Data Analyst

HONGYI LI

***-** **** **, *******, NY *****

***************@*****.*** 217-***-****

EDUCATION

University of Illinois at Urbana-Champaign Champaign, IL

MS in Financial Engineering, GPA: 4.00 (out of 4.00) Dec. 2016

FRM Part 1 & Part 2 passed, CFA level 2 candidate

Hong Kong Baptist University Hong Kong

BS in Applied and Computational Math, GPA: 3.80 (out of 4.00) Jun. 2015

First Honor, GRE (sub-math): 860 (top 10%)

University of Copenhagen Copenhagen, Denmark

International Exchange Program (Math) Jan.-Jun. 2014

ACADEMIC RESEARCH

Graduate Practicum on Trades and Subsequent Effects, UIUC Champaign, IL

Examining the Hawkes Process Feb.-May. 2016

Sponsored by Prof. Richard B. Sowers

Achieved over 30% (usu. less than 10% for order book data) when fitting non-convergent stochastic models of asymptotic price impact which robustly agrees with empirically estimated response and diffusion functions from market data

Estimated the Bare Impact Function of E-mini high frequency trading under 2 diffusive assumptions and produced stable simulations matching tail features (moments) to real market

Undergraduate Research on Image Processing, HKBU Hong Kong

Survey on Super Pixel Segmentation Jun.-Aug. 2014

Sponsored by Prof. Sunney I. Chan

First Author, “Report on Two-stage Convex Image Segmentation Method and Super Pixels”

Invented reversed over-segmentation method(SLIC) with 10% less under-segmentation error and 5% more boundary-recall than prevailing methods through combining two-stage methods with a super pixel over-segmentation method (SLIC)

EXPERIENCE

Industrial and Commercial Bank of China (USA) NA New York City, NY

Credit Analyst Intern Feb.-Mar. 2017

Helping to conduct 5 C’s credit analysis on 6 individual mortgage loan cases and cash flow analysis

Assisting in compliance checking and designing advertising materials

CITIC Group – Renaissance Era Investment Beijing, China

Quantitative Analyst Intern Summer 2016

Increased whole-market mispriced-ETF searching efficiency to 3-sec level by python and R coding

Built a handbook of arbitrage strategies and rules on structured products

Constructed a 35% annual yield (usu. less than 20% for hedge funds) stock-selection model by machine learning techniques (boosting, decision-tree and SVM) based on 400+ factors

ACTIVITIES

Member, Beta Gamma Sigma; Phi Kappa Phi; Golden Key International Honour Society Champaign, IL

SKILLS

Finance and Statistics: Numerical Analysis (PDE & Optimization), Machine Learning (Deep NN), Risk Analysis, Derivative Valuation, MC-Simulation, Time Series Analysis, Data Mining, Image Processing

Programming Languages: Python, C/C++

Other MATH/STAT Languages: R, Matlab, Maple, SAS, SQL

Languages: English, Mandarin, Cantonese

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