MS&E Senior Projects
As a culmination of the major, MS&E's senior project course offers students a "learn-by-doing" experience.
Student teams start by selecting and defining a problem with a sponsor organization. They then design, test, analyze, and provide recommendations to the sponsor using their cumulative MS&E education. Finally, students present their engineered solutions for these in-depth, real-world problems.
Senior projects often open a variety of doors for students, including continued research engagements and employment opportunities with sponsor organizations.
But don't just take our word for it—four years after completing their senior project, a team of first-place project winners shared how they've used the skills they learned and mindsets they developed during the senior project as they've navigated the first few years of their careers (video is for Stanford affiliates only).
Hear from the Pie Ranch team—Alex Hellman, Sami Koire and Kate Salmon, with additions from teammate Maddie Kerr—below:
Explore senior projects from 2024 below on this page, and find earlier projects using these links:
2025 senior projects: Data-driven decisions
MS&E seniors presented their capstone projects in March 2025. Across a wide breadth of industries and using a variety of MS&E techniques, the students offered data-driven solutions to help organizations make better decisions.
Students drew on their course work—including mathematical modeling, systems analysis, organization theory, optimization, probability, statistics, ethics, computer science, and economics—to inform their work on the projects. Teams of three to five students acted as consultants to their sponsor organizations, tackling real-world problems in real-time over 10 weeks.
Many of this year's projects made use of AI-powered tools such as Stanford's STORM, LangGraph, and even a custom-built AI-powered platform to aid in decision-making for their sponsor organizations. All of the students used a combination of qualitative and quantitative methods to understand both the data and human factors at play.
Sponsors ranged from municipal governments like the City of Palo Alto, to established firms like State Farm, to non-profits like the Immigration Institute of the Bay Area that serve to impact the community at large. While the students wrapped their findings into a final presentation at the end of spring quarter, their work will continue to be utilized by their sponsor organizations long into the future.
Teams met regularly with their faculty mentors throughout the quarter to refine their work and fine-tune their final presentations. This year's faculty mentors included professors Riitta Katila, Elisabeth Paté-Cornell, Markus Pelger, Amin Saberi, and James Sweeney.
Note: Videos are included for teams whose sponsor organizations gave permission to share the final presentations. Each project also has a written summary. Not all teams gave permission to share their information, and thus not all students are represented below.
Hydrogen-powered garbage trucks for the City of Palo Alto
The team was tasked with assessing and identifying the most cost-effective, environmentally sustainable, and regulatory-compliant fleet composition for the city’s waste management operations.
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Bridging trust: A data-driven approach to student credibility
The team built a trust model for assessing risk in providing student loans, allowing Gradesmatch to sustainably help more students gain access to tertiary education in South Africa.
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Communicating social impact for Last Forest
The team helped Last Forest better quantify and communicate its social impact to meet Fair Trade Organization certification standards.
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Automating market research for venture capital
The team created a tool to help venture capitalists assess AI products as potential investment opportunities.
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Tailoring global success: Scaling Sleek Garments
The team created a custom expansion score calculator and profit model to aid Sleek Garments' decision-making as they seek to expand from their home market of Ghana into international markets.
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Optimization for Stanford Sports Nutrition
The team developed a custom mobile application to help Stanford Sports Nutrition optimize its inventory management system to save time, effort, and food waste.
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Data-driven approaches for incentivizing sales performance
By combining data analysis with firsthand insights, the team developed a sales performance simulator that allows agents to set realistic sales goals and adjust their approach based on market fluctuations.
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Measuring impact for Vocal Justice
The team created a custom, AI-powered analysis platform that will consistently measure Vocal Justice's program effectiveness without external assistance, saving them significant time and resources.
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