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Browse project ideas by Polygence mentors
Economic Research Project
Together, we will choose an area of economic research that the student is most interested in and produce economic research about it! For example let’s say a student is very curious fashion, we could conduct research together about the fashion industry and explore the underlying economic dimensions of the market.
Economics, Music, History
The Art of Linguistic Persuasion: Analyzing Rhetoric, Media Bias, and Public Discourse
In an era dominated by social media and news breaking by the minute, how a story is told often has a great deal of impact on public perception. This project is designed for students interested in Political Science, Journalism, Law, or Communications who want to investigate the power of language in the public sphere. Since the same set of facts can be framed in vastly different ways, we live in an era of “divergent realities” in media and government discourse. Drawing on my 33 years of experience in rhetorical analysis and my work as a professional editor, I will guide you through a systematic "deconstruction" of modern rhetoric in an area of your choice. We will look at how specific words, figurative language, narrative structures, and repetition of talking points can be used to persuade an audience to adopt or reject a point of view. In this project, we will examine: • The Rhetoric of Policy: Analyzing how different political factions use language to frame the same issue. • Narrative and Dehumanization: Exploring the historical and contemporary link between media descriptions and the real-world treatment of marginalized groups. • The Mechanics of Disinformation: Identifying the linguistic bias and misinformation. How do "loaded" words and logical fallacies shape our understanding of government and society? The Outcome: The student will produce a rigorous research paper or a comparative discourse analysis. This project is ideal for students who want to develop the analytical tools necessary for careers in law, public policy, or international relations, proving they can navigate complex societal data with objectivity and insight.
Creative Writing, Literature
The Art of Linguistic Persuasion: Analyzing Rhetoric, Media Bias, and Public Discourse
In an era dominated by social media and news breaking by the minute, how a story is told often has a great deal of impact on public perception. This project is designed for students interested in Political Science, Journalism, Law, or Communications who want to investigate the power of language in the public sphere. Since the same set of facts can be framed in vastly different ways, we live in an era of “divergent realities” in media and government discourse. Drawing on my 33 years of experience in rhetorical analysis and my work as a professional editor, I will guide you through a systematic "deconstruction" of modern rhetoric in an area of your choice. We will look at how specific words, figurative language, narrative structures, and repetition of talking points can be used to persuade an audience to adopt or reject a point of view. In this project, we will examine: • The Rhetoric of Policy: Analyzing how different political factions use language to frame the same issue. • Narrative and Dehumanization: Exploring the historical and contemporary link between media descriptions and the real-world treatment of marginalized groups. • The Mechanics of Disinformation: Identifying the linguistic bias and misinformation. How do "loaded" words and logical fallacies shape our understanding of government and society? The student will produce a rigorous research paper or a comparative discourse analysis. This project is ideal for students who want to develop the analytical tools necessary for careers in law, public policy, or international relations, proving they can navigate complex societal data with objectivity and insight.
Creative Writing, Literature
Reinforcement Learning-Based Autonomous Navigation for a Raspberry Pi Robot Building a Low-Cost, Real-World Autonomous Driving Platform
Autonomous navigation is one of the most exciting and demanding challenges in robotics — requiring a system to perceive its environment, make decisions in real time, and recover gracefully from mistakes. In this project, we will design and build a small-scale autonomous driving robot on a Raspberry Pi platform, using reinforcement learning to teach the robot to navigate real-world environments without explicit programming. Starting from scratch, we will assemble the hardware, set up the sensing pipeline using onboard cameras and sensors, and train an RL agent that learns through trial and error to follow lanes, avoid obstacles, and make navigation decisions on the fly. A key focus of this project is bridging the gap between simulation-based training and real-world deployment — one of the hardest open problems in autonomous systems research. Final deliverable: A fully functional Raspberry Pi-based autonomous robot trained using reinforcement learning and tested in a real physical environment, along with a research paper documenting the hardware setup, RL training methodology, sim-to-real transfer strategies, performance results, and lessons learned for scaling to more complex autonomous systems.
AI/ML
LLM-Assisted Early Detection of Cognitive Decline Through Speech and Language Analysis Listening for What the Brain is Trying to Tell Us
Changes in the way people speak and write are among the earliest detectable signs of cognitive decline — yet these subtle shifts are easy to miss in a standard clinical visit. In this project, we will build an AI pipeline that analyzes speech patterns, word choice, sentence complexity, and fluency from recorded patient conversations to flag early signs of cognitive decline associated with Alzheimer's and related dementias. We will explore how LLMs can be combined with audio processing tools to extract meaningful linguistic features, and evaluate the system's ability to distinguish between healthy aging and early-stage neurodegeneration across diverse patient populations. Final deliverable: A multimodal speech and language analysis tool evaluated on clinical or publicly available datasets, along with a research paper reporting predictive performance, key linguistic biomarkers identified, and implications for scalable, non-invasive cognitive screening.
AI/ML
Generative AI for Synthetic Patient Data in Parkinson's Disease Research Solving the Data Scarcity Problem in Rare Neurological Conditions
One of the biggest barriers to AI research in neurodegenerative diseases is the lack of large, diverse, and well-annotated patient datasets. In this project, we will investigate how generative AI models can be used to synthesize realistic patient data — including motor assessments, clinical notes, and imaging features — for Parkinson's disease research. We will design and evaluate a generative pipeline, rigorously test whether synthetic data can augment real datasets to improve downstream model performance, and critically examine the risks of training clinical AI systems on data that was itself AI-generated. Final deliverable: A generative data pipeline producing synthetic Parkinson's patient data, validated against real-world distributions, along with a research paper evaluating data quality, downstream model impact, and the ethical boundaries of synthetic data use in clinical AI.
AI/ML
A Multimodal AI System for Tracking ALS Disease Progression Turning Heterogeneous Patient Data into Actionable Clinical Insights
ALS progresses differently in every patient, making it extraordinarily difficult to predict how the disease will evolve and when to adjust care. In this project, we will build a multimodal AI system that integrates data from multiple sources — clinical assessments, speech recordings, wearable sensor data, and electronic health records — to model and predict individual ALS disease trajectories over time. We will work through the real challenges of longitudinal patient data, including irregular measurement intervals, missing data, and the need for models that clinicians can actually interpret and trust. Final deliverable: A multimodal disease progression model evaluated on longitudinal ALS patient data, along with a research paper reporting predictive accuracy, clinically meaningful insights, model interpretability, and recommendations for integration into palliative and supportive care planning.
AI/ML
Predicting Diabetes Risk from Health Indicators
In this project, you will explore machine learning and data analysis by building a model to predict diabetes risk using a public dataset such as the CDC Diabetes Health Indicators dataset. You can analyze features like age, BMI, physical activity, blood pressure, cholesterol, smoking history, and general health ratings. You will learn how to clean data, create visualizations, train a predictive model, evaluate performance, and discuss limitations around fairness, privacy, and real world medical use. The final outcome could be a Jupyter notebook, research paper, or presentation with charts and model results.
AI/ML, Statistics
Evaluating AI Answers for SAT or AP Questions
In this project, you will explore applied AI by testing how well AI tools answer a set of SAT style reading questions or AP practice questions. You will gather 30–50 public practice questions, prompt an AI system to answer them, and compare the responses against answer keys and explanations. Students will learn about prompt design, accuracy scoring, hallucinations, and how to evaluate AI systems beyond whether an answer “sounds right.” The final outcome could be an evaluation report, comparison analysis, or testing framework.
AI/ML, Statistics
Building a Campus Club Event Finder
In this project, you will explore software engineering and product design by creating a web app that helps students find events from different campus clubs in one place. You could gather sample event data from public club websites, Instagram posts, or a manually created spreadsheet with fields like event name, date, location, category, and RSVP link. You will learn how to define a real user problem, design a simple interface, organize data, and build features such as search, filters, and saved events. The final outcome could be a working prototype, or technical write up.
AI/ML, Statistics