Enhancing the Accessibility of Privacy Policies Using Generative AI

This project addressed the complexity of privacy policies, often inaccessible to users due to their length and technical language. To address this challenge, our team created a generative artificial intelligence (generative AI) WebUI tool for summarizing privacy policies. The tool uses user-specific inputs, including age and education level, to tailor privacy policy summaries to a user’s level of understanding. Furthermore, we incorporated prompt engineering and readability metrics for improved comprehension of privacy policies. The tool was developed and refined based on feedback received from surveying 100 WPI students and beta testing our tool with 5 WPI students. By simplifying privacy policies, this tool empowers users to make informed decisions regarding their digital privacy, and this tool has the opportunity to be further developed by future teams

Automating Resonant Frequency and Weight Characterization of Percussion Drumsticks at Vater Percussion

The objective of the project is to design an automated system to sort drumsticks based on weight and resonant frequency for Vater Percussion. The rationale for the project is that by automating this sorting process, Vater employees will be able to spend more time on other processes in their facility that require more human attention and industry knowledge. The state of the art is a manual, human-run sorting process with three workstations. The methods used include axiomatic design, time studies, financial analysis, CAD modeling, and an in-person simulation of our design with the use of a collaborative robot. The results show that we were able to design a sorting system with independent tasks that decreases processing time and necessary man-hours. Our team can reach two fundamental conclusions based on the results. First, an automated sorting system is effective in relieving personnel to focus on more important tasks. Second, an automated sorting system has a positive return-on-investment due to the man-hours it saves. Our proof-of-concept demonstrates the effectiveness of using a collaborative robot to automate the stick sorting process and achieve the results stated above

Renovation of WPI Townhouses for Community Space

Worcester Polytechnic Institute has invested time and resources into exploring potential redesigns and renovations of the Townhouses to increase the housing option’s desirability. This Major Qualifying Project focuses on designing and renovating the Townhouses to incorporate a community space using architectural design and structural engineering analysis. The scope of the proposed renovation includes the design of architectural programming and concepts, the structural schemes of a wood structure and a wood-steel hybrid structure, and mechanical systems based on changes to the building envelope and building thermal calculations

Understanding Contrastive Learning in Computer Vision: Theory and Applications

Self-supervised representation learning has drawn great attention in the computer vision community due to its potential of alleviating human annotations for a large amount of data. Specifically, contrastive learning has recently become the dominant method in self-supervised learning and has shown competitive performance over its supervised counterpart on several downstream tasks such as classification, object detection, segmentation. Despite its empirical success, the theoretical foundations and geometric assumptions behind contrastive learning remain underexplored. To address this, our work begins with a theoretical investigation into the learning dynamics of contrastive objectives. We show that a model learned by deep contrastive learning with a family of loss functions such as InfoNCE essentially approximates one-class support vector machines (SVMs) with millions of neural tangent kernels (NTKs). This result comes from the fact that the gradients in contrastive learning can be interpreted as stochastic learning of SVMs, which is equivalent to a sequence of negative data augmentation and sample reweighting operations. Our analysis provides unique insights that: (1) truly "hard'' negative samples are the outliers of one-class SVMs in the NTK spaces, and (2) the loss parametrizes the sample weights. We prove that under mild conditions the gradient in contrastive learning as a classifier needs T negative samples to achieve a regret of \Tilde{O}\left(\frac{1}{T}\right), indicating that in contrastive learning more negative samples should be able to improve the performance with proper sample weights. We demonstrate our results by showing that visually "easy'' negative data augmentation such as purely random Gaussian noise can achieve very similar performance to visually ``hard'' negative data augmentation. Building on this theoretical foundation, we further explore the geometric structure of contrastive learning, particularly in non-Euclidean spaces. Recent work has demonstrated the empirical strength of hyperbolic embeddings in metric learning tasks, yet their integration with contrastive frameworks lacks comprehensive theoretical and empirical grounding. In this dissertation, we investigate the effects of integrating hyperbolic space into metric learning, particularly when training with contrastive loss. We identify a need for a comprehensive comparison between Euclidean and hyperbolic spaces regarding the temperature effect in the contrastive loss within the existing literature. To address this gap, we conduct an extensive investigation to benchmark the results of Vision Transformers (ViTs) using a hybrid objective function that combines loss from Euclidean and hyperbolic spaces. Additionally, we provide a theoretical analysis of the observed performance improvement. We also reveal that hyperbolic metric learning is highly related to hard negative sampling, providing insights for future work. This work will provide valuable data points and experience in understanding hyperbolic image embeddings. In addition to these theoretical and geometric perspectives, we explore how contrastive learning can enhance task-specific objectives. We proposes Info Chamfer distance (InfoCD), a novel contrastive Chamfer distance loss tailored for point cloud alignment tasks. By dispersing matched points to align point cloud distributions, InfoCD yields enhanced surface similarity metrics. Importantly, the research demonstrates that minimizing InfoCD equates to maximizing a lower bound of mutual information between geometric surfaces, resulting in a regularized CD metric that is both robust and computationally efficient for deep learning applications. Extending the utility of contrastive learning to generative modeling, we apply its regularization properties to Single Image Super-Resolution (SISR) through a plug-and-play method called CDSR (Contrastive Discriminator for Super-Resolution). Unlike prior methods that rely on semantic features from large pretrained networks, CDSR enhances discriminator sensitivity by leveraging data-driven contrastive signals, without incurring additional computational costs. This approach directs the generator toward producing perceptually richer outputs while maintaining low distortion, as confirmed by extensive experiments across various benchmarks. From theory to application, and from Euclidean to hyperbolic domains, this dissertation broadens the understanding and applicability of contrastive methods in both representation and generative learning

Data-Driven Marketing Analysis for Music Worcester

Music Worcester is a non-profit organization and a key pillar in the cultural arts community in the Greater Worcester area. They host a myriad of musical performances, including choral, dance, brass, classical, and contemporary music. This project analyzes Music Worcester’s ticket sales data, utilizing patron segmentation, time series analysis, statistical modeling, and data visualization techniques to identify customer tendencies. Based on these results, marketing strategies were proposed to better target key demographics, increase ticket sales, and encourage repeat patronage

Unmanned ground vehicle mechanical design for off-road mobility

In this MQP, the team redesigned the Autonomous Vehicle Mobility Institute's Unmanned Ground Vehicle (UGV) to serve as a testing platform for off-road vehicle performance in unstructured environments. The project addressed three main tasks: balancing weight distribution, redesigning the power system, and integrating a stereo camera into the perception system with corresponding software upgrades. To achieve balanced weight distribution, custom brackets were designed and manufactured to correct the original asymmetrical chassis. The power system was significantly improved by replacing the batteries and adding peripherals, reducing weight by over 65% (from 100 lb to 34 lb) while increasing capacity from 35 Ah to 100 Ah. Additionally, stereo vision was integrated into the perception system, the operating system was transitioned from Windows to Ubuntu Linux, and software enhancements—including comprehensive documentation, code refactoring, declarative configurations, and version control—were implemented. These improvements position the UGV as an advanced platform for future autonomous development

Creating a Resource Guidebook for Underserved Populations in Cuenca, Ecuador

This project aimed to increase awareness of essential local services available in Cuenca, Ecuador. In collaboration with The Cuenca Foundation and the Saint Francis Catholic Community of Cuenca, we created a website and paper pamphlet with a list of organizations and the details to utilize them. Our findings emphasize the need for improved outreach and streamlined communication between organizations and those in need. We recommend expanding upon the data pool and listed organizations, as well as fostering stronger community connections to keep this guide relevant

Exploring Coding Practices of Neurodiverse Students

The field of computing has long valued individuals with aptitude in areas such as problem solving, pattern identification, and creativity. While some neurodiverse learners’ cognitive strengths lie in certain areas of computational thinking [16], such as problem decomposition, neurodiverse retention and participation in higher education has remained low relative to their neurotypical peers due to systematic and social barriers [6]. Over time, a focus on evaluation metrics based on strong test-taking and study skills rather than their knowledge, coupled with possible frustrations relating to unclear objectives can lead to boredom, burnout, and culminate in pursuing another field. To better understand how neurodiverse students interpret and respond to a given task based off of the clarity of the intended goal, we conducted a study among college students (N = 49), of which 10 were neurodiverse and 39 were non-neurodiverse, where participants completed two computer programming tasks. One task was more deduction-based, using the process of elimination to create equal groups, and the other required more spatial reasoning to draw a rectangle according to specific criteria. Both tasks had prompts of either a well-defined or ill-defined nature. We identified that the ambiguity of the prompt had a statistically significant impact on the correctness of the solution generated for our deduction-based task, p = .001. While we saw certain trends which were present in neurodiverse solutions but not non-neurodiverse solutions, such as the frequency of small errors in impacting correctness, our limited sample size made it difficult to conclude if the neurodiversity of the participants explains the differences we observed

Birth Centers and Midwives: Keys to Reproductive Justice

Midwives solve key areas of inequality surrounding access to childbirth care by prioritizing mothers' choice in all matters related to the birthing process. They give a unique focus to the needs of the mothers. Due to Massachusetts regulations, there is only one certified birth center in the state. This study aims to examine the need for midwives and midwife-run independent birth centers in Worcester. To accomplish this goal, the team reviewed state-distributed surveys regarding maternal outcomes, followed by interviews with leaders in maternal health. The knowledge gained informed a survey translated into various languages for future study teams. The team found racial disparities in maternal outcomes and determined greater public support for birth centers could help improve this issue

Enhancing Alzheimer's Care at Christopher House Through Sensory Stimulation Interventions

As we age, our senses deteriorate and become less responsive. In those with Alzheimer’s disease (AD), numerous senses, including vision, hearing, smell, touch, and taste become severely impaired. Prior studies have shown that these sensory impairments may prompt various behavioral and psychological symptoms and exacerbate cognitive decline. Thus, engaging in meaningful and intentional sensory activities to stimulate these senses is instrumental. Unfortunately, these sensory activities are often underutilized at dementia nursing facilities, thus leaving residents at severe risk of sensory deprivation. As part of this project, we proposed to introduce sensory stimulation interventions to the Brookside dementia unit at Christopher House, a nursing facility in Worcester, Massachusetts. To achieve this, we conducted 3 in-depth interviews with staff at Christopher House and 17 interviews with sensory and Alzheimer’s experts worldwide to identify gaps and best practices. In addition, we performed a systematic review of 20 multisensory studies, including randomized controlled trials and quasi-experimental studies, between 2000 and 2024 to seek evidence-based practices. Our findings underscored the significance of personalization, meaning and purpose, intentional engagement, consistency, direct attention, sensory overload, and staff training when designing and facilitating these activities. We summarized these findings in the form of a 3D pilot render of a multisensory room and a personalized sensory handbook for activities staff. In sum, our findings revealed the remarkable potential of sensory interventions to revolutionize AD care