Washington DC-Baltimore Area
🎓 Educational Background As a dedicated PhD candidate at George Mason University, specializing in Systems Engineering and Operations Research, I am deeply committed to driving innovation and solving intricate challenges within the realms of transportation, logistics, supply chain, and waste management. Equipped with a Master's and Bachelor's degree in Industrial Engineering, my academic foundation solidifies my expertise in optimizing complex systems. 💼 Professional Experience During my tenure as an Operations Research Intern at Amtrak, my focus included refurbishing crew scheduling models for On Board Service (OBS) and Train and Engineer (T&E) teams using Python. I also developed a scheduling optimization model for En-Route Cleaner (ERC) crews, aligning seamlessly with transportation system policies to elevate customer satisfaction. 💡 Passion for Innovation My professional journey is fueled by a passion for finding cutting-edge solutions that enhance the performance and sustainability of real-world systems. I bring a unique blend of analytical prowess, strategic thinking, and a commitment to continuous learning to every project. 🌐 Values and Perspective I am not just a researcher; I am a collaborator who values diversity and believes in the power of collective intelligence. Eager to bridge academia and industry, I am poised to contribute my rich experiences and perspectives to the professional landscape. 🚀 Seeking Opportunities As I embark on the next chapter of my career, I am actively seeking opportunities to leverage my skills and knowledge. If you are looking for a dynamic professional with a proven track record in optimizing real-world systems, let's connect and explore possibilities together!
Applied data science, machine learning, and optimization techniques to support transportation operations, service reliability, safety, and performance monitoring initiatives. Utilized Python, SQL, GIS data, statistical analysis, and automated data pipelines to transform large-scale operational data into actionable insights for engineering, planning, and business stakeholders. Designed and implemented analytical solutions across multiple operational domains, including weather-related rail risk analysis, operational performance monitoring, root-cause classification, schedule adherence and on-time performance (OTP) analysis, trackwork data quality validation, policy-impact evaluation, and fare-gate computer vision support. Applied supervised learning techniques, including classification and regression models, decision trees, rule extraction, statistical analysis, and scenario evaluation to identify patterns, assess operational outcomes, and support data-driven decision-making. Developed validation frameworks to evaluate business rules, improve data reliability, and ensure consistency between analytical outputs and operational requirements. Collaborated with cross-functional teams to translate complex technical concepts into clear, business-focused recommendations, enabling stakeholders with diverse technical backgrounds to understand findings and make informed decisions while maintaining appropriate confidentiality of operational processes and systems.
Enhanced large-scale crew scheduling models for On-Board Service (OBS) and Train & Engineer (T&E) teams in Python on top of an existing Java framework; validated scenarios and stress-tested constraints. Developed a MILP for En-Route Cleaner (ERC) crews; compared policy-compliant schedules using quantitative KPIs (coverage, duty balance, overtime risk). Created clear write-ups and briefings translating modeling choices, assumptions, and results to non-technical stakeholders.
Built and maintained short-horizon production schedules for machining/painting, balancing capacity, demand, and changeovers. Coordinated with inventory, procurement, and logistics to reduce bottlenecks; tracked plan adherence and drivers of variance. Used ERP reports and spreadsheets to monitor flow times and delivery performance, summarized insights for management.