Transforming the puzzle of university scheduling with MatchMyLab

MatchMyLab is an AI-driven platform designed to help university departments allocate teaching assistants to courses and generate fair, conflict-free schedules across large undergraduate programs. It was built to replace the fragmented combination of spreadsheets, forms, email chains, and manual coordination that often makes TA scheduling slow, stressful, and difficult to manage at scale. By incorporating factors such as TA availability, prior experience, course needs, workload balance, and scheduling constraints, MatchMyLab provides a centralized and reusable system that makes the process faster, more transparent, and more explainable.
 

The project began in a much simpler form than it exists today. My earliest solution was not a full platform, but a Python script I built in response to the TA scheduling challenges I was experiencing firsthand. At the time, my goal was simply to use my spare time to make an exhausting and inefficient process more manageable. But it quickly became clear that the problem was much broader than a single course or a one-time fix. As word spread, other professors and instructors saw the potential of the tool and began expressing interest in using it as well. That early momentum made it clear that this could become something far more impactful than an individual workaround.
 

As MatchMyLab started to grow from an early idea into a real platform, I knew it had the potential to become much bigger than something I could build alone. At that point, I brought Julien Mutton and Gustavo Rodrigues Foz onto the project, and they quickly became integral to its development. What began with my own experience of the inefficiencies and frustrations of TA scheduling evolved into a genuinely collaborative effort, shaped by their technical insight, creativity, and sustained commitment. Together, we transformed the project from an early proof of concept into a scalable, AI-driven system designed to improve fairness, transparency, and efficiency in TA allocation and scheduling.
 

One of the most meaningful aspects of this project for me has been the opportunity to mentor and work alongside undergraduate collaborators whose contributions have had a lasting and substantial impact on the direction, quality, and scalability of MatchMyLab. Their work helped expand the tool into something capable of supporting large instructional environments and responding to the needs of a much broader group of users.

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MatchMyLab’s momentum has been matched by growing external recognition and support. The project was first selected in the Nittany AI Challenge as one of 15 teams out of more than 40 to receive prototype funding, then advanced as one of 10 finalists, earning additional support along the way. We ultimately placed 3rd overall and also earned 2nd place in the Penn State Office of the Physical Plant Award, securing a total of $4,800 in award funding to continue development. The project was also featured in the Penn State Biology Department newsletter and invited to present in the Penn State Biology Seminar Series as a student-led innovation addressing a major departmental need. Beyond its original setting, MatchMyLab has generated strong interest across campus, with pilots underway in the Eberly College of Science and interest emerging from additional institutions.