Vice President; Quantitaive Research Development Resume Example Nfl

VICE PRESIDENT; QUANTITAIVE RESEARCH DEVELOPMENT

Summary

Implemented quantitative trading strategies for major asset classes; lead efforts in enhancing end-to-end quantitative trading process, including portfolio construction, portfolio optimization, risk modeling, and backtester; extensive experience of applied research in machine learning and statistical data analysis; deep involvement with managed futures fund and global risk parity fund.

Experience

NflJuly 2012 to CurrentVice President; Quantitaive Research Development
Inglewood , CA

Implemented quantitative trading strategies for equities, fixed income, currency, commodities, managed futures, and global risk parity;
Implemented an enhanced version of proprietary portfolio construction techniques for relative value strategies;
Implemented a two-stage optimization framework to leverage short-term signals while deciding trading plans for long-term strategies;
Developed risk modeling framework to estimate intra-asset class and inter-asset class risk;
Architected and implemented a robust portfolio optimization engine with convex programing techniques from Axioma to minimize distortion from theoretical portfolios while maximizing the mean-variance objective function;
Developed a global portfolio optimization framework that coordinates the optimization of multiple strategies from the same account to achieve better realized vol while enforcing account-level constraints.
Re-architected the strategy- and fund-level backtester to enable tracking and understanding the deviation of live performance from backtest results, and improved the simulation in backtest to be closer to real-life rebalances. Evaluating the framework with global risk parity funds which manage ~$25B in total.
All the implementation is done in Python with Pandas/Numpy/SciPy.

Crain CommunicationsSeptember 2009 to June 2012Researcher
Chicago , IL

A key problem in data-model alignment is that different model features (e.g., lexical, structural or semantic) vary widely in their importance for different model alignment. I developed an iterative supervised-learning framework in Java to (i) determine the weights assigned to each alignment strategy and use these weights to combine them for collective decision of matching; and (ii) determine the degree of information propagation from one element to to its neighbors along different relationships to maximize information utility.
For heterogeneous (e.g., relational, XML, text) Web data analysis, designed a data-format agnostic indexing mechanism and an intelligent data integration technique that identifies similar entities from data. Developed efficient and scalable algorithms of locality sensitive hashing on Hadoop to find similar entities from billions of data entries. The metrics considered include Jaccard and cosine similarity with TF-IDF weight scheme.

Educational Testing ServiceMay 2009 to August 2009Research Intern
New York , NY

Designed and implemented a scalable feature selection technique, using hypothesis testing on Map-Reduce, to capture the positive/negative associations of user behavior (represented by millions of features) with ads click activity in hundreds of categories;
Implemented L1-regularized logistic regression in Map-Reduce for predicting ads click probability based on historical user behavior in several hundred dimensional space.

Duke UniversityJanuary 2005 to January 2009Research Assistant
City , STATE

Developed a continuous forecasting system in Java that predicts future system performance, with statistical learning techniques, based on historical data in the format of high-dimensional time-series collected from computer systems;
Designed and implemented a novel multi-label classification technique for diagnosing recurrent system problems, and a diagnosis-aware clustering technique to determine the cause of a new problem type;
Developed a predictive framework in R that models system response surface with Gaussian process regression, and designed an active learning algorithm that identifies the most informative experiment to run, which can maximize the improvement in utility with minimal overhead.

Education

Duke University2009Ph.D: Computer ScienceCity, State

Dissertation: Simplifying System Management through Automated Forecasting, Diagnosis, and Configuration Tuning

Tsinghua University2004M.E: Computer ScienceCity, China

Thesis: Modeling and Control of Unmanned Aerial Vehicle

Nanjing University of P&T2001B.E: Computer ScienceCity, China

Ranked 1/150

Accomplishments

Best Paper Award, 28th International Conference on Data Engineering (top conference in the field), 2012
IBM Invention Achievement Award, IBM Corporation, 2010, 2011
National First-class Prize in Mathematical Contest in Modeling (MCM), 1999
14 patents filed.

Publications

Temporal Analytics on Big Data for Web Advertising. In Proc. of International Conference on Data Engineering (ICDE), April 2012; won best paper award
Predicting Completion Times of Batch Query Workloads Using Interaction-aware Models and Simulation. In Proc. of 14th International Conference on Extending Database Technology (EDBT), March 2011
Automatic Tuning of Database Configuration Parameters with iTuned. In Proc. of Intl. Conf. on Very Large Databases (VLDB), August 2009
Processing Forecasting Queries. In Proc. of Intl. Conf. on Very Large Databases (VLDB), September 2007

Skills

Python/pandas, Java, C++, SQL, R
machine learning, statistical data analysis
database systems and big data platforms

Resume Overview

School Attended

Duke University
Tsinghua University
Nanjing University of P&T

Job Titles Held:

Vice President; Quantitaive Research Development
Researcher
Research Intern
Research Assistant

Degrees

Ph.D : Computer Science
M.E : Computer Science
B.E : Computer Science

Vice President; Quantitaive Research Development Resume Example

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