Carnegie Mellon vs. RPI: Which offers stronger undergraduate research opportunities?

I’m a high school senior interested in computer science and hoping to get involved in research as an undergraduate. I’m deciding between Carnegie Mellon and RPI, and research access is one of the main factors I’m considering. How do the two schools compare in terms of opportunities for undergraduates to join faculty research projects?
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Carnegie Mellon offers the stronger overall undergraduate research environment for a student focused on computer science. Its School of Computer Science is unusually large and research-intensive, with active work across artificial intelligence, robotics, systems, security, human-computer interaction, language technologies, and machine learning. Undergraduates can seek positions directly with labs and faculty, pursue research for course credit, and use campus programs such as the Undergraduate Research Office’s Small Undergraduate Research Grants and Meeting of the Minds symposium.

The biggest difference is the density and breadth of CMU’s computing research ecosystem. Many well-known research institutes and labs are integrated into undergraduate life, including the Robotics Institute, Software and Societal Systems Department, Language Technologies Institute, Human-Computer Interaction Institute, and CyLab. That gives a student more potential projects and research communities to explore if an initial lab is not the right match.

RPI also provides real undergraduate research access and can be especially appealing for students who want a smaller, highly technical campus. Its computer science faculty conduct research in areas such as data science, AI, cybersecurity, visualization, and computational science, and students may join projects through faculty outreach, independent study or research-credit arrangements, and summer research experiences. Because RPI is smaller, a proactive student may find it easier to develop close working relationships with individual professors early on.

Still, CMU has more specialized CS labs, more research-active graduate students and postdocs to learn from, and a more established pipeline from undergraduate coursework into advanced research. At either school, access is not automatic: students who contact faculty thoughtfully, build relevant skills through classes and projects, and start looking during their first year will have the best results.
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