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Established in 1999 by Warren B. Phelps III ’69 (University trustee emeritus) and Patricia Hitchcock Phelps ’69, P'96, this fund supports the University Fellows program. Preference is granted to projects in the sciences, mathematics or computer science.

Algebraic Starscapes Using Complex Eigenvalues of Bohemian Matrices


The fundamental theorem of algebra states that every polynomial equation with complex coefficients has complex roots. These roots can be mapped on the complex plane, resulting in beautiful, geometric patterns called algebraic starscapes. I developed python code to compute and graph the complex eigenvalues of specific families of matrices, utilizing St. Lawrence's supercomputer for the millions of computations this required. I experimented with matrix size and integer inputs, as well as a multitude of variables in the graphic generation phase.

Sensor Data Transplantation for Resilient Drone Operation


Drones are uncrewed vehicles that operate under the direction of software running on a “flight computer” – a computer that controls the drone as it travels along a pre-programmed path. This research dealt with  increasing the resiliency of the drone in the face of computer failures. We developed a method for transplanting certain sensor data from one flight computer to another, allowing a backup computer to take over the job of the flight computer while the problem is fixed.

Identifying the different kind of peroxidase with activity in bryozoan Membranipora membranacea


My research project this summer aimed at determining the sequence of different peroxides present in membranipora RNA samples. By the means of using a transcriptome, a hypothetical DNA sequence of the different peroxides was theorized. By using the theoretical sequence, primers were made and used to perform an RT-PCR. The obtained DNA was inserted into a plasmid vector and cloned. The cloning process allowed DNA sequencing to be carried out. After this process, the actual DNA sequence of peroxidase mRNA was determined.

Using Deep Learning to Detect Fake Images


Camera quality on smart phones has been improving rapidly along with facial recognition technology. Photo and video editing apps have become increasingly popular and sophisticated. As people use these apps for entertainment, this has also raised concerns about how fake but realistic looking videos may sow confusion, uncertainty, and doubt about the veracity of images. These manipulated videos and other digital representation produced by artificial intelligence are called “deepfake”.