# 2,893 Inspirational Research Project Ideas

Turn inspirations into your passion project.

This collection of project ideas, shared by Polygence mentors, is meant to help inspire student thinking about their own project. Students are in the driver seat of their research and are free to use any or none of the ideas shared by their mentors.

- AI/ML
- Animation
- Arts
- Biology
- Biotech
- Business
- Cancer
- Chemistry
- Cognitive
- Comp Sci
- Comp Sci - Game Design
- Creative Writing
- Dance
- Economics
- Engineering
- Entomology
- Environmental Science
- Ethics
- Fashion
- Finance
- Fluid Dynamics
- Healthcare
- History
- Illustration
- Languages
- Linguistics
- Literature and Languages
- Math
- Medicine
- Music
- Neuroscience
- Nutrition
- Organizational Leadership
- Philanthropy
- Photography
- Physics
- Psychiatry
- Psychology
- Public Health
- Quantitative
- Social
- Social Science
- Sports Analytics
- Statistics
- Surgery

## Create an NBA trade simulator

Scrape NBA statistics using python or google sheets and basketballreference.com, identify a metric to compare different players on different teams, and run a statistical hypothesis test to determine if the teams involved would accept the potential trade! Then create an interface to run your simulator and view the result

Math, Engineering, Economics

## Modeling the spread of an infectious disease (e.g., COVID-19)

In this project, we will learn about mathematical modeling and its diverse applications in epidemiology. Pertinent questions may be: Which infectious disease should we model (i.e., the “big picture” context)? What is the nature of the data we are provided with (i.e., exploratory data analyses)? What are the various types of modeling frameworks available to us (i.e., model paradigm)? On what levels can models be compared (i.e., model comparison)? Which steps should be taken to ensure the accuracy of our model (i.e., model checking)? The chosen paths of exploration will depend on the student’s interests. Final Notes: If you have a particular idea for a project in mind, I am happy to guide you throughout your journey. Intellectual curiosity is a vital component of the research process!

Statistics, Comp Sci, Math

## Who moves where? Exploring US Census migration data

Workers vary along many different dimensions: age, sex, income, education, household size, and so on. So, too, do places: New York City has very different employment opportunities and cost of living than those in Detriot or Phoenix or Billings. In this project you'll flex your data analysis and visualization skills to document facts about migration patterns in the US: Where are young college graduates most likely to move? What about married couples with kids? Retirees? Houston's population has been growing rapidly --- what sorts of workers have been moving there? What sorts of workers are leaving Detroit? There's lots of interesting work in economics exploring the determinants and consequences of life-cycle migration decisions, but the first step is to understand what's already there in the data. You can help us do that!

Statistics, Math, Economics

## Harmonic Series and Their Close Friends

From calculus we know that the harmonic series sum_{i=1}^{oo} 1/i diverges. What would happen with the series convergence if you cross out all the terms of the series that contain digit "9." Say, 1/19, 1/29 etc. This is an old olympiad problem. We will try exploring a much harder question for the so called p-series sum_{i=1}^{oo}1/i^p where p is a positive real number.

Math, Economics, Quantitative

## Simulating the Evolution of the Smallest Brain

Design a semi-random network growth mechanism that will produce networks with structure that is similar to actual neuronal networks. (We will use the neuronal network of the organism C. Elegans as a model.) You will learn the basic mathematics behind network science and write computer programs to simulate network growth. Prerequisites: 1. Programming experience

Math, AI/ML

## Determining most impressive World Records between Track and Field Disciplines

Multiple world records exist in the sport of Track and Field as there exists disciplines such as javelin, shotput, high jump, 100 meter sprints, and 5000 meter races, among others. As the disciplines do not share commonalities, it is hard to determine if the current 400 meter record is more impressive than the shotput record, for example. An additional complication is that only the best few thousand results exist per discipline, so traditional statistical measures around averages and distributions cannot be run. The goal of this project is to determine a good method of approach to comparing statistical records.

Statistics, Math, Sports Analytics, AI/ML

## The Complexity of Problems and Mathematical Limits of Computers

There are an endless number of fascinating and practical problems that we hope to solve with the help of computers. While some of these problems have efficient algorithms, such an algorithm is far from guaranteed to exist. During this project, we will focus on a problem of interest to you (i.e. the student). Rather then simply hoping this problem can be solved efficiently and diving straight into programming, we will study the mathematical structure the problem and prove limitations on our ability to write algorithms to solve it. Not only does this give us deep insight into the structure of problem, but it is of vast practical interest, as such results tell us what is and is not possible. One major benefit of this is that if anyone later tries to use computers to solve this problem, they can avoid seeking overly ambitious algorithms that may in fact be entirely non-existent, saving vast time and effort due to the insight granted them by your theoretical analysis of the problem. For a concrete example of such a project, I did a project along these lines myself as a high school student. The project was awarded 1st Prize at the Massachusetts State Science and Engineering Fair and the final paper can be found here: https://arxiv.org/abs/1110.1052

Math, Comp Sci

## How virtual is virtual reality?

Virtual reality uses an amazingly simple principle - matching the movements of your head or hands with movements on a screen - to trick your brain and do an impressive job convincing you that a digital scene is real. So why is your brain so easily tricked by virtual reality? And how can we use this to better understand how the brain works? In this project, you will explore the science behind vision and cognition to better understand the scientific basis of technologies such as virtual reality and explore how scientists are using virtual reality to better understand how the brain works.

Math

## Image analysis and generating 3D printing trajectories

Many software packages exist to convert clinical data acquired via MRI or CT scan to 3D models and subsequently 3D printing instructions. While these software packages can be useful, they often are accompanied with bugs that are difficult to troubleshoot. Developing the skill of generating printed structure trajectories could be useful for future 3D printing work.

Math, Engineering

## Poverty trends

How did world poverty trends change in the last decade? Which regions improved and which did not? What are the possible explanations?

Finance, Statistics, Math, Economics

## Discovery of Potential Disease treatments

Investigate disease treatments and review research literature to discover a potential disease treatment. Propose the disease treatment and how it could potentially be used to treat patients.

Math, Chemistry

## Decoding Motor Intent Signals using Deep Learning

In this project, we will use deep learning methods to decode the motor intent from neural signals obtained from the cortex. We will use feature extraction techniques to reduce the input data dimensionality, which later helps substantially lower the motor decoder's complexity. We will apply various approaches to decode single motor movement signals and combination motor movement signals.

Neuroscience, Math, Comp Sci

## What a Waste!

It's inevitable and inescapable: humans generate waste. But how we define and manage this waste is what will have a lasting impact on our own health, and the environment. The goal of this project is for the student to choose an activity they do on a daily basis that generates waste (either wastewater or food), and explore where that waste goes and what possible resources could be recovered during the engineering waste management process. For example, examine how much and what types of waste are produced from a week's worth of groceries. There are plastic food packages, aluminum cans, glass jars, and food waste from preparing vegetables for cooking or banana peels. How does your community process the waste? What properties/characteristics of the waste still have value if transformed by a thermal, biological, chemical, or mechanical process? What processes/technologies can be utilized to recovery a valuable product? How can the linear system become circular? Are these processes environmentally and economical sustainable? What are the limitations and drawbacks of this technology? Throughout this project, the student will gain an understanding of resource recovery topics that they contribute to daily and an introduction to the technologies currently used (and not yet used) to manage their waste.

Math, Chemistry

## Causality vs correlation - A basic tutorial on causal inference for the public

Causality is the desire of many scientific disciplines. Correlations are easier to analyze typically, but are not equivalent to causation. This is exemplified in the following phrase: "correlation does not imply causation". Causality is typically measured in medicine, policy and science via a randomized control trial, where something is randomized and then its effect on something else is measured. For example, randomizing who gets a COVID-19 vaccine and comparing people with vaccines vs without will determine the causal effect of the vaccine on preventing COVID. Modern causal inference allows one to encode causality as a graph, represented by nodes and edges in a pictorial representation. This enables scientists, policy makers and leaders to not only encode their causal assumptions for a specific problem, but also provide general algorithms for determine if someone can determine a causal effect from purely observations. A break down of graphical notions of causality in laymen terms would be useful for education at the high school and undergraduate level. Successful completion of this project could go on to inspire and educate people on causal inference. A successful project would take the basics of causal inference and break it down into a paper and/or series of short posts that introduce the topics.

Neuroscience, Statistics, Math

## Are Medicare and Medicaid optimal for social welfare?

While many economists agree that some people in the society need the government's help to get better medical services, not all agree on the best method to do so. We can use data on Medicare and Medicaid spending and some measured outcomes to evaluate these policies.

Math, Economics, Comp Sci

## Making Machines Make Art - Procedurally Generated Content

Computers and people can cooperate to make infinite varieties of creative content. In this project, you will learn how to create infinite images, music, video game levels, 3d objects, or text using techniques like neural style transfer, genetic algorithms, rejection sampling, Perlin noise, or Voronoi tessellation. You will create a functioning content generator that can be showcased on a website or at a research conference.

Comp Sci, AI/ML, Math, Statistics, Comp Sci - Game Design

## Is Nuclear Energy worth pursuing?

The world faces a climate crisis, one in which immediate and drastic action is needed. Promising technologies such as nuclear power have faced public opposition and regulatory hurdles for years. Together, we can explore whether it is technically viable (is it better than other energy generation techniques) and practically acceptable (is it safe and what are the long-term consequences).

Math, Physics, Engineering, Chemistry

## A basic mobile robot

Using an Arduino based microcontroller we can design a simple robot to accomplish a simple task. Depending on the student's mathematical maturity and coding expertise we can develop a mobile robot that acts semi autonomously to follow a wall, line follow, or detect objects in its vicinity. Prerequisites or Learn quickly: Coding , Electronic Circuits Nice to Knows: PID control and Cad

Math, Physics

## Is Number System a dynamic concept?

Number system is such a fascinating concept in Math if you explore its origins and evolution. When a Kindergartner perceives it as a consecutive series of positive numbers, the concept evolves and brings out more and more hidden characteristics such as negative and positive numbers, rational and irrational numbers, complex numbers, thus adding more complexity to something that was seen as simple. As we go higher up, we realize that between any two numbers, there is an infinite number of numbers since irrational numbers continue like a never ending story. A research to understand the dynamics of number system would be a fun journey.

Math

## On the measurement of racial wealth inequality

Atkinson's 1970 work "On the measurement of inequality" is a groundbreaking theoretical exercise in that it ties together a number of seemingly disconnected concepts (measures of inequality, models of choice under uncertainty, and "classes of social welfare functions") to provide a framework for producing measures of inequality in a given distribution of resources for a group of people. However, that framework is carried out for income inequality. We know from the real world that wealth inequality is a much more complex phenomenon, especially when considering racial and historical facts about the U.S. Can a similar analysis be carried out for ranking wealth distributions and providing measures of wealth inequality, as seen in Atkinson 1970? If so, what should motivate the modeling choices made here that will not appear in the original work?

Math, Economics, Social Science