DeKalb, Illinois, United States
I am an Industrial & Systems Engineer and Ph.D. Candidate driven by a single mission: bridging the gap between advanced mathematical optimization and boots-on-the-ground operational excellence. Combining a deep background in heavy industry with rigorous academic research, I specialize in transforming complex, stochastic data into resilient supply chains and high-throughput manufacturing systems. Whether formulating a mathematical recourse decision for a disrupted logistics network or collaborating with engineering teams on large-scale system designs, I bring a unique combination of field-tested industrial discipline and advanced analytical insight to every challenge. Currently, my doctoral research at Northern Illinois University focuses on developing large-scale optimization frameworks to solve high-stakes supply chain and logistics challenges under deep uncertainty. Utilizing Mixed-Integer Linear Programming, two-stage stochastic models, and metaheuristics like Genetic Algorithms and Simulated Annealing, I design resilient networks capable of dynamically adapting to major disruptions. A primary pillar of this work includes modeling public health logistics networks—specifically optimizing regional testing infrastructure using CPLEX and Python to evaluate dynamic "surge and pivot" capabilities. Complementing this mathematical modeling is my expertise in statistical analysis and experimental design, leveraging Minitab, ANOVA, and Factorial Design of Experiments to validate system performance and reliability. This research draws directly from my background in heavy manufacturing and infrastructure operations. At Caterpillar Inc., I led cross-functional engineering initiatives to optimize fastener and hydraulic production for the CAT 336, leveraging predictive analytics, LLamasoft capacity planning, and lean principles to deliver 30%+ efficiency gains and $100K in annual scrap savings. Previously, at Power Tech Industries, I directed critical asset rehabilitation projects for East Coast Indian Railways, cutting turnaround times by 25% and costs by 11% while maintaining strict quality standards. Ultimately, I thrive at the intersection of operations research and practical implementation, translating advanced engineering theory into automated tracking systems, simulations, and dashboards that directly impact throughput. I am passionate about continuous improvement, collaborative innovation, and building intelligent supply chains.
Conduct large-scale data cleaning, validation, and preprocessing of public health and logistics datasets using Python and SQL. Develop analytical and optimization models to improve supply chain, transportation, and resource allocation systems. Apply operations research techniques, including network optimization and CPLEX-based modeling, to support data-driven decision-making. Design and implement route optimization and logistics models to improve efficiency and reduce operational costs. Create interactive dashboards and visualizations using Tableau and Power BI to communicate insights to stakeholders. Perform statistical analysis and simulation to evaluate system performance and identify process improvements. Collaborate with faculty, researchers, and public health partners to translate research findings into actionable recommendations. Document research methodologies, results, and findings for reports, presentations, and academic publications. Support grant-related research activities and ensure data accuracy and reproducibility. Mentor and assist graduate students in data analysis, modeling techniques, and research tools when needed.
• Led a team of 4 to boost CAT 336 line efficiency by 30% and cut cycle time by 15%. • Reconfigured layouts using lean methods; improved equipment output by 40%. • Programmed CNC tools for hydraulic parts, cutting tool wear by 20%. • Reduced machining defects by 25% through CNC error resolution. • Applied predictive analytics (85% accuracy) to lower downtime by 25%. • Delivered process plans 2 months early; accelerated SOP rollout by 20%. • Built MS Access database to cut instruction retrieval time by 50%. • Created Tableau dashboards improving defect visibility by 50%. • Used SQL + Python on 10K+ records to cut scrap by 20%, saving $100K/year. • Automated supplier tracking, saving 10+ hours/month. • Built supply chain dashboards reducing lead time by 15%, saving $75K. • Used LLamasoft to optimize production plans, increasing throughput by 12%.
• Cleaned complex SHIELD Illinois datasets using Python, boosting data accuracy by 30%. • Used Google API to generate geo-coordinates and calculate school-to-clinic travel times. • Applied Haversine formula for drone delivery optimization in pandemic logistics. • Built interactive Tableau dashboards to visualize routing insights for 2 states. • Streamlined COVID sample supply chain, reducing processing time by 25%. • Contributed to future pandemic planning via advanced route and distance modeling.
• Streamlined Indian Railways bogie rehab process, increasing efficiency by 30% and cutting turnaround time by 25%. • Reduced project cost by ₹7M (11%) through CNC layout precision and scrap management. • Delivered 100% on-time project execution with enhanced system workflows. • Directed cross-functional teams, improving coordination and project flow by 20%. • Enforced quality protocols that lowered defect rates by 18% and ensured regulatory compliance. • Improved labor utilization by 15% via workforce planning and task sequencing. • Applied lean techniques to cut material waste by 10% and optimize operations. • Tracked KPIs using data analytics, enabling predictive maintenance strategies. • Programmed and optimized CNC operations for axle frames and bogie housings, increasing machining accuracy by 20%. • Reduced rework and tool wear through CNC path verification and parameter refinement.
• Collaborated with planning and production teams on the “Fabrication and Rehabilitation of Rail Bogie Frames” project. • Analyzed production data using Excel and SQL to generate daily reports for informed stakeholder decisions. • Proposed a shared cutting lines method, reducing sheet metal costs by 5% and improving material utilization. • Built Tableau dashboards to monitor KPIs like material usage, output, and defect rates, enabling proactive decisions. • Conducted quality control analyses using statistical tools, driving improvements in reliability and consistency. • Strengthened skills in cost analysis, process optimization, and real-time operations tracking through cross-functional teamwork.