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Chemistry and BioChemisty I, Group D
No full text9:00 A.M. | Alanna Mahar \u2726 (Faculty Mentor: Ken Mills) Protein Splicing: Inteins from Extremophiles 9:15 A.M. | Elizabeth Guerriero \u2725 (Faculty Mentor: Sarah Petty) The Effect of Dipeptides on Beta-Sheet Formation Associated with Alzheimer\u27s Disease 9:30 A.M. | Avelin Thepsomphone \u2725 (Faculty Mentor: Andre Isaacs) Synthesis of Indolizines Via CuAAC 9:45 A.M. | Michael Irlandez \u2726 (Faculty Mentor: Elizabeth Landis) Nanoporous gold electrodes for electrochemical sensin
Health Studies
No full text9:00-10:30 A.M. | Faculty Mentor: Renee Beard Kate Leinson \u2726 Ariadna Lopez \u2725 Josephine Cunningham \u2725 Shannon Letendre \u2725 Margaret Sullivan \u272
Political Science
No full text4:00-4:30 P.M.| Lauren Reilly \u2725 (Faculty Mentor: Daniel Klinghard) Populist Rhetoric in U.S politics
4:30-5:00 P.M.| Pi Sigma Alpha Initiation Ceremony Faculty Mentor: Daniel Klinghard Marc Joseph Capobianco \u2725 Christina Cashion \u2725 Caitlin Desmond \u2725 Lucas Giarrusso \u2725 Nathan Howard \u2725 Brendan King \u2725 Liam McDonnel \u2725 Murphy Joshua Tubbs \u2725 Michelle Zychowski \u2725 Lucille Calcio \u2726 Shaye Callanan \u2726 Benjamin Capocci \u2726 Alexa Grace Carroll \u2726 Abigail Sarah Clark \u2726 Evan John Garcia \u2726 Madeline Hamm \u2726 Anika Phelps Heywood \u2726 Abigail Elizabeth Lane \u2726 Marc Edward Marandola Jr. \u2726 Scarlett Lynn McGuire \u2726 Cullan Kimball McHugh \u2726 Kelsey McMaster \u2726 Elise Marie O\u27Neill \u2726 Keara Papa \u2726 Ashwin Prabaharan \u2726 Katherine Russell \u2726
Graduating Members of 2025 Cords awarded by Catherine Mary Backfisch Luke Xavier Burch ‘25 Audrey Bruno ‘25 Christina Cashion ‘25 Caitlin Desmond ‘25 Olivia Dubin ‘25 Lucas Giarrusso ‘25 George Healey ‘25 Nathan Howard ‘25 Brendan King ‘25 Isabella McIntyre ‘25 Gwendolyn Minogue ‘25 Liam Murphy ‘25 Catherine Ann Pellini ‘25 Blaze Raz ‘25 Eamon Reilly ‘25 Juan Trillo ‘25 Thesis Honor Cord (Faculty Advisor: Alex Hindman) Joshua Tubbs ‘25 Catherine Wilcox ‘25 Thesis Honor Cord (Faculty Advisor: Daniel Klinghard) Claire Wolf ‘25 Thesis Honor Cord (Faculty Advisor: Sydney Carr Glenn) Michelle Zychowski ‘2
Theatre and Dance I
No full text2:00-3:00 P.M. | Shakespeare\u27s Lovers Faculty Mentor: Ed Isser Emma Butler \u2725 Feifan Zhao \u272
Theatre and Dance II
No full text3:30-5:00 PM | The Best of All Possible Worlds: A Music Theatre Review Faculty Mentor: Meaghan Deiter Frank Amuso \u2726 Feifan Zhao \u2725 Hillary Boadu \u2725 Jimmy Duffy \u2727 Emily Fox \u2726 Rowan Laufik \u2726 Sophie Rego \u2725 Meredith Shaw \u2727 Michael Sheehan \u2725 Lilly Percival \u2727 Victor Torres \u2727 Tyler Brackett \u2727 Zara Wilson \u2728 Vincent Sekafetz \u272
Achieving Fairness in Zoning Laws with Machine Learning
Full text linkZoning is a powerful regulatory tool that determines how municipalities use and develop land. The goal of zoning is to classify land use (e.g., residential, commercial, industrial) to maximize compatibility among neighboring parcels. Local zoning decisions, however, are made by small-sized boards, often through an opaque process, which raises concerns about bias and fairness. In the United States, zoning has historically prioritized single-family housing and thus created economic barriers that limit access to certain communities. Given the task of classifying land use and the wealth of geographical, demographic, and infrastructural data describing each parcel, the problem of bias in zoning could benefit from an algorithmic decision-making process aimed at equity. This paper explores zoning classification as a supervised learning task based on existing data in a locality (Worcester County, Massachusetts). We extensively collect publicly available data from multiple sources to predict zoning labels with machine learning models. Our results show that accurate predictions here require relatively complex models. Furthermore, we perform counterfactual analysis using socioeconomic features to explore their influence on current zoning decisions. Taken together, this exploratory study aims to assess zoning through machine learning and highlights opportunities for future work to apply computational techniques for fair zoning designation
The Hand That Gives and Takes (Creative)
No full textPineapples are historically associated with hospitality, exoticism, and wealth, particularly in European and colonial contexts. They symbolized luxury, generosity, and power—thus being understood as a gift or metaphor for ownership. In my image, the hand serves as a stand-in for colonialism, taking and giving, offering welcome while asserting control.
The pinky ring and lavish sleeve show elegance. The fingers are unblemished, inviting viewers to imagine the person behind these symbols of excess and power.
Each change of the three-layered orientation alters the balance. During editing, the object was given a blue tone after grime and imperfections were removed, evoking melancholy that is not perceived from the natural object. The large version of the object grounds the composition. Its inversion in the middle destabilizes. Flipped and reduced, it acts as a reflection of the first, introducing a tension between opulence and distortion, between giving and taking. The smallest sits at the top, reduced but upright, creating the pyramid.
Do refinement and abundance lose power once they are replicated and reframed? Arranging the hand in this ascending progression suggests a cyclical and elevating motion, offering a meditation on how symbols of status shift when repeated, inverted, and reduced.https://crossworks.holycross.edu/photographing_antiquity/1079/thumbnail.jp