Data Science Commons^™

Cmc Thesis Chatbot, Luis Gomez

CMC Senior Theses

This GitHub repo is a senior thesis for Claremont McKenna College; it is a thesis about theses. The project is an interactive RAG-based chatbot that helps students, researchers, and faculty explore Claremont McKenna College senior theses. The goal was to create a domain-specific chatbot to show that it is possible to combat the limitations of AI, including hallucinations, outdated data, and lack of domain expertise. The website link is:

CMCThesisChatbot

Analyzing Political Sentiment On Micro-Blogging Data: A Lexicon And Machine Learning Approach To The 2024 U.S. Presidential Election, Ava Grey

CMC Senior Theses

This paper explores the trends in sentiment towards U.S. presidential candidates Kamala Harris and Donald Trump through micro-blogging social media text during the five months leading up to the election. Two datasets of varying sizes and origins were used to contextualize and validate analysis findings. The analyses include both a lexicon-based approach and a machine learning predictive method. Common sentiment analysis techniques like term frequency, term frequency inverse, various lexicons, and n-grams were utilized during the lexicon approach. During the modeling, a random forest was utilized in addition to the methods used during the lexicon approach. Results showed that overall …

Neural Correlates Of Attentional Biases In Dietary Choice: Role Of Childhood Socioeconomic Status, Justine Jamie N. Gotico

CMC Senior Theses

Childhood poverty has been shown to increase adult risk for obesity above and beyond its direct effects on adult socioeconomic status (SES). One proposed mechanism of these effects is by shifting behavioral patterns of dietary consumption and choice, for example by increasing rapid attention to high-calorie unhealthy foods. Yet, whether such neural mechanisms can explain observed differences in dietary behavior based on childhood SES remains an open question. Here we used event-related potentials (ERPs) to examine early attentional correlates of low childhood SES during a dietary choice task, based on research suggesting that early attentional biases toward high-calorie foods emerge …

Large Scale Machine Learning Over Knowledge Graphs, Bedirhan Gergin

Electronic Theses & Dissertations (2024 - present)

Knowledge graphs (KGs) have become popular across various fields, providing convenient access to web-based knowledge while storing and formalizing domain-specific information. By analyzing KGs, patterns, connections, and dependencies can be identified across different data sources, enabling the inference of new knowledge from given facts. As the use of KGs expands, the size of modern KGs has grown significantly, making them impossible to process within the main memory of a single computer. Distributed computing offers a viable solution to this challenge by leveraging the combined capabilities of multiple servers within a cluster. This thesis explores how distributed computing can be effectively …

Evaluating Multimodal Ai Systems: A Comparative Analysis Of Large Languagel Model-Based Models For Text, Image, And Video Generation, Azeezat O. Akinola

College of Graduate Studies: Theses & Dissertations

In the era of rapid technological advancement, efficient content generation, application development, and data management are crucial for meeting the demands of dynamic digital environments. This thesis uses state-of-the-art models to explore three core areas: AI-driven video content creation, text-to-image-to-text consistency, and automatic text summarization. The first study investigates the potential of AI-powered text-to-video generation to democratize video production and enhance storytelling. By comparing the performance of three models—ModelScope, Text2Video (Zero), and Motion Consistency—this study assessed the quality of generated videos using CLIP scores. It evaluated statistical significance through t-tests and homogeneity tests. Results indicate that ModelScope outperformed the others, …

In Search Of The Rational Voter In The 2020 Presidential Election: Understanding The Impact Of Voter Costs And Benefits On Turnout, Norou Diawara, Tiffany Henley, Samuel L. Brown, Md Iqbal Hossain

Mathematics & Statistics Faculty Publications

The ability to vote is one of the most valuable rights and privileges afforded by the Constitution of the United States to its citizens. For many, voting is not just a civic duty; it is also a choice. Voting is crucial to our democracy, and any changes to it may affect the efficiency of the democratic process. The bigger question is whether voters behave rationally by engaging in a cost-benefit calculus in deciding whether or not to vote. Using data science, this paper will examine the probability of voting and investigate its impact via cost and benefit among other variables …

The Effect Of Facial Phenotypes On Differential Performance Of Facial Recognition, Evan R. Garrett

Graduate Theses, Dissertations, and Problem Reports (ETD)

Facial recognition technology is utilized in many facets of life. As the use has become more widespread these systems have improved in reliability and performance approaching the level of human accuracy. With these improvements the problem of bias still remains as a persistent problem. Efforts have been made to minimize the bias prevalent in the systems via studies into various demographic factors, creating training datasets that have a more uniform distribution of subjects, and other methods. As facial recognition is one of the most utilized forms of biometric recognition it is vital to analyze potential causes of bias to help …

Applying The Matching Law To Major League Baseball (Mlb), Christopher Watkins, Vincent Berardi

Psychology Faculty Articles and Research

The application of the generalized matching equation (GME) has been detailed in a variety of sports, including football, basketball, and others. However, only a limited number of studies have focused on Major League Baseball (MLB), and they typically have examined ≤ 5 players and/or focused on a single behavior. This paper increases the generalizability of such work by using newly available, state-of-the-art data from thousands of players to explore the GME in several scenarios within three aspects of a baseball game - defense, pitching and batting. We found that the GME accurately summarized response allocation in most scenarios, with r …

Investigating The Impact Of Aerial Firefighting On Rate Of Wildfire Spread, Lindsay Ann Wiard

Graduate Student Theses, Dissertations, & Professional Papers

Aerial retardant drops are widely used in wildfire suppression, yet their effectiveness in slowing fire spread remains difficult to quantify at scale. This study evaluates the impact of aerial suppression on wildfire rate of spread (ROS) using a modeling framework that incorporates both observed (real) and counterfactual (synthetic) drop locations from a sample of 62 wildfires in Oregon. Synthetic drops were generated to simulate a no-suppression baseline, allowing us to compare changes in ROS in the presence and absence of suppression. We trained two random forest classifiers: one using both real and synthetic drops (the full model), and another using …

Error In The Loop: How Human Mistakes Can Improve Algorithmic Learning, Ryan W. Copus, Cait Spackman, Hannah Laqueur

Faculty Works

Algorithms often outperform humans in making decisions, in large part because they are more consistent. Despite this, there remains widespread demand to keep a “human in the loop” to address concerns about fairness and transparency. Although evidence suggests that most human overrides are errors, we argue these errors can provide value: they generate new data from which algorithms can learn. To remain accurate, algorithms must be updated over time, but data generated solely from algorithmic decisions is biased, including only cases selected by the algorithm (e.g., individuals released on parole). Training on this algorithmically selected data can significantly reduce predictive …

System Dynamics With Insight Maker, Steven D'Alessandro, Fons Wijnhoven

Research outputs 2022 to 2026

This book offers a practical, model-driven pathway for reasoning about uncertain futures in business and public policy using system dynamics with Insight Maker. It begins by motivating why historical data alone often fail to predict social change, and it introduces the core language of system dynamics—stocks, flows, feedbacks, delays, and auxiliary variables—alongside the complementary use of agent-based modeling. Through business-relevant cases (e.g., park management trade-offs, epidemic–economy interactions, and industry competition), the book demonstrates how non-linear structure generates counter-intuitive dynamics, why scenario analysis is essential, and how to translate causal loop diagrams into stock-and-flow simulations. Readers are guided step-by-step to build, …

Check Your Data Before You Wreck Your Model: The Impact Of Careless Responding On Substance Use Data Quality, Abby L. Braitman, Anna M. Petrey, Jennifer L. Shipley, Rachel Ayala Guzman, Emily Renzoni, Alison Looby, Adrian J. Bravo

Psychology Faculty Publications

Background: The accuracy of survey responses is a concern in research data quality, especially in college student samples. However, examination of the impact of removing participants from analyses who respond inaccurately or carelessly is warranted given the potential for loss of information or sample diversity. This study aimed to understand if careless responding varies across a number of demographic indices, substance use behaviors, and the timing of survey completion.

Method: College students (N = 5809; 70.7% female; 75.7% White, non-Hispanic) enrolled in psychology classes from six universities completed an online survey assessing a variety of demographic and substance use-related information, …

M3t, Mariia Khan, Jumana Abu-Khalaf, David Suter, Bodo Rosenhahn, Yue Qiu, Yuren Cong

Research Datasets

For embodied agents, such as robots, tracking objects in their surroundings through visual observation is essential — a task, referred to as Visual Object Tracking (VOT). For instance, during a rearrangement task, a robot may need to track objects, as part of the scene change understanding process, to accurately restore them to their original states. Classic Multiple Object Tracking (MOT) datasets typically focus on tracking moving, single-class object instances in a video from a fixed viewpoint, limiting their applicability to embodied AI tasks. In embodied AI tasks, objects belong to multiple classes, are often static, and are observed from continuously …

Embscu, Mariia Khan, Jumana Abu-Khalaf, David Suter, Bodo Rosenhahn, Yue Qiu, Yuren Cong

Research Datasets

This dataset was created for the evaluation of the EmbSCU method, suitable for solving the Scene Change Understanding (SCU) task. The SCU task involves predicting a changed location, describing a change, and generating language instructions for the robotic agent to revert a change. Current datasets, related to scene change understanding, can be divided into scene change detection (SCD) and image difference captioning (IDC) datasets. Unlike existing approaches, EmbSCU facilitates simultaneous change detection, description and language-based rearrangement instruction generation for the agent to revert changes. Although the EmbSCU dataset is simulated, it is highly complex, incorporating 104 unique indoor Ai2Thor rooms. …

A Mathematical Model On The Temporal Dynamics Of Aviation Competitive Pricing, Tichaona Chikore,, Farai Nyabadza,

Journal of Aviation/Aerospace Education & Research

This study investigates the competitive dynamics of airport pricing using U.S. airport data to validate the findings. It employs linear and nonlinear ordinary differential equation models to analyze the influence of competitive interactions and internal factors on pricing decisions. The methodology involves parameter estimation via optimization techniques and quantile regression to capture heterogeneity across market segments. Mathematical analysis and simulation results show that if competitive coupling coefficients are low then there is weak competitive influence on pricing, with airports’ pricing largely driven by internal factors. Also, if the adjustment rates exhibit consistency across airports then internal dynamics are dominant in …

Generating Real-World Evidence In Early Alzheimer's Disease: Considerations For Applying The Target Trial Emulation Framework To Study The Safety Of Anti-Amyloid Therapies, Xiaojuan Li, Sonal Singh, Bahareh Rasouli, Jennifer Lyons, Noelle M. Cocoros, Richard Platt, Ivan Abi-Elias, Jerry H. Gurwitz

Department of Medicine Faculty Publications

Anti-amyloid beta monoclonal antibodies (anti-Aβ mAbs) have received approval from the US Food and Drug Administration for the treatment of patients with mild cognitive impairment or mild dementia due to Alzheimer's disease (collectively known as early AD) based on evidence from clinical trials. However, whether findings from these trials are generalizable to the real world is uncertain. We need reliable evidence on the real-world safety of these treatments to inform decision making for clinicians, patients, and caregivers. Using lecanemab as an exemplar, we outline the key considerations in designing and implementing an observational study on safety and utilization outcomes using …

T3-Ciders: Fostering A Community Of Practice In Ci-And Data Enabled Cybersecurity Research Through A Train-The-Trainer Program, Wirawan Purwanto, Mohan Yang, Peng Jiang, Masha Sosonkina

Electrical & Computer Engineering Faculty Publications

We present a training program named T³-CIDERS, the Train- The-Trainer approach to fostering cyberinfrastructure (CI)- and Data-Enabled Research in CyberSecurity. T³-CIDERS is a train-the-trainer program for advanced cyberinfrastructure (CI) skills that is designed to be synergistic with research, teaching, and learning activities in cybersecurity and cyber-related disciplines. The participants, termed 'future trainers' (FTs), are trained in effective instructional design and CI hands-on materials from DeapSECURE, developed in a previous CyberTraining program. T³-CIDERS aims to enhance cybersecurity research and education through broader adoption of advanced CI techniques such as artificial intelligence, big data, parallel programming, and platforms like high-performance computing (HPC) …

High-Fidelity Soh Prediction In Lithium-Ion Batteries Using Hybrid Ml Networks, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

Accurate and efficient prediction of lithium-ion battery state of health (SOH) is critical for ensuring reliability in electric vehicles, grid storage, and aerospace systems. Traditional SOH estimation methods often struggle with nonlinear degradation behaviors and lack sensitivity to subtle electrochemical signals, limiting their real-world deployment. To address these challenges, this study examines hybrid deep learning models that integrate differential capacity (dQ/dV) analysis to enhance predictive accuracy. Four hybrid architectures - hybrid CNN-LSTM multihead, CNN extractor for LSTM, DNN-LSTM, and DNN Bi-LSTM - were developed and evaluated using the NASA randomized battery usage dataset, offering a realistic benchmark under diverse operational …

Out Of Order And Causally Correct: Ready-Event Discovery Through Data-Dependence Analysis, Erik John Jensen, James Leathrum Jr., Christopher Lynch, Katherine Smith, Ross Gore

Electrical & Computer Engineering Faculty Publications

Data-dependence analysis can identify causally-unordered events in a pending event set. The execution of these events is independent from all other scheduled events, making them ready for execution. These events can be executed out of order or in parallel. This approach may find and utilize more parallelism than spatial-decomposition parallelization methods, which are limited by the number of subdomains and by synchronization methods. This work provides formal definitions that use data-dependence analysis to find causally-unordered events and uses these definitions to measure parallelism in several discrete-event simulation models. A variant of the event-graph formalism is proposed, which assists with identifying …

Enhancing Channel Data Savings And Information Transfer Efficiency In Ultrasound Imaging, Sai Konda, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Ultrasound is a popular imaging technique mainly due to its non-invasive nature. And so, it is being used in a variety of applications. Due to plane wave imaging technique in ultrasound, frame rate of ultrasound imaging has the potential for being very high. Due to which, many channel data frames are being generated within a few seconds. As a result, tasks such as storing data frames and transferring them from front end ultrasonic system to processing computers are presenting significant challenges. Our current research work minimized these issues. We proposed and implemented: (a) Data encoding technique - We combined every …