

With a Master of Computer Science in Data Science from the University of Illinois at Urbana-Champaign and a Master of Science in Civil Engineering from The Ohio State University, Kapil has developed a robust analytical foundation that spans both traditional transportation modeling and modern machine learning techniques. He is a registered Professional Engineer in Arizona and Texas, and holds Microsoft certifications as an Azure AI Engineer and Azure Data Scientist.

Throughout his career, Kapil has demonstrated ability to transform complex big data into actionable intelligence. Most recently, as a Staff Data Scientist at Walmart, he led the development of fraud detection systems using machine learning and graph analytics, achieving measurable improvements in precision and recall that translated to millions of dollars in fraud prevention. Prior to that, as Lead Data Scientist at Texas Health Resources, he managed a team developing ML-powered patient service optimization solutions and implemented natural language processing systems for sentiment analysis of patient feedback.

Kapil has expertise in transportation, having previously worked at TTI as a Travel Demand Modeler. During that tenure, he enhanced the TexPACK model used in Travel Demand Forecasting, developed methodologies for estimating traffic speeds using INRIX data, and provided support for travel demand modeling across Texas MPOs. His earlier work at Gannett Fleming and other consulting firms included calibrating route choice models for tolled facilities, developing mode choice models for transit studies, and analyzing household travel survey data for major Florida metropolitan regions.

His technical proficiency spans Python, PySpark, SQL, TransCAD, Cube, and ArcGIS, complemented by extensive experience in cloud platforms (Azure, GCP) and MLOps practices. Kapil excels at communicating complex analytical findings to diverse stakeholders, from technical teams to executives.