Dongliang (Dorian) Lu

*** **** ***** ****** *** *D·NEW YORK NY 10025

917-***-**** ac8djs@r.postjobfree.com

EDUCATION

Columbia University, New York, NY Expected December 2018 Master of Science, Financial Engineering (GPA: 4.16/4.0)

• Coursework: Stochastic Models, Optimization, Machine Learning, Monte Carlo Simulation, Data Analysis & Time Series, Model Based Trading, FX Markets & Derivatives, Credit Risk & Derivatives, Deep Learning, Stochastic Control for FE Shanghai Jiao Tong University, Shanghai, China September 2013 - June 2017 Bachelor of Science, Physics and Astronomy (GPA: 3.8/4.0; Rank: 1/33)

• Coursework: Probability and Statistics, Partial Differential Equations, Numerical Methods, Programming in Python

• Awards: Shanghai Excellent Student Scholarship (40/20000), 1st Prize for OPT Academic Innovation Scholarship (1/500), 1st Prize in Shanghai for National Mathematical Contest in Modeling PROFESSIOAL EXPERIENCE

Columbia Business School, New York, NY Research Assistant May 2018 – Present

• Built a unique database by scraping and cleaning lobbying-related data from 9 Government Agencies’ websites using Python; achieved a 99% accuracy matching organization names; extracted citations in final rules using regular expressions

• Adapted Smith-Waterman Algorithm by combining bags of words models to find similar paragraphs in 16 final rules and 5421 comments, which enhanced calculation speed by 10,000 times compared with the original algorithm

• Applied NLP techniques such as penalty regression models, generative models on count, set, TF-IDF and PCA transformed features on final rules and gained an accuracy of 99.5% in predicting lobbying success

• Quantified relationship between lobbying influence and comment times, meeting times, and uniqueness of comments using regression methods; justified the assumption that large banks were favored by financial regulators Guotai Junan Securities, Shanghai, China Carbon Finance Trading Assistant November 2016 - April 2017

• Predicted national productions for 17 industries such as cement, flat glass etc. using GARCH(1,1) Model; modified energy-consuming and carbon-emission factors for standard coal from over 60 papers; predicted carbon emission quota’s supply and demand till 2020, which yielded a 0.2% prediction error for the year 2016

• Forecasted the next five years’ monthly CCER (Chinese Carbon Emission Reduction commodity) by modeling a Poisson Process based on its production, application and government approval

• Established an automatic prediction system in EXCEL VBA and reduced operation to one click Fan Investment, Shanghai, China Quantitative Analyst July 2016 - November 2016

• Modified event-driven strategy by adding the signal of trading volume and direction, which boosted trading accuracy by 30%

• Back-tested over 30 financial and market indicators (ROE, EBITDA, etc.) of the Chinese A share market in SAS using the last 16 years’ data and achieved a 20.2% yearly return long-short strategy with 24.8% max-drawdown PROJECTS

House Price Prediction, New York, NY August 2018 – September 2018

• Performed feature engineering and tuned parameters of penalty regression models using 5-fold cross validation

• Stacked three models’ results as new features using XGBoost and achieved a RMSLE score of 0.072 High Frequency Intraday Pairs Trading Strategy for Oil Companies, New York, NY February 2018 – May 2018

• Adapted OU process to model the spread of oil companies’ stock pairs and calibrated parameters using scipy.optimize

• Selected optimal stock pairs and yielded a pairs trading strategy with daily return of 0.19% and Sharpe Ratio of 2.7 CRR Model and American Options, New York, NY October 2017 – December 2017

• Derived price of perpetual American put option and optimal exercise time by solving Hamilton-Jacobi-Bellman equation

• Experimented different time-space discretization; identified the instability of Explicit Euler’s Scheme Pricing in Python

• Applied Newton algorithm to solve nonlinear equations; used Implicit Euler’s Scheme to get the American put price Numerical Simulation of Financial Market Based on EIGEN Model, Shanghai, China July 2016 - May 2017

• Established correspondences between bio-evolution and financial market; initialized assumptions on agents’ trading strategies

• Simulated a 500-Agent market under Minority Game Theory using Monte Carlo simulation in MATLAB; investigated market behavior under different risk-free interest rates, mutation rates and initial capitals of agents COMPUTER SKILLS/OTHER

Programming Languages: Python, C/C++, MATLAB, SAS, R, VBA, SQL, LaTeX Competition Awards: Gold Medal for 23th Hope Cup National Mathematic Tournament (11th Grade), 1st Prize for 7th National Olympic Competition of Physics Pan-Pearl River Delta (10th Grade)

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