- Ph.D., Civil and Environmental Engineering, Northwestern University, 2022
- M.S., Transportation Engineering and Planning, University of Toronto, Canada, 2016
- B.Sc., Industrial and Civil Engineering, Sharif University of Technology, Iran, 2014
Dr. Moein Hosseini works with clients to provide data-driven solutions to complex, interdisciplinary problems. He has 9+ years of experience applying statistical, optimization, and machine learning methods to provide solutions in the mobility, transportation, and civil infrastructure industries that include data visualization and dashboarding, robotic process automation (RPA), and natural language processing. His extensive experience includes working across all stages of the data science lifecycle, including data collection, data mining, data wrangling, exploratory data analysis, and machine learning implementation. Dr. Hosseini's interdisciplinary background spanning civil engineering, transportation engineering, and data science enables him to provide valuable and actionable support to clients facing complex and high-stakes business challenges.
Dr. Hosseini received his Ph.D. in Civil Engineering at Northwestern University, where his research used data analytics, data-driven optimization, and machine learning to address challenges caused by the continuous growth in travel demand on roadways and highways. As a research assistant in the Transportation Systems Analysis and Planning program, he worked on the opportunities created by vehicle automation technologies and advanced traffic management strategies to overcome these challenges caused by the continuous growth in travel demand.
Dr. Hosseini is experienced with data-oriented hardware and software development. He provides improved and customized solutions with long-lasting value that meet the current and anticipated future business needs of the client. His experience includes:
- Leading studies to achieve optimal alignment between the client's product or service and their customers;
- Optimizing data collection procedures through automation scripts;
- Software verification and validation; and
- Building software, data quality requirements, and testing plans to ensure the reliability and stability of the product.