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Browse project ideas by Polygence mentors
Testing a New Dark Energy Parametrization with Mock Cosmological Data
Recent large-scale structure data have sparked renewed interest in the possibility that dark energy may evolve over cosmic time. In this project, the student will test the robustness of a newly proposed dark energy energy-density parametrization using mock datasets. The project will involve generating synthetic cosmological expansion data under controlled assumptions, performing Bayesian statistical inference to recover model parameters, and comparing the performance of the new parametrization against commonly used alternatives. The student will investigate questions such as: Does the new model introduce biases? Does it improve flexibility without overfitting? Under what conditions can evolving dark energy be reliably detected? This project serves as a hands-on introduction to statistical inference, model comparison, and the careful testing of theoretical proposals before applying them to real observational data.
Statistics, Physics
Extending CMBverse: Visualizing Secondary Anisotropies of the CMB
The cosmic microwave background (CMB) not only encodes information about the early universe, but also carries subtle imprints from the late-time universe as photons travel toward us. In this project, the student will develop clear visualizations of one or more secondary anisotropy effects — gravitational lensing, the thermal Sunyaev–Zel’dovich (tSZ) effect, and the kinetic Sunyaev–Zel’dovich (kSZ) effect — and integrate them into the CMBverse website. The goal will be to produce high-quality plots that isolate and explain how each effect modifies the primary CMB signal, accompanied by concise, accessible explanations describing what physical processes generate these distortions and what they teach us about dark matter, dark energy, and structure formation. By the end of the project, the student will have contributed new educational research tools to a public-facing platform, while gaining experience in numerical modeling, scientific visualization, and translating technical physics into clear explanations. This project is well-suited for students interested in connecting theory, computation, and science communication.
Statistics, Physics
Building a Stock Price Prediction Model: Can AI Beat the Market?
This project explores whether machine learning can actually predict stock prices better than traditional methods. We will collect historical data on a chosen stock or index, build prediction models using techniques like linear regression, random forests, and LSTMs (recurrent neural networks), then rigorously test their performance. The student will learn Python programming, work with financial APIs, and discover why this problem is harder than it looks. I help students avoid common pitfalls like data leakage and overfitting, and explain results honestly. The final product is a finance + AI research paper with code, suitable for showcasing technical skills to competitive programs.
AI/ML, Economics, Finance
College Application Help
Can design a college application proposal from brainstorming all the way to personal statements. Includes scholarship or fellowship applications! Previous experience in Caltech, MIT, Brooke Owen Fellowship, and various scholarships.
Economics, Engineering
How smart are AI Applications?
Artificial Intelligence (AI) aims to mimic human intelligence. When successful (especially generative AI) it produces applications that can exhibits behavior like what smart people do. Applications such as ChatGPT can engage in a a serious conversation with a human being, come up with a piece of code in Python, Java, C++ or any other program ming language to solve a complex problem, can write a smart power point presentation on a topic can produce videos according to users request and specification, etc. However, though AI application may behave like smart human beings in some areas, the way AI works is fundamentally different from the way humans acquire intelligence. For example, while AI depends crucially on very large amounts of data and on previous encounters of clues in its data (machine learning) to make decisions, human intelligence can make decisions on unseen and novel problems very easily. This research is multifaceted and different students can different aspects of the general problem under investigation.
AI/ML
Artificial Intelligence and Hunan Intelligence
This is an investigation into the relationship between human intelligence (HI) and current applications of Artificial Intelligence (AI). We examine the basic assumption that AI is an attempt to mimic natural HI. We want to determine what areas both AI and HI agree and what areas they disagree. We would like also to consider how far AI reflects our understanding of what Human Intelligence is. In areas where they disagree, what impact does that have on current AI applications.
AI/ML
Miscellaneous Projects
1. I can assist in developing games from concept to prototype. 2. I can also assist in structuring and optimizing blogs to attract readers, enhancing the user experience.
Biotech, Music, Business
Literature Review for Beginners - Podcast or Writeup
In this project we will conduct a literature review on a topic of your choosing. The core idea will be to identify how different strands of the literature connect to each other, and how our understanding of the topic of your choosing has evolved over time. The final deliverable will be either a podcast-style discussion of your literature review or a short writeup that summarizes your findings.
AI/ML, Economics
Machine Learning Classification for Beginners
In this project, we will construct different machine learning models to perform a classification task of your choosing. We will both discuss and find appropriate data, and I will guide you through the whole lifetime of the process: data cleaning, preprocessing, model implementation and comparison. The final deliverable will be a Github repository with your functional code that you can add to your portfolio.
AI/ML, Economics
Climate Anxiety in Everyday Life: : An Interview Study of Climate Anxiety, Meaning, and Coping
This research project examines how people experience, interpret, and respond to climate change in their everyday lives, with particular attention to climate anxiety and related feelings such as grief, dread, anger, numbness, and hope. Using in-depth interviews, the study aims to understand not only what climate distress feels like for individuals, but also how it becomes shaped by people’s social locations, community ties, and broader cultural narratives about responsibility, uncertainty, and the future. The project asks: How do people make meaning of climate threat? What kinds of coping strategies do they describe (e.g., emotional regulation, avoidance, activism, spiritual practices, mutual aid)? And how do they decide what counts as a “reasonable” response to living in a time of ongoing crisis? To carry out this project, the student will strengthen their skills in qualitative research design and interviewing, including developing an interview guide, recruiting participants, building rapport, and conducting ethical human-subjects research. The student will also become acquainted with theories of emotion, environmental inequality, risk, and care, and learn practical methods for analyzing interview data (e.g., transcription, coding, and thematic analysis).
History, Literature
