Huang Shujiao
E-mail:huangsj_09@126.com
Mobile:（86）13426111942
08 statistics, Beijing Institute of Technology
Beijing 100081, China
OBJECTIVE
To pursue a Master degree in Statistics with an emphasis on finance
EDUCATION BACKGROUND
B.S. in Statistics, Beijing Institute of Technology
GPA: 89/100, Major GPA: 92/100
 The Premium students in Beijing Institute of Technology, 2010-2011
 The First prize scholarship, 2010-2011
 The Second prize scholarship, 2009-2010
 The Third prize scholarship, 2008-2009
Double Degree, in Economics, Peking University
GPA: 83/100
MAIN COURSE AND SCORES
 Probability theory
 Applied regression analysis
 Applied random process
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Beijing, china, 08/2008-07/2012
Beijing, china, 08/2010-07/2012
 Mathematical statistics
 Experiment design
 Applied multivariate statistics
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ACADEMIC EXPERIENCE
Team leader, mathematical modeling contest
Beijing institute of technology, 04/ 2009-09/2011
Thesis: Electric elevator running proposal in high-rise mansion via Markov queuing theory
 Introduced two variables, waiting time in Markov process and elevator’s mean open numbers in one running
process, to weight the degree of user’s satisfaction and saving energy.
 Used M/M/n Markov queuing model to compute the waiting time.
 Proposed a new formula about mean elevator running time, by hypothesizing arrival amenable poison
distribution and get off in any floor by equal probability, which can achieve by computer simply and loss less.
 Achieved the whole modeling process successfully, and then give the proposal to choose better elevator
running program.
Member, financial mathematics seminar
Beijing Institute of technology, 08/ 2010 –01/ 2011
Book: Stochastic Calculus for Finance, by Steven E. Shreve, Springer
 Discussed the binomial asset pricing model, including a multiperiod binomial model mainly about
no-Arbitrage.
 Learned American Derivative Securities about what is intrinsic value and stopping times.
SELECTED COURSE DESIGNS
Course: Mathematical Modeling
Beijing Institute of Technology, 08/2010-01/2011
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Modeled early warning hierarchical system, by analytic hierarchy process to determine impact factors
weighting and fuzzy membership function to determine impact factor score, and introduced dynamic
analysis, through hypothesizing the rate of hierarchical score is linear.
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Modeled Chinese 3G-phone-user’s prediction, by logistic regression and grey GM (1, 1).
Course: Experiment Design
Beijing Institute of Technology, 05/2011
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Analyzed the best condition on cadmium plating and indicators group to improve one product’s quality, by
ANOVA and orthogonal design.
Course: The Leadership
Beijing Institute of Technology, 05/2011
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Designed questionnaire about high school student’s quality and their independent admission result (a
different university select way).
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Analyzed the data to deduce the correlation between various qualities and the result.
EXTRACURRICULAR ACTIVITIES
2011 Peking University Statistics Summer Course and academic lecture
07/2011-08/2011
 The Theme of Phase Diagram by Jiashun Jin, Carnegie Mellon University, July 5-6
 Financial Econometrics by Lan Zhang, University of Illinois at Chicago & Per Mykland, University of
Chicago, July 13
 Learned properties of high frequent data, and the hot topic of market microstructure.
 Studied combining two time scales to work the volatility estimation.
 Sparse moving maxima models for tail dependence in multivariate financial time series by Zhengjun Zhang,
University of Wisconsin, July 21
 Learned the concepts of lag-k tail dependence existing in financial time series.
 Knew the jumps in returns and basic model.
 Penalized Bregman divergence for large-dimensional regression and classification by Chunming Zhang,
University of Wisconsin, July 21
 Optimal estimation of a Nonsmooth Functional by Mark G. Low, University of Pennsylvania, July 5
2011 International Conference on Applied Mathematics and Statistics
Beijing, China, 21-22/08/ 2011
 Statistical Model in Science and Technology by Jun Liu, Harvard University.
 Learned statistics has a wide application.
 Looked at some specific examples about application in Text Mining and A Bayesian Model of traditional
Chinese drug.
 A statistics-guided study of DNA copy number alteration for lung adenocarcinoma by Ker-Chau Li, University
of California Los Angeles.
 Studied the real problem that distinguishes EGFR-mutation cancer.
 Learned the process those professors to analyze and think.
 Biological Network Inference Using Bayesian Gaussian Graphical Models by Hongyu Zhao, Yale University.
 Studied the real problem that infers the causal relationship among protein by simultaneously measuring
multiple protein activities at the single cell level.
 Learned the Bayesian hierarchical modeling framework to infer the signaling pathway based on the
posterior distributions of parameters in the model.
 Knew the consideration about both intrinsic noise and measurement error in this model and MCMC was
used for statistical inference.
 Mixture Kalman Filter and Plug-and-Play Navigation Systems by Rong Chen, Rutgers University.
 Studied the real problem that utilizes external environmental information and plug-and-play sensor
profiles to research navigation system by working under scenarios, combination of sensors, and any
information sets.
 Learned the method adaptive filter structure and a software-defined fusion architecture with an intelligent
control system with the computational tool, Mixture Kalman Filter.
 Quantile regression estimation: efficiency and super-efficiency by Zhijie Xiao, Boston College.
 Studied two typeset of quantile-regression-based estimators: The Composite Quantile Regression and the
Quantile Averaging Estimator.
 Looked at efficiency comparison among Thessalonians estimators and others, like OLS and LAD
estimators.
 Learned an optimal quantile average estimator for the coefficients in linear regression models and its
benefit.
 Robust estimation in inverse problems via quantile coupling by Maozai Tian, Renmin University of China.
 Looked at a new topic about inverse problem.
 Studied hierarchical space model in inverse problems.
 Learned some oracle inequalities for the inverse problems by using quantile coupling technique.
LANGUAGE SKILLS AND OTHERS
 TOEFL: xxx
 GRE General: V 450, Q 800, AW 3.0
 GRE Mathematical Sub: 780/990
 Programming languages: C, C++
 Operating Systems: Linux, Microsoft
 Math & Statistical Package: Matlab, R, SPSS