Team Projects
Team Projects are a hallmark of PGSS and allow students to conduct scientific research with peers who are as passionate about the work as they are. Projects are led by a faculty member who provides a general idea or topic for research. The team members develop the project from those general guidelines by developing their own hypotheses, designing experiments, analyzing data, and drawing conclusions. Each year there are between 10 and 15 projects to choose from.
Team Projects meet for 3 hours a day twice a week in the first four weeks of the program. Week 5 of PGSS is designated "Team Project Week" when students wrap up program activity and spend most of their academic time working on their proejcts.
PGSS culminates in the PGSS Scientifc Symposium during which the teams present their work to their peers in the program, faculty, and invitied guests. In addition, students prepare a formal scientific paper to be submitted on the last day of the program that is published in the .
Selected Team Project Descriptions from 2026
Investigating the Evolutionary Conservation of Myosin Light Chain II | Proteins are the primary functional drivers of many cellular processes, and their structure and sequence can reveal deep insights into evolutionary relationships. In this project, you will explore the concept of protein conservation by examining myosin light chain II (MLC II), a key muscle protein conserved across animal species. In this project, you will design and conduct experiments to compare muscle protein profiles from a range of marine and terrestrial organisms of your choosing by first extracting and quantifying your protein. Your extracts will then be analyzed by Coomassie staining and western blot analysis to assess overall proteomic patterns and the presence of conserved MLC II isoforms. You will supplement your wet bench work with computational analyses of protein sequences, using bioinformatics tools to compare amino acid sequences and infer evolutionary relationships.
Computational Cognitive Neuroscience – Comparing Human and Machine Perception | Recent advances in artificial intelligence have produced deep neural network models that can recognize sounds and images with human-level accuracy. But do these models actually process information the same way our brains do? One way to investigate differences between model and human representations is by using "model metamers"—stimuli that the model says are the same but might be different to a human observer. In this team project, students will investigate the differences between human and machine perception by running an electroencephalography (EEG) experiment. We will record human brain activity in response to both natural stimuli and model metamers synthesized from a deep neural 4 network. We will ask if we see a difference in the EEG response between the natural and synthetic stimuli, and if so, at what time in the response? Students will gain hands-on experience in computational cognitive neuroscience by designing the stimulus comparisons, conducting the EEG experiment, and applying computational data analysis to the EEG responses to the natural and synthetic stimuli. Students will also learn about modern artificial intelligence models and how we can compare these computational models with biological sensory systems. Location: Baker Hall Basement (we will reserve conference room A55A, EEG experiments are down the hall).
Turn-Based Game Strategy | Designing algorithms to play turn-based games has been a pursuit of many programmers since computers began. In 1997, IBM programmers wrote an algorithm to defeat chess champion Garry Kasparov. It was not until 2017 that a computer algorithm was able to beat the best go players in the world. In this project, the students will choose a turn-based game of moderate complexity (such as Reversi, Mancala, Connect-4, or Pente, there are many good games out there for this project) and design an algorithm to play the game, including a simple interface for a human to challenge the algorithm. Each project will have 4-6 students, and there will be up to three different projects of this type.
Imaging the Andromeda Galaxy |he Allegheny Observatory has a 24" PlaneWave telescope with remote observing capabilities. Students will visit the observatory one afternoon for a tour and to learn to operate the telescope. One class period will be replaced with nighttime observing operating the telescope from campus. The team will image the Andromeda Galaxy and build a model of the mass in stars and dust. Using data from radio telescopes they will determine the rotation of the galaxy and use that and Kepler's 3rd law to build another model of the mass. Students will then compare the mass at various positions from these two different methods and estimate the amount and distribution of dark matter. Depending on the interests of the team, they will build a beautiful image, research possibilities for dark matter, look at a second galaxy or galaxy cluster and/or collect their own spectra.
Searching for elementary particle signatures | Why do elementary particles have mass? Physicists have long been stumped by this question. The framework that describes the existence and behavior of every elementary particle observed today, known as the Standard Model (SM), fails to provide an explanation for why particles acquire mass. It wasn’t until 2012, when a new particle known as the Higgs boson was discovered, that physicists were able to gain insight into this mystery. The Higgs boson was discovered at the Large Hadron Collider (LHC), the largest and most powerful particle accelerator in the world. Two beams of protons (with each proton traveling roughly 99.9999991% of the speed of light!) collided with each other to produce many showers of particles every second, which were then detected by large multipurpose detector experiments placed around the LHC. Physicists then used sophisticated data-analysis and computing techniques to measure the trajectories of these particles as well as their energies and momenta, allowing them to retroactively determine what particles were created in the collisions and discover the Higgs boson. In this project, students will have the opportunity to learn and practice important data-analysis techniques by searching for particle signatures in data taken by the LHC, with the ultimate goal of 7 recreating the Higgs-boson measurement from the data that led to its discovery. Through this process, students will gain a deeper understanding behind the theory of the Higgs boson as well as how current particle detectors (such as those used at the LHC) are constructed, engineered, and operated to detect particles. By the end of this project, students will have gained exposure and literacy in the tools ubiquitous in experimental particle physics and will (hopefully!) find an appreciation for the subatomic world and how physicists interact with it.