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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

Project ideas are 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.

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

Coding skills

Python, R, SQL, C++, Spark, Hadoop, Power BI

Languages I know

Cantonese, Mandarin

Teaching experience

I am a Statistics Lecturer at the University of Chicago. There, I taught a self-designed course, "Data Science in Quantitative Finance and Risk Management" to 30 pre-college students, covering topics ranging from machine learning to risk calculations using Python. Before this, I have spent 2 years mentoring and instructing university-level courses at the University of Michigan. I lead the Statistics and Data Analytics instructions team to teach over 150 undergraduate students on theoretical and applied statistical topics including univariate inference and analysis of variance using R programming language. I greatly enjoy mentoring student projects and introducing extra-curricular statistical applications in an open-floor environment.

Credentials

Work experience

The University of Chicago (2022 - Current)
Lecturer
The College Board (2023 - Current)
Research Statistician
University of Michigan (2020 - 2021)
Machine Learning Research Assistant
College Board (2023 - Current)
Research Statistician

Education

University of Connecticut
BA Bachelor of Arts (2019)
Actuarial Science and Economics
University of Michigan - Ann Arbor
MS Master of Science (2021)
Data Science

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