This is my virtual, *expanded* resume
Note: * indicates ON resume
Additional Note: Some parts of this resume contain external links for more information
GPA: 3.8 (Major: 4.0)
notable courses (in relative order of difficulty/major): CMSC 14200 (Introduction to Computer Science II), CMSC 14300 (Systems Programming I), CSMC 14400 (Systems Programming II), CMSC 23710 (Scientific Visualization), CMSC 22200 (Computer Architecture), CMSC 25300 (Mathematical Foundations of Machine Learning)
MATH 19620 (Linear Algebra), MATH 15910 (Introduction to Proofs in Analysis), STAT 24300 (Numerical Linear Algebra), MATH 20300 (Analysis in Rn I)
GPA: 4.49
notable courses: Machine Learning, Artificial Intelligence, Linear Algebra, Multivariable Calculus, Computer Systems Research Lab
- 80m+ AUM, funded by Mark Cuban
- Researched and modeled carry strategy utilizing ForEx interest rate differences
- Presented and pitched strategy to board of investors, citing thorough backtesting, research, and problem-solving
- Project displayed below (notable statistics: Sharpe Ratio of 1.245, 10.68% yield YoY)
- Researched trends and provided detailed suggestions for target audience for Hollister clothing brand under Abercrombie & Fitch
- Learned about marketing and data science practices behind the rebranding of Abercrobmie (NYSE: ANF +307.70% in a year)
- Completed ML workflow from data acquisition to model creation, training, and validation, to model deployment
- Created Neural Network algorithm (LSTM) with novel approach to classify tennis strokes
- Achieved 99% prediction accuracy and produced production-ready trained models
- Submitted research paper for publication: Categorization of Tennis Swing Using a Recurrent Neural Network in Human Activity Recognition
- Used comparable companies, discounted cash flows, and precedent transaction models to value companies in Natural Resource sector
- Helped actively manage a $150,000+ all-equity portfolio by participating in weekly group meetings that involve a weekly stock pitch
- Accepted into and passed the competitive New Member Education program which maintains an acceptance rate of less than 10% on campus
- Learned discounted cash flow and statement modeling for large capitalization companies in Natural Resources and Technology
- Admitted to selective program tailored towards students interested in quantitative finance
- Learning from the three year curriculum including technical training and on-campus events such as the UChicago Trading Competition
- Participating in a selective education program involving quantitative analysis strategies, implementation, and research
- Created a unique pairs trading algorithm combining signals and testing strategies in a group of analysts using genetic optimization
- Performed market research for a product from a company based in Chicago looking to disrupt a $300b+ consumer industry
- Created multivariate Python model (X-Means, GRU) to calculate best pricing practices at multiple levels and maximize profits
- Critically analyzed company’s practices and presented plausible solutions and pricing techniques to company executives
- Advised investment committee to actively manage a $30,000+ portfolio for group at the UChicago Chapter
- Programmed and researched options trading algorithm with group of quantitative analysts, outperforming market in testing split
Poker - favorite player: Phil Ivey
Exploring the World - favorite places: Lake Como, Italy; Valencia, Spain; Lisbon, Portugal; London, England
Coffee - favorite drink: *iced* latte
Music - favorite genre: Country (Sand In My Boots, Morgan Wallen)
Basketball, Football - favorite team: Golden State Warriors, San Francisco 49ers
Chess - chess.com peak rapid rating: 1800 (openings: London System + Sicilian)
Personal Investing - current stock picks: Abercrombie & Fitch (NYSE: ANF), J.P. Morgan Exchange-Traded Fund Trust (NYSE: JEPQ)
Artificial Intelligence (AGI) / Machine Learning - Algorithms coded: Regression, RNN, CNN, LSTM, K-means, X-means, SVM, Random Forest
Running - Goal: Marathon at 8:00 pace
Mostly work across computer science and finance
- 1.245 Sharpe, 83.3% Win Rate, 15,132 trades executed in a 10-year time period, 175.762% PnL (10.68% YoY) matching S&P 500 returns from 2010-2019, little to no correlation with S&P 500 and USD is not used as a long or short currency
- Created a carry trade algorithm across 4 ForEx currencies (Swiss Franc, Japanese Yen, Australian Dollar, Canadian Dollar) using 16 separate bond interest rates from federal reserve data (1962 - 2024) and 10 year out-of-sample backtesting verification
- Programmed 5 separate algorithms to optimize performance: Linear Regression, Histogram-Based Gradient Boosting Regression, Convoluted Neural Network (4 hidden layers), Long Short-Term Memory Network, Weighted Decision Tree + Random Forest
- Utilized various data manipulation techniques like sliding windows, lookback periods, absolute and relative data margin, and imputers/filters for NaN data points as well as continuous training even during out-of-sample testing
View Source Code | | Download Presentation- Utilized an LSTM to classify the following tennis strokes: forehand, backhand, backhand slice
- Novel approach implementing sliding windows in HAR, detailing model performance in attached research paper (unpublished)
- Proved effectiveness of LSTM, outperforming RNN, CNN, GRU; Achieved 99%+ prediction accuracy when categorizing
- Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Convoluted Neural Network (CNN), Gated Recurrent Unit (GRU)
View Source Code | | Download Research PaperNot in publication
- Implemented a pairs trading algorithm using PNC (long) and BAC (short), backtesting the strategy from 2013-2023, including and excluding 2020
- Cross-correlated a set of banks as assets to determine a possibly profitable pair with a maximum bound correlation coefficient
- Chosen banks for testing included: Bank of America (NYSE: BAC), JP Morgan Chase (NYSE: JPM), US Bancorp (NYSE:USB), Citigroup (NYSE: C), Wells Fargo (NYSE: WFC), PNC (NYSE: PNC), Charles Schwab (NYSE:SCHW), Morgan Stanley (NYSE: MS), Goldman Sachs (NYSE: GS), Truist (NYSE: TFC), ICICI Bank (NYSE: IBN)
- Tested various executions and optimized trading timing using a Genetic Algorithm (PyGAD), an algorithm typically used to identify certain genes responsible for behaviors or attributes
- Achieved Sharpe Ratio of over 3, indicating highly successful returns in the 2013-2023 testing window; however, this Sharpe Ratio doesn't include transaction costs, projecting higher-than-actual returns
View Source Code | | Download Research PaperNote: This project was completed during the final round during the interview process for the BI, Data & Analytics Track under the H&M Group Trainee Program
- Implemented a backtesting algorithm for two machine learning models aiming to optimize the clothing brand's resources to maximize profit and reduce waste
- Given a large dataset containing the real sales info, and then comparing it to the model's predictive outputs, the algorithm created a new metric to identify which algorithm continued to predict an efficient, profit-mazimizing strategy comparatively
- Numerous metrics were tested and visualized before concluding which machine learning model was more accurate and could provide higher sales for the clothing brand while minimizing waste
View Source Code- Created a high-level presentation covering the basics of Machine Learning from linear vs non-linear models to supervised versus unsupervised models
- Presentation time was around 30 minutes and the goal beyond the basic algorithm was to describe how these models could help provide a stronger method for identifying risk in markets, later shown in the case studies
- Models and methods presented included: Principal Components Analysis, Ridge regression, Partial Least Squares, LASSO, LARS, Elastic Nets, Neural Networks, Deep Learning, Support Vector Machines, Decision Trees, K-,X-means
Download PresentationNote: if access to model is requested, please contact me via the email address or phone number provided in the Contact section
- Analyzed and modeled the company International Business Machines (NYSE: IBM); rated a BUY in December 2023 (+17% return)
- Learned about the company's business model, products, segments, leadership, and trends; Compared products to market size and competitors to understand if IBM had a reasonable competitive advantage
- Performed a full financial analysis using the company's 10-K via BamSEC, proposing a roughly 5% upside in its base case
Download Presentation- Created a survey to gather information from over 100 participants; performed data analysis on the data combined with market information
- Suggested three separate implementations to help increase sales for the Hollister subsidiary of Abercrombie & Fitch
Download Presentation