Reid C
- Research Program Mentor
MPA at Columbia University
Expertise
Finance, Artificial Intelligence and Machine Learning, Data Science, Economics, International Relations and Global Affairs, Political Economy, Financial Technology (FinTech), Behavioral Economics, Quantitative Analysis, Computational Economics, International Political Economy, Financial Regulation, Public Policy, Research and Academic Writing, Econometrics
Bio
Hi! I'm Reid. I hold a master's degree in Finance and Economics from Columbia University, where my research focused on how markets, technology, and policy interact in the real world. I previously helped build a machine learning startup focused on responsible AI, where I've developed systems that tackle real-world problems at the intersection of technology, ethics, and business. For the past two years, I've mentored students interested in AI, finance, economics, data science, and public policy, helping them move from early ideas to well-structured research projects grounded in academic literature, data analysis, and rigorous methodology. My mentoring emphasizes the full research process, from refining a question to producing work that is polished and suitable for competitions, publications, or college applications. Alongside mentoring, I work as a Senior Consultant advising major financial institutions on technology and financial strategy, and I've previously worked with organizations such as JPMorgan Chase, Bloomberg, NBC, and CNN. Outside of work, I enjoy reading and writing about economics and global affairs, playing competitive pool, and learning Italian! I love helping students build both the research and technical skills needed to tackle ambitious projects in finance, economics, AI, machine learning and beyond. If you're curious about artificial intelligence, finance, or economics and excited to build research you're proud to share, I'd love to work together!Project ideas
What If You Missed the Best Days in the Stock Market? A Real Investing Simulation
In this project, we will test a famous investing question: what actually happens if you try to time the stock market instead of staying invested? Using real historical data from the S&P 500 between 2008 and today, we will build an Excel simulation comparing buy-and-hold investing to strategies that miss the market’s best (or worst) days. We’ll explore major events like the 2008 crash, COVID, and recent interest rate hikes. The student will use regression and simple machine learning tools to analyze outcomes and explain why timing is harder than it looks. I will help the student design realistic rules, avoid common mistakes, and write up results clearly. The final outcome will be a research paper plus an interactive Excel model.
Can AI Be Fair? Testing Bias in Loan Approval Algorithms
In this project, we will investigate whether machine learning models used to approve loans treat everyone fairly. Using real credit data, we will train our own loan approval model, then test it for bias across gender, race, and income using fairness metrics like disparate impact and equalized odds. The student will learn Python-based machine learning (scikit-learn), build classification models, and explore the tension between accuracy and fairness. I will guide students through responsible AI principles, interpreting model decisions, and proposing policy solutions. The final outcome is a research paper on AI ethics suitable for competitions or publication, plus working code demonstrating the analysis.
Do Stocks Overreact to Big News? Testing Market Psychology Around Fed Announcements
This project explores how investors react to major news, like Federal Reserve interest rate announcements. We will select specific announcement dates and analyze short-term stock market movements before and after the news. Using Excel-based event studies and simple machine learning pattern detection, we will test whether prices overreact and then correct. I guide students through choosing events, setting fair time windows, and interpreting results without overclaiming. The final product is a finance research paper or analyst-style report that shows how data can be used to study investor behavior.
Who Loses the Most When Prices Rise? Tracking Inflation’s Impact on Everyday Life
This project looks at inflation from a policymaker's point of view: how rising prices affect different people depending on what they spend money on. We will analyze inflation data for categories like food, rent, gas, and transportation, then simulate how purchasing power changes for different income levels using Excel models. With basic machine learning trend analysis, we will identify which groups are hit hardest during inflation spikes. I help students connect their findings to economic theory and real policy debates. The final outcome is a macroeconomics research paper that makes inflation personal, data-driven, and easy to explain.
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.
