John L
- Research Program Mentor
MS at University of Michigan - Ann Arbor
Expertise
Machine Learning, Financial Statistics, Corporate Finance, Econometrics, Business Analytics, Statistics, Psychometrics
Bio
I am a Statistics Lecturer at the University of Chicago. There, I designed a course on Data Science in Quantitative Finance and Risk Management and taught it to undergraduate students of all majors and backgrounds. What I enjoy most about teaching is that I am able to engage with students on both individual and team levels. My vision is to create a learning community for students to embrace uncertainty, obstruction, and frustration throughout their learning journey. I am also a Research Statistician at the College Board. My day-to-day tasks range from designing psychometric approaches to improving digital learning and assessments through a programmatic lens. I enjoy this field of work because I am able to communicate the technical facet of my work to field laymen. My goal is to novelize learning tools to help K-12 students make critical decisions in life.Project ideas
Data Science in Risk Management
Market drawbacks are inevitable, but do you know that their patterns are identifiable with statistics? This project will utilize a variety of probability measures, including extreme-value distributions and volatility models to optimize the allocation of stocks in your stock portfolio. The measures will be implemented in Python and R. Through this project, you will be able to: 1) Master the concepts of probability distributions 2) Obtain hands-on exposure to tasks of a Quant 3) Maximize your portfolio returns with minimal risks
Deploying Trading Algorithms with Machine Learning (ML)
This project focuses on employing investment decisions based on model output, particularly for equities and cryptocurrencies. Techniques involve using regression and classification designs to predict the market's price composition. Fundamental and technical data are used. The models will be implemented in Python. Through this project, you will be able to: 1) Develop the mathematical foundations for the ML models and implement them 2) Optimization algorithms for training the models on actual data. 3) Analyze the performance of the trading algorithms