About Me
James received his B.S. in mathematics at National Tsing Hua University (NTHU). During school, he was introduced to financial engineering and had done research at Prof. Han's quant lab.
After graduation, he joined structured products desk at Development Bank of Singapore (DBS) until 2019.
James also completed his M.S. degree in mathematics at Rutgers University in 2021 with concentrations in mathematical finance and data science.
He now works as a quantitative analyst at Duff and Phelps, a financial advisory firm that specializes in complex financial instruments valuations.
Technical Skills
Programming Languages: Python, TypeScript, SQL, R, C++
Cloud & DevOps: AWS (ECS, ECR, S3, Lambda), Docker, Git / GitHub
Frameworks & Tools: Django, FastAPI, Flask, Angular, Postman
Education
M.S., Mathematical Finance, Rutgers University, New Brunswick, NJ, USA, 2021
B.S., Mathematics, National Tsing Hua University, Hsinchu, Taiwan, 2018
Work Experiences
Quantitative Analyst
Duff and Phelps (currently Kroll) | New York, NY, USA
Aug 2021 - Present
- Developed and implemented pricing models for complex financial instruments valuations
- Performed fairness opinion and accounting support for both public and private companies
Model Risk Analyst
Valley National Bank | Morristown, NJ, USA
Jan 2021 - Apr 2021
- Constructed survival curve for bank's non-maturity deposits using survival analysis
- Validated treasury model assumption and interest rate risk analysis
System Analyst
Development Bank of Singapore (DBS) | Taipei, Taiwan
Jul 2018 - Aug 2019
- Maintained quote automation and sales reports for structured products (DCI & SN) desk
- Validated migration data using MySQL and VBA for firm-wise wealth management system upgrade
Academics & Internships
Teaching Assistant
Rutgers University | New Brunswick, NJ, USA
Summer 2019 and Fall 2020
- MATH485 Introduction to Math Finance
- MATH373 Numerical Analysis
Research Assistant Internship
Math Institute, Academia Sinica | Taipei, Taiwan
Jun 2017 - Aug 2017
- Conducted research and implemented saddle point approximation on Value at Risk (VaR) in risk management. This project was supervised by Prof. Han.
Past Projects
Finteck - Option Pricing Website
October 2024
Finteck is built using Angular (Typescript), CI/CD using Docker / Github actions and hosted on Render.com.
This is a web-based pricing tool for European and American type options, convertible notes, and preferred shares.
[Website]
Automated IPO Data Analytics Pipeline
October 2024
Implemented GitHub Actions workflows to run daily scheduled tasks that consume Finnhub API for IPO data collection.
This project is built using Python and logging package.
[GitHub]
Merton's Jump Diffusion Model
Jan 2023
Merton's Jump Model introduces compound poisson process into regular SDE that captures jumps due to corporate or extreme events in option pricing.
This project analyzes the dynamics of the jump model, calibrated parameters of jump frequency and magnitude from market option prices and used it to re-price further extended options with different strike and maturity.
[Medium]
Value at Risk (VaR) in risk management
Sep 2019 - Dec 2019
This Python notebook demonstrated historical, simulation and normal distribution methods of estimating portfolio VaR and CVaR. The notebook also included portfolio weight optimization and derived its efficient frontier.
[Notebook]
Cubic spline Curve Construction
Oct 2020 - Dec 2020
Constructed OIS/LIBOR curve using B-spline and used it to price interest rate swap.
[GitHub]
Implied Volatility in C++
Nov 2019 - Jan 2020
OOP design of put and call option classes that calculate Black-Scholes implied volatility using bisection and Newton's method that results in log(N) efficiency.
[GitHub]
Credit Scoring using Logistic Regression
Jan 2021 - Apr 2021
Utilized IV and WOE for feature selection and logistic regression to predict whether customers would be 2 years delinquent (default) and their credit score. The model achieved 94% accuracy and 82% AUC.
[Kaggle]
AI classification on hand-written digits (CS520 Intro to AI project)
Mar 2021 - Apr 2021
A group project that implemented Naive Bayes, Perceptron, Softmax Regression and Neural Network in Python for image classification on hand-written digits. We also compared their performance in terms of training time and testing error.
[PDF]
Last updated August 10th 2025 - Click to switch to dark mode
